emin IVYSPRING Vys
INTERNATIONAL PUBLISHER
Journal of Cancer
2024; 15(20): 6594-6615. doi: 10.7150/jca.102230
Research Paper
DHX34 as a promising biomarker for prognosis, immunotherapy and chemotherapy in Pan-Cancer: A Comprehensive Analysis and Experimental Validation
Nanbin Liu1,2,3,t, Qian Wang1,2,4,t, Pengpeng Zhu1,2,3, Gaixia He1,2,3, Zeyu Li1,2,4, Ting Chen1,2,4, Jianing Yuan1,2,4, Ting La1,2, Hongwei Tian1,2,2, Zongfang Li1,2,3,4,1%
1. National and Local Joint Engineering Research Cente of Biodiagnosis and Biotherapy, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China.
2. Shaanxi Provincial Clinical Research Center for Hepatic & Splenic Diseases, Xi’an, China.
3. Department of Geriatric General Surgery, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China.
4. Tumor and Immunology center of Precision Medicine Institute, Xi’an Jiaotong University, Xi’an, China.
t Nanbin Liu, and Qian Wang contributed equally to this work and shared the first authorship.
☒ Corresponding authors: lzf2568@mail.xjtu.edu.cn (Z.L.); hongweitian@xjtu.edu.cn (H.T.).
@ The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Received: 2024.08.12; Accepted: 2024.10.05; Published: 2024.10.28
Abstract
Background: As a member of the DExD/H-box RNA helicase family, DHX34 has demonstrated a significant correlation with the development of multiple disorders. Nevertheless, a comprehensive investigation between DHX34 and pan-cancer remains unexplored.
Methods: We analyzed the value of DHX34 in pan-cancer based on some databases, such as The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and The Human Protein Atlas (HPA) by use the R language as well as some online analysis tools, including STRING, TISIDB, TISCH2. And based on our samples we performed Western blot (WB), qPCR and immunohistochemical staining (IHC) experiments.
Results: DHX34 was highly expressed in most tumors, including Liver Hepatocellular Carcinoma (LIHC), compared to corresponding normal tissues. Among cervical cancers, DHX34 mutation frequency was the highest. Intriguingly, a positive correlation was observed between DHX34 expression and Mutational Burden (TMB) across 12 tumor types, and Microsatellite Instability (MSI) across 10 tumor types. Remarkably, DHX34 exhibited a favorable diagnostic value in a multitude of tumors. High expression of DHX34 is associated with poor prognosis in tumors such as adrenocortical carcinoma (ACC), renal papillary cell carcinoma (KIRP), low-grade glioma (LGG), and LIHC. Correlation analysis indicated that DHX34 expression correlated with clinicopathological features in a variety of tumors. The Protein-Protein Interaction (PPI) network and GSCALite database suggested that DHX34 and its ten co-expression genes might promote cancer progression by regulating the cell cycle. Gene Set Enrichment Analysis (GSEA) results further showed that DHX34 was positively correlated with pathways such as cell cycle, mitosis, and gene transcription regulation. The TISIDB database showed that DHX34 expression was closely associated with immune infiltration. Based on the TISCH2 database, we found that DHX34 was expressed in a number of immune cells, with relatively high expression in monocyte macrophages in LIHC.
Conclusions: In summary, our study found that DHX34 is highly expressed in pan-cancer and has diagnostic and prognostic value. Targeting DHX34 may improve the therapeutic efficacy of immunotherapy and chemotherapy in a multitude of tumors.
Keywords: Pan-Cancer, DHX34, Prognosis, Chemotherapy, Immunotherapy
Introduction
Cancer, a significant cause of mortality in the 21st century, is experiencing a rapid increase in both its incidence and mortality rates globally [1]. Despite
the clinical effectiveness of unconventional treatments such as radiotherapy, surgery, and chemotherapy, as well as advanced technologies including gene
therapy, stem cell therapy, natural antioxidants, targeted therapy, photodynamic therapy, nanoparticles, and precision medicine, the prognosis for these patients remains unfavorable due to treatment resistance, side effects, and various other challenges [2-5]. Therefore, it is crucial to develop novel biomarkers or therapeutic targets for cancer diagnosis and treatment.
The RNA helicase family, which is conserved from bacteria to humans, plays a pivotal role in every facet of RNA metabolism, including ribosome biogenesis, transcription, RNA maturation, the processing of MicroRNAs (miRNAs) and Circular RNAs (circRNAs), mRNA export, translation, and RNA degradation [6]. Recent studies have unequivocally established the crucial role of the RNA helicase family in carcinogenic processes and immune modulation. Notably, DHX9 has been implicated in the tumorigenesis of various cancers [7]. Remarkably, the deletion of DHX9 leads to a substantial reduction in cancer cell viability in vitro and fosters a significant boost in immunogenicity in mouse models of small-cell lung cancer, thereby greatly enhancing the responsiveness to immunotherapy [8]. DHX15, another member of this family, is involved in the tumorigenesis of LIHC, gastric cancer, and colorectal cancer [9-11]. Furthermore, DHX15 exhibits potential immune-regulatory effects by affecting the functions of dendritic cells, B cells, and NK cells [12-14]. Additionally, DHX33 plays a pivotal role in the growth and proliferation of B-cells [15], and its overexpression in LIHC suggests its potential as a predictive biomarker for this cancer [16]. Lastly, DHX37 exhibits a complex interaction with carcinogenesis, further underscoring the diverse and intricate roles of the RNA helicase family in cancer biology [17-19].
DHX34, a member of the DExD/H-box RNA helicase family, exhibits a profound connection with the onset of numerous diseases. For instance, its frequently altered splicing pattern has been observed in acute myeloid leukemia cases [20]. Furthermore, the occurrence of preeclampsia is strongly linked to the methylation level of the DHX34 gene [21]. Additionally, studies have reported that monoallelic variants of DHX34 are associated with neurodevelopmental disorders [22]. Notably, DHX34 serves as a reliable predictor of LIHC prognosis within a prognostic risk score model [23]. Despite these insights, however, there is no comprehensive study on the relationship between DHX34 and pan-cancer.
Therefore, the objective of our research was to investigate the expression levels of DHX34 and their association with diagnosis and prognosis across
various cancer types. To achieve this, we utilized databases and platforms such as The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Human Protein Atlas (HPA) database. Additionally, we conducted a comprehensive analysis to examine the mutational status, Protein-Protein Interaction (PPI) network, co-expression network, and biological functions of DHX34. Furthermore, we detected the relationships between DHX34 expression and various tumor characteristics, including TMB, MSI, Tumor Immune Microenvironment (TIME), Immune Checkpoint Inhibitors (ICI) response, and drug resistance. Our findings revealed that DHX34 exerts a pro-cancerous effect on cancer cells, indicating its potential as a diagnostic and prognostic biomarker in pan-cancer.
Materials and methods
Data acquisition and processing
From the TCGA database (https://portal.gdc .cancer.gov/), we retrieved RNA sequencing data and clinical follow-up information for patients with 33 distinct cancer types. This data allowed us to further explore the differential expression of DHX34 across various cancer subtypes. Additionally, we sourced GSE42568, GSE26566, GSE37182, GSE39791, GSE19804, and GSE71016 from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) to complement our analysis of the expression of DHX34 in different cancer types. The “ggplot2” package in R software was employed for conducting comprehensive expression analysis and visualization.
The HPA database (https://www.proteinatlas .org/) provided us with information on the expression of DHX34 RNA and protein in human beings, and the DHX34 RNA expression in single-cell tissues and cancer cell lines. Also, the HPA database provides the subcellular localization of DHX34 using indirect immunofluorescence microscopy as well as visual representations of protein expression in human tissues after Immunohistochemistry (IHC) labeling [24].
Patients and tissue samples
A total of 50 paraffin-embedded samples from LIHC cases underwent IHC staining. Furthermore, five pairs of frozen colonic carcinoma, LIHC, Lung Adenocarcinoma (LUAD), and Stomach Adenocarcinoma (STAD) tissues and their corresponding non-tumor tissues were utilized for Western blotting (WB) analysis. Comprehensive clinical data was collected for all patients. Following surgical intervention, all patients underwent regular follow-up procedures, including imaging scans and laboratory tests conducted every 3 to 6 months.
All tissues were obtained from the sample bank of the National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy at the Second Affiliated Hospital of Xi’an Jiaotong University. Before the commencement of this study, all patients had signed informed consent forms, and the research was approved by the ethics committee of the Second Affiliated Hospital of Xi’an Jiaotong University.
Genomic alterations of DHX34 in pan-cancer
The characteristics of genomic alterations in DHX34 across the pan-cancer were analyzed utilizing the cBioPortal database (https://www.cbioportal.org) [25]. This comprehensive analysis focused on investigating the genetic alteration rate, mutation types, and specific mutated site information of DHX34 in pan-cancer.
Correlation of DHX34 expression with MSI and TMB in pan-cancer
The TMB and MSI scores were sourced from the TCGA database. Subsequently, Spearman’s correlation analysis was conducted to evaluate the associations between the expression levels of DHX34 and both TMB and MSI.
The diagnostic and prognostic value of DHX34 in pan-cancer
To assess the diagnostic potential of DHX34 in 33 different cancer types, Receiver Operating Characteristic (ROC) curves were employed. The analysis and visualization of these data were facilitated by the “pROC” and “ggplot2” packages in R software. To analyze the relationship between DHX34 expression and the prognosis of these cancers, we focused on three key metrics: Overall Survival (OS), Disease-Specific Survival (DSS), and Progression-Free Interval (PFI). We assessed the correlation of DHX34 expression with OS, DSS, and PFI using univariate Cox regression analysis using the survival package and visualized using the “ggplot2” package. Subsequently, Patients were classified into high and low DHX34 expression groups based on the median DHX34 expression in different cancers. We performed survival analyses using the “survival” packages in R software to detect the link between DHX34 and survival prognosis.
The correlation between DHX34 expression and clinicopathological features in pan-cancer
To elucidate potential correlations between DHX34 expression and various clinicopathologic indicators across pan-cancer, we employed the Wilcoxon or Kruskal-Wallis test. These indicators encompassed pathologic T stage, pathologic stage, pathologic M stage, WHO grade, IDH status, AFP
levels, histologic grade, and radiation therapy. Furthermore, to gain a deeper understanding of the relationship between DHX34 and specific clinical parameters in LIHC, we utilized the chi-square test and logistic regression analysis.
The related genes and PPI Network analysis of DHX34 in pan-cancer
We respectively analyzed the 20 genes with the highest correlation to DHX34 across the 8 tumors in which DHX34 has a prognostic value and visualized them using the “ggplot2” package. Additionally, We analyzed the PPI network of DHX34 using the STRING database (https://cn.string-db.org/) [26]. The top 10 genes with the highest correlation in the co-expression network in LIHC and the top 10 genes with the highest interaction score in the PPI network were selected. we utilized the Tumor Immune Estimation Resource 2.0 (TIMER2) (http://timer .cistrome.org/) [27] to examine the correlations between DHX34 and its related genes across pan-cancer. We performed correlation analysis of the above genes in LHC and visualized them using the “circlize” package.
Prognostic value and functional analysis of DHX34-related genes
We analyzed the prognostic value of the above 20 genes in LIHC using “survival” packages. In addition, we analyzed the signaling pathways regulated by DHX34 and its related genes with prognostic value in LIHC using GSCALite (https://guolab.wchscu.cn/GSCA) [28].
The Differentially Expressed Gene and Gene Set Enrichment Analysis (GSEA) analysis of DHX34 in pan-cancer
We divided the patients into high and low expression groups based on the median expression level of DHX34 and analyzed the differential genes using the “DESeq2” package, visualizing them in the “ggplot2” package. The genes displayed are: | log2(FC) | > 1.5 and a p < 0.05. To ascertain the biological pathway variations between high- and low-DHX34 groups, the “clusterProfiler” package performed the GSEA analysis. The False Discovery Rate (FDR) < 0.25 and an adjusted p-value < 0.05 were regarded as remarkable altered pathways.
The correlation between DHX34 expression and the TIME in pan-cancer
The relationship between DHX34 expression and immune system-related modulators in various malignancies was evaluated using the TISIDB online database (http://cis.hku.hk/TISIDB/index.php) [29]. These modulators included Tumor-Infiltrating
Lymphocytes (TIL), immune stimulators, immune inhibitors, Major Histocompatibility Complex (MHC), chemokine, and receptors.
The single-cell expression analysis of DHX34 in LIHC
To determine the possible function of DHX34 at the single-cell level, the relationship between DHX34 expression and immune cells was examined using the Tumor Immune Single-cell Hub 2 (TISCH2) database (http://tisch.comp-genomics.org/) [30].
The immunotherapy and chemotherapy response analysis of DHX34 in pan-cancer
From the TCGA dataset, RNA-sequencing expression (level 3) profiles and related clinical data for pan-cancer were retrieved. Subsequently, the Tumor Immune Dysfunction and Exclusion (TIDE) method was employed to predict the potential response to ICI treatment [31]. This analysis was facilitated by “ggplot2” and “ggpubr” packages in R software. We examined the relationship between DHX34 expression and drug sensitivity for pan-cancer using the GSCALite [28].
Correlation analysis of DHX34 with ferroptosis and m6A-related genes in LIHC
Ferroptosis refers to the impaired metabolism of intracellular lipid oxides and the production of toxic lipids to induce cell death, m6A is RNA methylation, a methylation on the 6th N atoms on adenine (A) in RNA that affects mRNA stability, translation efficiency, variable splicing, and localization. We analyzed the correlation of DHX34 with Ferroptosis and m6A-related genes in LIHC. Ferroptosis-related genes were derived from Ze-Xian Liu et al. Systematic analysis of the abnormalities and functions of iron death in cancer [32]. The m6A-associated genes were derived from a study by Juan Xu et al. on the molecular characterization and clinical significance of m6A regulators across 33 cancer types [33].
RNA preparation and Quantitative Real-Time PCR (qRT-PCR)
Total RNA was extracted from tissues using the TRIZOL reagent (Invitrogen) by the manufacturer’s instructions. Using a PrimeScript RT Reagent Kit (Takara), the purified RNA was converted to cDNA. qRT-PCR tests were conducted using the Takara SYBR Premix Ex Taq II Kit. The results were adjusted to the expression of GAPDH. The primer sequences utilized in this investigation were as follows:
GAPDH-forward: TGTGGGCATCAATGGATT TGG
GAPDH-reverse: ACACCATGTATTCCGGGTC AAT
DHX34-forward: TGAGAGCCTCAGTCAGTA TGG
DHX34-reverse: TGTCAGGAATACAATCTTGG TGG
Western Blotting
Tissues were lysed in RIPA buffer (Beyotime Biotechnology, China) containing a protease inhibitor cocktail. Following the use of a BCA assay kit (Beyotime, Jiangsu, China) to measure the concentration of protein, equal amounts of protein were separated using 10% Sodium Dodecyl Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE) and then deposited onto a Polyvinylidene Difluoride (PVDF) membrane. Specific primary antibodies were used to incubate the proteins, including anti-DHX34 (1:1000, Affinity) and anti-GAPDH (1:2000, CST). Following three rounds of washing, the membrane was left to be incubated for two hours at room temperature with secondary antibodies that matched its species. Lastly, protein visualization was performed using the Enhanced Chemiluminescence (ECL) Western Blot Detection Kit (Millipore). The protein loading control was GAPDH.
Immunohistochemistry
The tumor and normal tissues fixed in paraffin were sectioned at a thickness of 4 um.
After deparaffinizing and hydrating, these sections were then subjected to a heat treatment at 95 ℃ within a citric acid buffer adjusted to a pH of 6.0, aiming to extract the antigens. Before incubation with the primary antibodies, the slices were treated with 3% H2O2 and subsequently blocked with 5% goat normal serum. The primary antibodies against DHX34 (1:200, Affinity) were applied, followed by the appropriate secondary antibody. Next, the sections were visualized with Diaminobenzidine (DAB) and finally counterstained with Hematoxylin. We performed a semi-quantitative analysis using Image-Pro Plus 6.0 software by capturing five random microscopic images of each section. The analysis encompassed the area and density of the stained region, and Integrated Optical Density (IOD). The average of five IOD values per section served as a reliable indicator to reflect DHX34 expression levels.
Statistical analysis
The aforementioned packages in R version 4.0.3 and Graphpad Prism 8.0 were used to analyze and visualize the data. The Welch one-way ANOVA was used to evaluate comparisons between several groups. The Student t-test was employed to evaluate comparisons between the two groups. Each experiment was performed thrice and data were shown as mean ± Standard Deviation (SD). Any value
of p<0.05 was considered to be statistically significant.
Results
The expression of DHX34 in human organs and tissues
The mRNA of DHX34 was widely expressed in various human organs and tissues (Fig. 1A). Analysis of the consensus dataset revealed that DHX34 mRNA is primarily expressed in the testis, spleen, bone marrow, ovary, liver, cerebellum, pituitary gland, cervix, lung, and thyroid gland (Fig. 1B). Furthermore, data acquired from the HPA database indicated that DHX34 is predominantly expressed in bone marrow, testis, spleen, skin, appendix, salivary gland, ovary, pancreas, lymph node, and fallopian tube (Fig. 1C). The detailed expression patterns of DHX34 in various single-cell tissues, including adipose tissue, bone marrow, brain, breast, colon, liver, lung and stomach were shown in Fig. 1D-K. Moreover, we obtained DHX34 subcellular localization from the HPA database. DHX34 subcellular localization was obtained by immunofluorescence localization of the nuclei, microtubules, and ER in A-431, U-2OS, and U-251MG cells, the green color represents the location and intensity of DHX34 expression, which shows that DHX34 was primarily located in the nucleoplasm (Fig. 1L). These three cells are indispensable in tumor research and are widely used in cell biology and molecular biology studies. Based on the importance and representativeness of these three cells, we chose them to study the subcellular localization of DHX34. In addition, these three cells are relatively easy to culture in experimental manipulation, which can ensure the accuracy and reproducibility of experimental results.
DHX34 is highly expressed in most tumors
Upon analyzing pan-cancer data from TCGA, we discovered an upregulation of DHX34 expression in both unpaired and paired tumor tissues across 15 cancer types, including Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cholangiocarcinoma (CHOL), Colon Adeno- carcinoma (COAD), Esophageal Carcinoma (ESCA), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Rrenal Papillary Cell Carcinoma (KIRP), Liver Hepatocellular Carcinoma(LIHC), Lung Adenocarci- noma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Prostate Adenocarcinoma (PRAD), Rectum Adenocarcinoma (READ), Stomach Adenocarcinoma (STAD) and Uterine Corpus Endometrial Carcinoma (UCEC) (Fig. 2A, 2B). Furthermore, analysis of the
HPA dataset revealed that DHX34 mRNA is mainly expressed in adrenocortical cancer, cervical cancer, and liver cancer cell lines (Fig. 2C). Consistent with these findings, our evaluation of six GEO datasets indicated overexpression of DHX34 in BRCA, CHOL, COAD, LIHC, LUAD, and PRAD (Fig. 2D-I). To further validate the protein expression pattern of DHX34, we examined IHC-staining images from the HPA database, which highlighted the elevated expression of DHX34 in BRCA, COAD, LIHC, LUAD, and PRAD (Fig. 2J).
The gene mutation of DHX34 in pan-cancer
To assess the mutation of DHX34 in pan-cancer, we conducted a comprehensive study using the cBioPortal database and found that DHX34 was altered in 5% (128/2565) of pan-cancer patients (Fig. 3A). Furthermore, our analysis of the mutation frequency of the DHX34 gene across various tumor types showed that cervical cancer (20%), esophagogastric cancer (15.34%), and bladder cancer (13.04%) had the highest alteration frequency, ranking among the top three. Notably, amplification was identified as the most prevalent type of DHX34 gene mutation (Fig. 3B). Analysis of the mutation sites of DHX34 in pan-cancer, revealed a total of 18 mutation sites, spanning the region between 0 and 1143 amino acids (Fig. 3C). Additionally, a positive correlation was observed between DHX34 expression and TMB across 12 tumor types, and MSI across10 tumor types (Fig. 3D, 3E), indicating that DHX34 significantly impacts both TMB and MSI.
The diagnostic value of DHX34 in pan-cancer
As shown in Fig. 4, DHX34 has a good diagnostic value in a variety of cancers, including BLCA (AUC = 0.802, 95% CI: 0.697-0.907), BRCA (AUC = 0.776, 95% CI: 0.733-0.820), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) (AUC = 0.814, 95% CI: 0.519-1.000), COAD (AUC = 0.954, 95% CI: 0.936-0.971), Esophageal Carcinoma (ESCA) (AUC = 0.957, 95% CI: 0.906-1.000), HNSC (AUC = 0.829, 95% CI: 0.775-0.882), Kidney Chromophobe (KICH) (AUC = 0.811, 95% CI: 0.710-0.912), KIRC (AUC = 0.798, 95% CI: 0.754-0.842), KIRP (AUC = 0.717, 95% CI: 0.644-0.791), LIHC (AUC = 0.970, 95% CI: 0.954-0.986), LUAD (AUC = 0.844, 95% CI: 0.811-0.877), LUSC (AUC = 0.936, 95% CI: 0.914-0.959), Oral Squamous Cell Carcinoma (OSCC) (AUC = 0.799, 95% CI: 0.723-0.875), READ (AUC = 0.985, 95% CI: 0.966-1.000), Sarcoma (SARC) (AUC = 0.930, 95% CI: 0.805-1.000), STAD (AUC = 0.947, 95% CI: 0.912-0.982), UCEC (AUC = 0.747, 95% CI: 0.679-0.816).
Journal of Cancer 2024, Vol. 15
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Brain
Proximal digestive tract
RNA expression (nTPM)!
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The subcellular localization of DHX34, as depicted by immunofluorescence visualization in HPA database.
Figure 1. The expression of DHX34 in human organs and tissues. (A) Overview of DHX34 mRNA and protein expression across human organs and tissues. (B, C) Summarized DHX34 mRNA expression in various organs and tissues, based on the consensus and HPA dataset. (D-K) Expression analysis of DHX34 mRNA in distinct single cell tissues. (L)
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READ
SARC
SKCM
STAD
TGCT
THCA THYM
UCEC
UCS
UVM
2.5
Normal
Tumor
Normal
Tumor
B
F
COAD
G
LIHC
00
1ª
2
=
GSE37182
GSE39791
**
=
9.6
The expression of DHX34 Log2 (TPM+1)
=
É
TE
E
E
6
**
7.4
E
1
The expression of DHX34
JE
9.4
The expression of DHX34
.
Normal Tumor
9.2
7.2
0
9.0
7.0
S
8.8
8.6
6.8
BLCA
BRCA
ESC
CHOL
GOAD
ESCA
HNSC
KICH
KIRC
KIRP
LIHC
LUAD
LUSC
PAAD
PCPG
PRAD
READ
SARC
STAD
THCA
THYM
UCEC
Normal
Tumor
Normal
Tumor
0
H
LUAD
GSE19804
I
PRAD
GSE71016
4.0
nTPM
6.4
25
The expression of DHX34
20-
6.2
The expression of DHX34
15
6.0
3.5
10-
5.8
5
0
5.6
3.0
Adrenocortical cancer
Cervical cancer
Liver cancer
Gastric cancer
Lymphoma
Leukemia
Lung cancer
Uterine cancer
Myeloma
Bladder cancer
Ovarian cancer
Rhabdoid
Colorectal cancer
Bone cancer
Pancreatic cancer
Sarcoma
Breast cancer
Thyroid cancer
Bile duct cancer
Skin cancer
Esophageal cancer
Brain cancer
Kidney cancer
Neuroblastoma
Head and Neck cancer
Uncategorized
Gallbladder cancer
Prostate cancer
Non-cancerous
Testis cancer
5.4
5.2
2.5
Normal
Tumor
Normal
Tumor
J
BRCA
COAD
LIHC
LUAD
PRAD
The prognostic value of DHX34 in pan-cancer
To investigate the prognostic value of DHX34, we performed univariate Cox regression analysis to evaluate DHX34 expression with OS, DSS, and PFI in pan-cancer. Forest map showing the prognostic value of DHX34 in a variety of cancer types (Fig. 5A-C). To further determine the prognostic value of DHX34, survival analysis was performed. Our findings revealed that high expression of DHX34 was significantly correlated with shorter OS in Adrenocortical Carcinoma (ACC) (HR = 8.05, 95% CI: 2.99-21.64, p < 0.001), KIRP (HR = 3.18, 95% CI: 1.64-6.19, p < 0.001), Low-Grade Glioma (LGG) (HR = 2.31, 95% CI: 1.62-3.30, p < 0.001), LIHC (HR = 1.91, 95% CI: 1.35-2.72, p < 0.001), Malignant Mesothelioma
(MESO) (HR = 2.76, 95% CI:1.66-4.59, p < 0.001), SARC (HR = 1.91, 95% CI: 1.27-2.86, p = 0.002) (Fig. 5D-I). Additionally, a significant association was observed between high DHX34 expression and shorter DSS in ACC (HR = 7.67, 95% CI: 2.83-20.81, p < 0.001), KIRP (HR = 5.15, 95% CI: 1.96-13.57, p < 0.001), LGG (HR = 2.38, 95% CI: 1.63-3.48, p < 0.001), LIHC (HR = 1.78, 95% CI: 1.14-2.77, p = 0.011), MESO (HR = 2.80, 95% CI: 1.44-5.45, p = 0.002), SARC (HR = 1.72, 95% CI: 1.10-2.67, p = 0.016) (Fig. 5J-O). Furthermore, high DHX34 expression was associated with shorter PFI in ACC (HR = 4.42, 95% CI: 2.21-8.86, p < 0.001), KIRP (HR = 2.00, 95% CI: 1.16-3.44, p = 0.012), LGG (HR = 1.97, 95% CI: 1.49-2.62, p < 0.001), LIHC (HR = 1.53, 95% CI: 1.14-2.04, p = 0.004), and Skin Cutaneous
Melanoma (SKCM) (HR = 1.29, 95% CI: 1.03-1.61, p = 0.028) (Fig. 5P-T).
The correlation between DHX34 expression and clinicopathological characteristics
In a subgroup analysis, we observed that high DHX34 expression correlated with advanced pathologic T stage and pathologic stage in ACC (Fig. 6A, 6B). Similarly, it was correlated with the pathologic M stage in KIRP (Fig. 6C). Furthermore, high DHX34 expression was associated with higher WHO grade and IDH status (WT) in LGG (Fig. 6D, 6E). In LIHC, high AFP levels, pathologic T stage, histologic grade, and pathologic stage were all found to be correlated with high DHX34 expression (Fig. 6F-I). Lastly, patients who underwent radiation therapy displayed a correlation with high DHX34 expression in SKCM (Fig. 6J).
We employed the logistic regression method to analyze the link between DHX34 expression levels and the clinicopathologic characteristics of LIHC. The findings indicated a strong association between DHX34 expression and gender (P = 0.036), Age (p =
0.044), AFP (ng/ml) (p < 0.001), prothrombin time (p = 0.031), and histologic grade (p < 0.001) (Table 1).
| Characteristics | Total (N) | OR (95% CI) | P value |
|---|---|---|---|
| Gender (Male vs. Female) | 374 | 0.627 (0.405 - 0.971) | 0.036 |
| Age (> 60 vs. <= 60) | 373 | 0.657 (0.436 - 0.988) | 0.044 |
| BMI (> 25 vs. <= 25) | 337 | 0.744 (0.485 - 1.143) | 0.177 |
| Pathologic T stage (T3&T4 vs. T1&T2) | 371 | 1.268 (0.791 - 2.031) | 0.324 |
| Pathologic N stage (N1 vs. N0) | 258 | 2.644 (0.271 - 25.763) | 0.402 |
| Pathologic M stage (M1 vs. M0) | 272 | 0.901 (0.125 - 6.488) | 0.917 |
| Pathologic stage (Stage III & Stage IV vs. | 350 | 1.391 (0.858 - 2.254) | 0.180 |
| Stage I & Stage II) | |||
| Tumor status (With tumor vs. Tumor free) | 355 | 1.456 (0.954 - 2.220) | 0.081 |
| Residual tumor (R1&R2 vs. R0) | 345 | 1.019 (0.394 - 2.631) | 0.970 |
| AFP (ng/ml) (> 400 vs. <= 400) | 280 | 5.773 (2.967 - 11.233) | < 0.001 |
| Albumin (g/dl) (>= 3.5 vs. < 3.5) | 300 | 0.998 (0.582 - 1.711) | 0.993 |
| Prothrombin time (> 4 vs. <= 4) | 297 | 0.572 (0.345 - 0.950) | 0.031 |
| Child-Pugh grade (B&C vs. A) | 241 | 0.809 (0.332 - 1.971) | 0.641 |
| Fibrosis ishak score (5&6 vs. 0&1/2&3/4) | 215 | 0.920 (0.526 - 1.606) | 0.768 |
| Histologic grade (G3&G4 vs. G1&G2) | 369 | 3.092 (1.982 - 4.822) | < 0.001 |
| Vascular invasion (Yes vs. No) | 318 | 1.539 (0.967 - 2.450) | 0.069 |
| Adjacent hepatic tissue inflammation (Mild & Severe vs. None) | 237 | 1.339 (0.802 - 2.237) | 0.265 |
A
D
TMB
# Samples per P …
THYM
ACC
LGG
Mutation spectrum
MESO
READ
LUAD
DHX34
5%
SARC
CHOL
KICH
STAD
Genetic Alteration
Mutation (unknown significance)
Amplification
Deep Deletion
No alterations
DLBC
HNSC
Correlation
BLCA
0.1
TOCT
0.2
LIFIC
0.3
PAAD
0,4
LUSC
SKCM
-legl((p-value)
B
LICEC
12
OV
20%
4
GALIM
4
Mutation
Amplification
Deep Deletion
Multiple Alterations
PRAD
LAML
KIRC
15%
CESC
Alteration Frequency
DIVM
ARCA
ESCA
PCPG
10%-
KIRP
UCS
THƯA
COAD
-0.25
0.00
Corelatian(TM8)
0.25
0.50
5%
MSI
Mutation data +
E
+
*
*
+
+
*
+
+
*
+
+
+
+
+
+
*
+
+
+
+
+
LUISC
CNA data +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
KICHI
LUAD
Cervical Cancer
Esophagogastric Cancer
Bladder Cancer
Pancreatic Cancer
Non-Small Cell Lung Cancer
Head and Neck Cancer
Lung Cancer
Melanoma
Soft Tissue Sarcoma
Endometrial Cancer
Hepatobiliary Cancer
Colorectal Cancer
Breast Cancer
Mature B-cell lymphoma
Ovarian Cancer
Glioma
Prostate Cancer
Renal Cell Carcinoma
Mature B-Cell Neoplasms
Thyroid Cancer
Medulloblastoma
Embryonal Tumor
Acute myeloid leukemia
Bone Cancer
Essential Thrombocythemia
Myelodysplastic/Myeloproliferative Neoplasms
Uterine Endometrioid Carcinoma
BLCA
ACC
ESCA
MESO
GRM
STAD
CESC
PRAD
KIRC
-loglo(p-value)
LINIC
20
LIVM
15
SARC
10
CHOL
4
9
C
LAML
Ov
Correlation
Missense
THYM
0.1
LOG
0.2
Truncating
UCEC
0.3
BRCA
0.4
5
Inframe
HINSC
THICA
Splice
TGCT
Fusion
P76006+12
SKCM
KIRP
PCPG
PAAD
0
COAD
DEAD
Helicase_C
HA2
DE HTP bind
TICS
DLDC
0
200
400
600
800
1000
1143ma
READ
-0.2
0.0
Correlation(Mish
0.2
0.4
BLCA
BRCA
CESC
COAD
ESCA
1.0
1.0
1.0
1.0
1.0
0.8
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
DHX34
0.2
DHX34
0.2
DHX34
0.2
DHX34
0.2
DHX34
AUC: 0.802
AUC: 0.776
AUC: 0.814
AUC: 0.954
AUC: 0.957
0.0
CI: 0.697-0.907
0.0
CI: 0.733-0.820
0.0
CI: 0.519-1.000
0.0
CI: 0.936-0.971
0.0
CI: 0.906-1.000
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
HNSC
KICH
KIRC
KIRP
LIHC
1.0
1.0
1.0
1.0
1.0
0.8
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
DHX34
0.2
DHX34
0.2
DHX34
0.2
DHX34
0.2
DHX34
AUC: 0.829
AUC: 0.811
AUC: 0.798
AUC: 0.717
AUC: 0.970
0.0
CI: 0.775-0.882
0.0
CI: 0.710-0.912
0.0
CI: 0.754-0.842
0.0
CI: 0.644-0.791
0.0
CI: 0.954-0.986
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1-Specificity (FPR)
1-Specificity (FPR)
1.0
1-Specificity (FPR)
1.0
1-Specificity (FPR)
1-Specificity (FPR)
1.0
LUAD
LUSC
OSCC
READ
SARC
1.0
1.0
1.0
1.0
1.0
0.8
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
DHX34
0.2
DHX34
0.2
DHX34
0.2
DHX34
0.2
DHX34
AUC: 0.844
AUC: 0.936
AUC: 0.799
AUC: 0.985
AUC: 0.930
0.0
CI: 0.811-0.877
0.0
CI: 0.914-0.959
0.0
CI: 0.723-0.875
0.0
CI: 0.966-1.000
0.6
0.6
0.0
Cl: 0.805-1.000
0.0
0.2
0.4
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1-Specificity (FPR)
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1.0
STAD
UCEC
1.0
1.0
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.4
0.4
0.2
DHX34
0.2
DHX34
AUC: 0.947
AUC: 0.747
0.0
CI: 0.912-0.982
0.0
CI: 0.679-0.816
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1-Specificity (FPR)
1-Specificity (FPR)
1.0
Identification of DHX34-related genes and PPI network
We analyzed the co-expressed genes of DHX34 in eight cancer types using RNA sequencing data obtained from the TCGA database and visualized the 20 most highly correlated genes (Fig. 7A-H). Using the STRING tool, we identified the top 20 proteins that interact with DHX34 (Fig. 7I). Following this, we utilized TIMER2 to investigate the co-expression patterns of the 10 most highly correlated genes in LIHC and the top 10 genes with the highest interaction score. The results indicated that most of these genes displayed a positive correlation with DHX34 in pan-cancer (Fig. 7J-K). Finally, we analyzed the correlations between the above genes, and the results showed that all of these genes were significantly correlated with each other (Fig. 7L-M).
DHX34-related genes of prognostic value and functional pathway
We analyzed the prognostic value of the above 20 genes and showed that 14 genes had prognostic value in LIHC, CCDC97 (HR=1.79, CI:1.26-2.54, p=0.001), CHTOP (HR=1.77, CI:1.25-2.52, p=0.001), DAZAP1 (HR=1.65, CI:1.17-2.35, p=0.005), EIF4A3 (HR=1.57, CI:1.11-2.23, p=0.011), NUP62 (HR=1.55, CI:1.09-2.19, p=0.014), PRPF19 (HR=1.65, CI:1.17-2.34, p=0.005), PYGO2 (HR=1.59, CI:1.12-2.25, p=0.009), SCAF1 (HR=1.49, CI:1.05-2.11, p=0.026), SMG8 (HR=1.51, CI:1.06-2.13, p=0.021), SMG9 (HR=1.63, CI:1.14-2.31, p=0.007), SNRNP70 (HR=1.53, CI:1.08-2.16, p=0.017), SRRT (HR=1.51, CI:1.07-2.14, p=0.019), STRN4 (HR=1.60, CI:1.13-2.27, p=0.008), UPF2 (HR=1.55, CI:1.09-2.20, p=0.014)(Fig. 8A-N). We leveraged the GSCALite to examine the potential roles of DHX34 and these 14 genes in LIHC, which suggests
that these genes may promote the progression of LIHC by modulating the apoptosis and cell cycle (Fig. 8O).
The DEGs and GSEA enrichment analysis of DHX34 in pan-cancer
By differential gene analysis, we found a large number of differential genes in DHX34 in all eight tumors, ACC (433 up-regulated genes and 623 down-regulated genes), KIRP (570 up-regulated genes and 232 down-regulated genes), LGG (506 up-regulated genes and 291 down-regulated genes), LIHC (1037 up-regulated genes and 291 down-regulated genes), MESO (123 up-regulated genes and 102 down-regulated genes), PAAD (94 up-regulated genes and 146 down-regulated genes), SARC (519 up-regulated genes and 506 down-regulated genes), SKCM (114 up-regulated genes and 620 down-regulated genes) (Fig. 9A-H).
To determine the DHX34-associated KEGG pathways, we conducted a GSEA. Our results revealed that in ACC, DHX34 was positively associated with the cell cycle (NES = 3.624, P.adj < 0.001), and negatively associated with immunoglobulin complex (NES = - 4.287, P.adj < 0.001) (Fig. 9I). In KIRP, DHX34 showed positive associations with tissue development (NES = 2.192, P.adj = 0.014) and negative associations with small molecule metabolic process (NES = - 3.435, P.adj < 0.001) (Fig. 9J). In LGG, DHX34 positively correlated with the pattern specification process (NES = 3.914, P.adj < 0.001) and negatively with a synapse (NES = -5.108, P.adj < 0.001) (Fig. 9K). For LIHC, DHX34 displayed positive associations with the pattern specification process (NES = 2.483, P.adj < 0.001) and negative associations with the organic acid metabolic process (NES = - 4.032, P.adj < 0.001) (Fig. 9L). In MESO, DHX34 positively correlated with nuclear
A
DHX34 - Overall Survival
B
DHX34 - Disease Specific Survival
C
DHX34 - Progress Free Interval
| Group | TotalNI | HRT95% CIT P value | Group | TotalNy | HR 95% CI) | P value | Group | Total(N) | HR/95% CI) | P value |
|---|---|---|---|---|---|---|---|---|---|---|
| 8.048 (2.993 - 21.642) 3.60-05 | ACC | 7.670 (2.826 - 20.815) | 6.340-05 | ACC | 4,421 (2.207 8.857) | 2.760-05 | ||||
| BLČA | 411 | 0.1961 0.825 (0.615 - 1.105) | BLCA | 397 | 0.805 (0.565 - 1.146) | 4 0.2288 | BLCA | 412 | 0.763 /0 588 - 1 008) | 0.0713 |
| BRCA | 1086 | M 0.3109 0.847 (0.514 - 1.168) | 1065 | 0.754 (0.488 - 1.154) | 0.2026 | BBP | 1058 | 0.847 (0.611 - 1.174) | 3400 | |
| CESC | 306 | 1.037 (0.553 1.649) 0.8764 | CESC | 1.230 (0.724 - 2.089) | 0.4441 | PER | 1060 | 1.393 (0,875 - 2.218) | 0.1824 | |
| CHOL | 35 | 1.661 (0.840 - 4.309) 0.2969 | CHOL | M | 1.389 /0.514 3.755) | 05173 0.5175 | Che | 200 | ||
| COAD | 477 | 1.244 (0.844 - 1.835) 0.2703 | CA | 1 303 (0.795 - 2. 134) | 0.2938 | 2085 COAD | 32 | 19 10432-2.5421 | 0,6217 | |
| DLBC DE | 48 | 0.769 (0.178 - 3.331) 0.7258 | DIE DLUG | 1.27 0 174 - 9 3896 | DLUC | 200 5 | 1.00 10,0 1.5451 1.674 (0.487 - 5.752) | 0.4136 | ||
| 163 19 | 1.387 (0.546 = 2.275) 0.1950 1548 | ESCA | 3 162 | 1 2 (-) 1.198 (0.672 - 2.137) | ORA 0.5404 | ESCA | 163 | 0.992 (0.574 - 1.286) | 0.6107 | |
| GEM | 1.120 (0.798 - 1.575) - 1.2711 | GBM | 155 | 1.137 (0.792 - 1.633) | 0.4881 | GBM | 168 | (0,608 | 0.3544 | |
| WISC MIKE | 503 Ad | 0.973 (0.745 0.8415 0.8168 | HNSC | ATB | 1.040 (0.736 - 1.470) | 0.8215 | HNSC | 503 | 0.852 1.185) 1.029 (0.776 - 1.265) | 0.8422 |
| Kan | 0.856 (0.230 - 3.190) 0.2030 | KICH | 64 | 0.794 (0.177 - 3.550) | 0.7625 | KICH | 64 | 0.894 (0.273 - 2.929) | 0,8526 | |
| 541 - | 1.172 (0,871 - 1,577) STORE | KIRC | 530 | 1.196 (0.821 - 1.742) | 0.3507 | KIRC | 539 | 0.980 (0.719 - 1.337) | 0.9005 | |
| KIRE IN | 130 | 11020779-1811 det | KIRP | 285 | 5.155 (1.958 - 13.571) | 0.0009 | KIRP | 289 | 2.000 (1,163 - 3.441) | 0.0123 |
| LAML | ||||||||||
| 530 | 2345 62 303 0.4414 2.312 (1.618 - 3.303) - | LGG | 522 | 2.385 (1.633 - 3.483) | 6.828-06 0.0113 | LGG | 530 | 1.975 (1,489 - 2.619) | 2.316-06 | |
| LGG | 373 | 4-148-06 - | LIHC | 365 | 1.776 (1.139 - 2.770) | LIHC | 373 | 1.525 (1.140 - 2.040) | 0.0045 | |
| LUAD | 530 | 1.915 (1.350 - 2.717) COME 0,5042 | LUAD | 495 | 1.210 (0.841 - 1,740) | 0.3048 | LUAD | 5.30 | 0.999 (0.768 1.300) 10 8391 499 | 0.9934 |
| LUSC | 90 | 1.105 (0,828 - 1.469) (0.124 - 1.248) 0.7147 | LUSC | 444 665 | 1.020 (0.668 - 1.556) | 0.9288 0 0024 | LUSC | 497 | 1130 | 0.4352 |
| MESO | 00 | 0951 2.799 (1.651 -4.587) 0.98-05 | MESO ON | 2.805 (1.442 - 5.454) | 1434 | MESO HOW | 1.623 (0.952 2.767) 2080 | U.U2 | ||
| OV | 379 | 1.189 (0.919 - 1.538) 0.1868 | Ba 45 | 953 | 1.231 (0.932 1.625) | STO 379 | 0.952 10. 753 - 1 | |||
| PAAD | 179 | 0.693 (0.458 - 1.048) 0.0825 | pop | 452 | 0.622 (0.390 - 0.992) | 00484 | PHAD | 0.052 /0 5/1-1 380 | 19426 | |
| PCPG | 1.84 | 0.364 (0.072 - 1.834) 0.2205 | 400 | 2 790 | 054 | PCFG | 184 | 1.255 (0.533 - 2.886) | 0.6210 | |
| PRAD | 501 | 6.320 (0.775 - 51.553) 0.0851 | PRAD | 0.811 10 278 2 3BRY | PRAD | 501 | 1.921 (1.257 2.936) | 0.0026 | ||
| READ | 168 | 0,556 (0.244 - 1.267) * 0,1626 | HEAD | 150 | (0.276 - 2.386) | READ | 165 | 0.974 (0.509 - 1.864) | 0.9357 | |
| SARC | 263 | 1.907 (1.273 - 2.858) 0.0018 | SARC | 257 | 1.716 (1,105 - 2.687) | 0.0168 | SARC | 263 | 1.219 (0,876 - 1.695) | 0.2397 |
| SKCM | 457 | 1.222 (0.934 - 1.598) 0.1437 | SKCM | 451 | 1.262 (0.947 - 1.681) | 0.1116 | SKICM | 458 | 1.286 (1.028 1.609) | 0.0276 |
| STAD | 370 | 0.826 (0.595 1.146) 0.2521 | STAD | 349 | 1.145 (0.748 - 1.754) | 0.5327 | STAD | 372 | 1.257 (0.878 - 1.800) | 0.2113 |
| TGCT | 139 | 0.745 (0.100 - 5.557) 0.7740 | TGCT | 139 | 1.925 (0.174 - 21.227) | 0.5930 | TGCT | 139 | 1.165 (0.622 - 2.181) | 0.6326 |
| THCA | 512 | 1.071 (0.401 - 2.858) 0.8913 | THCA | 505 | 1.408 (0.315 - 6.301) | 0.6548 | THCA | 512 | 0.650 (0.384 - 1.137) | 0,1343 |
| THYM | 119 | 0.600 (0.148 2.428) 0.4736 | THYM | 119 | 0.451 (0.046 4,473) | 0,4967 | THYM | 119 | 0.518 (0.209 - 1.283) | 0,1550 |
| UCEC | 553 | 1.366 (0.903 - 2.068) 0,1398 | UCEC | 551 | 1.359 (0.824 2.242) | 0.2290 - | UČEC | 553 | 1.184 (0.838 - 1.674) | 0.3384 |
| UCS | 57 | 0.2652 0.682 (0.348 - 1.337) | UCS | 55 | 0.663 (0.325 1,356) | 0.2604 | UCS | 57 | 0.908 (0,476 - 1.733) | 0,7097 |
| UVM | 8.0 | 2 0.1980 0.565 (0.238 1.342) | UVM | 80 | 0.625 (0 258 - 1.514) | - 0 2981 | UVM | 79 | 0.826 (0.382 - 1.795) | 0.6333 |
0
5
10
15
20
0
8
5
15
DVD
2.5
5.0
7.5
D
OS-ACC
E
OS-KIRP
F
OS-LGG
G
OS-LIHC
H
OS-MESO
I
OS-SARC
1.00
DHX34
1,0
DHX34
1.00
DHX34
1.0
DHX34
1.00
DHX34
1.0
DHX34
Low
Low
- Low
Low
Low
High
0,9
High
High
High
- High
Low
High
Survival probability
0.75
Survival probability
Survival probability
0.75
Survival probability
0.8
Survival probability
0.75
Survival probability
0.8
0.8
0.50
0.7
0.50
0.6-
0.50
0.6
0.6
0.4
0.25
0.25
Overall Survival HR = 8.05 (2.99-
0.25
Overall Survival HR = 2.31 (1.62 - 3.30)
Overall Survival
+
.64)
0,5
Overall Survival HR = 3.18 [1.64 < 0,001
16.19)
Overall Survival HR = 1.91 (1.35 - 2.72)
HR = 2.76 P< 0,001
1.66
Overall Survita
P< 0,001
0.2
4.59}
0.4
P< 0,001
P< 0.001
HR = 1.91 (1.27 - 2.86)
0.00
P= 0.002
0
50
100
150
0
50
100
150
0
50
100
150
200
0
30
60
90
120
0
25
50
75
0
50
100
150
Time (months)
Time (months)
Time (months)
Time (months)
Time (months)
Time (months)
J
DSS-ACC
K
DSS-KIRP
L
DSS-LGG
M DSS-LIHC
N DSS-MESO
DSS-SARC
1.00
DHX34
1.0
DHX34
1.00
DHX34
1.0
DHX34
1.00
DHX34
1.0
DHX34
Low
- Low
Low
High
High
Low
Low
High
High
High
Low
High
Survival probability
0.75
Survival probability
0,9
Survival probability
0.75
Survival probability
0.8
Survival probability
0.75
Survival probability
0.8
0.50
0,8
0.50
0.6
0.50
0.6
0.25
Disease Specific HR = 7.67 (2.83 -
ervival
Disease Specific HR = 5.15 [1.95-
0.25
0.7
Survival
Disease Specific Survive HR = 2.38 (1.63 - 3.48)
0.4
Disease Specific Survival HR = 1.78 (1.14 - 2.77)
0.25-
Disease & HR =2.80
cife Survival
44- 5.45}
Disease Sportfio Survival
+
P < 0.001
20.81)
13.57)
HEY 750 0 HR = 1,72 (1,40 -+2.67)
P < 0.001
P< 0.001
P = 0.011
P= 0.002
+
0.4
P= 0.016
0
50
100
150
0
50
100
150
0
50
100
150
200
0
30
80
90
120
0
25
50
75
0
50
100
150
Time (months)
Time (months)
Time (months)
Time (months)
Time (months)
Time (months)
P
PFI-ACC
Q
PFI-KIRP
R
PFI-LGG
S
PFI-LIHC
T
PFI-SKCM
1.0 -
DHX34
1.00
DHX34
1.00
DHX34
1.0-
DHX34
1.00
DHX34
Low
High
Low
High
Low
High
LOW High
Low High
Survival probability
0.8
Survival probability
0.75
Survival probability
0.75
Survival probability
0.8
Survival probability
0.75
0.6
0.50
0.50
0.6
0.50
0.4
0.25
0.4
0.25
Progi
Free Interval
HR = 4.4047.21 - 8.85)
Progress Free Inter HR = 2.00 (1.16 - 3
0.25
44)
Progress Free
HR = 1.97 (1,49 -
2.6
Progress Thet Cherval R =1.53 (1.14-1-04]
Progress
Theyval
0.2
P < 0.001
0.00
P= 0.012
P< 0.001
R = 1.29 (1303-LLA
0.2
P = 0.004
0.00
P= 0.028
0
50
100
150
0
50
100
150
0
1000
2000
3000
4000
5000
0
30
60
90
120
0
3000
6000
9000
Time (months)
Time (months)
Time (days)
Time (months)
Time (days)
outer membrane endoplasmic reticulum membrane network (NES = 2.617, P.adj = 0.001) and negatively with adaptive immune response (NES = - 3.414, P.adj < 0.001) (Fig. 9M). In PAAD, DHX34 showed positive associations with Negative Regulation of Nucleobase Containing Compound Metabolic Process (NES = 2.751, P.adj = 0.001) and negative associations with Digestion (NES = - 2.228, P.adj = 0.009) (Fig. 9N). For SARC, DHX34 showed positive associations with sequence-specific DNA binding (NES = 4.639, P.adj < 0.001) and negative associations with immunoglobulin complex (NES = - 5.998, P.adj < 0.001) (Fig. 9O). Finally, in SKCM, DHX34 positively correlated with immunoglobulin production (NES = 1.972, P.adj = 0.012) and negatively with skin development (NES = - 3.417, P.adj < 0.001) (Fig. 9P). These findings indicate that DHX34 is extensively involved in regulating cellular biological functions across multiple cancer types.
Correlation of DHX34 with TIME in pan-cancer
We conducted gene co-expression analyses in the TISIDB database to explore the relationship between the expression of DHX34 and various components of TIME, including lymphocytes, immune stimulators, immune inhibitors, MHC molecules, chemokines, and receptors. Our study revealed significant correlations between DHX34 expression and multiple immune factors in pan-cancer. Specifically, DHX34 expression showed a positive correlation with the expression of lymphocyte subsets such as Mem B in LGG and a negative correlation with iDC in KIRP (Fig. 10A). Among the 45 immune stimulators studied, DHX34 expression positively correlated with TNFRSF25 in KIRP and negatively correlated with TMEM173 in TGCT (Fig. 10B). In the analysis of 24 immune inhibitors, we observed a negative association between DHX34 expression and KDR in LIHC, while
A
ACC
B
ACC
C
KIRP
D
LGG
6
**
5
The expression of DHX34 Log2 (TPM+1)
The expression of DHX34 Log2 (TPM+1)
6
5
The expression of DHX34 Log2 (TPM+1)
The expression of DHX34 Log2 (TPM+1)
5.
5
4 .
4
4
4
3
3
3
3
2
2
2
2
T1
T2
T3
T4
Pathologic T stage
Stage I
Stage II Stage III Stage IV Pathologic stage
MO
M1
G2
G3
Pathologic M stage
WHO grade
E
LGG
F
LIHC
G
LIHC
H
LIHC
6 -
**
The expression of DHX34 Log2 (TPM+1)
5
6.
6 -
The expression of DHX34 Log2 (TPM+1)
5
The expression of DHX34
Log2 (TPM+1)
The expression of DHX34
5 -
Log2 (TPM+1)
5 .
4
4 .
4 .
4
3
3
3
3
2
2
2
2
WT
Mut
IDH status
⇐ 400
>400
T1
T2
T3
T4
G1
G2
G3
G4
AFP(ng/ml)
Pathologic T stage
Histologic grade
LIHC
J
SKCM
**
6
6.
The expression of DHX34 Log2 (TPM+1)
The expression of DHX34 Log2 (TPM+1)
5
5
4
4 .
3
3
2
2
Stage I
Stage II
Stage III
Stage IV
No
Yes
Pathologic stage
Radiation therapy
a positive association was found between DHX34 expression and PVRL2 in UVM (Fig. 10C). Fig. 10D demonstrated that DHX34 expression positively correlated with TAPBP in PAAD and negatively correlated with MHC molecule B2M in READ. Additionally, our study of chemokines revealed a negative correlation between DHX34 expression and CCL14 in LIHC, while a positive correlation was observed between DHX34 expression and CCL26 in TGCT (Fig. 10E). In the analysis of receptors, DHX34 expression positively correlated with CCR10 in LGG and negatively correlated with CCR1 in PAAD (Fig. 10F). Collectively, these findings indicated that DHX34 holds promising potential in predicting immune-related phenotypes in pan-cancer.
The single-cell expression of DHX34 in LIHC
Utilizing the scRNA-seq TISCH2 database, we procured eight distinct LIHC datasets for single-cell analysis to investigate the relationship between immune cell distribution and DHX34 expression levels at the single-cell level. Our analysis of the LIHC_GSE140228 Smartseq2 and LIHC_GSE146115 datasets revealed that monocytes or macrophages exhibited higher expression levels of DHX34 (Fig. 11A). Furthermore, we obtained insights into the distribution and expression of DHX34 across different immune cells through violin plot and clustered plots of scRNA-seq data (Fig. 11B-E). These findings suggest a significant correlation between DHX34 expression levels and the types and proportions of immune cells in LIHC.
A
ACC
B
KIRP
C
LGG
D
LIHC
1
5
H
092 (TPM+
4
3
A
LOW High
P
2
911
E
8
2
CLABAP
CLASHP
MOOR1
KRİ
BICHA
DAZAPI
BART
OTPgp3
POLDI
SNANPTO
TRUTH TRUT1
CONOT2
CNOT3
SCAF1
HNRNPA281
BICRA
DMPK
PROVI
FUS SART
DCAF15 DAZAPI
1
FTOV!
CHTOP
STIRN
MAGOH8
MAGOH
25
2.5
PYGICQ
Z-40era 25
SMG5
SNRNPTO
NROCZAP
INTS11
CODCST
X
I
4
CACTIN
ARNGEFI WASP
CAPNID
SAPO
-25
PTOVI
4.5
.
LØZAP2
TIONGE
5/354
-45
SMGS
5MG8
Surber
SPSWAP
PBX046
88
CCTOP1 TYK2
KMTẮC
i
SMG6
ZMPars
WRAPPS
ZNF335
CASC3
ANDREY
MGI
S
M2F1
PRIKCSH
¢
SUOPZ
A
SUPTSH
COGSL
ZMF138
POLD1
PEX.12
PTBPI
FRIVTİ
P
PHF16
USP21
MED25
BLOC188
TIX34
D
D
RPF
9
PO
SMG7
E
MESO
F
PAAD
G
SARC
H
SKCM
RBMUA
EIF443
4
A
2
(TPM-1)
F
$1
Sa
A
A
1!
UPF
E
1
8
PYM1
·
SCAFI
SNRNPTO
SCAF1
UZAF2
PNRC2
STRIH
INFAT3
BICRA
GRWD1
₹
1
EPNI
SCAF!
SART
PTOV1
UPF38
PLIGA
PNAP
SHAMPOO
SAFD
1
TIMMSD
POINTI ZNf-167
CLASAP
POLD1
TRIM20
MCD25
PPT31
ANAPC? TRIMZE
CAPN TO
CLASA CLASES
SCAFI PTAPI PTOPI
CACTIN PPPINST ZNP 71
MHOTZ
D.0
PERO-40 ATO48 PRPFAT
-2.5
E
KHSKUP BICHA
ZHF473
PPPSC CHỘTS
NUPAZ
SPHK2
HSPØP;
LMNB#
ZNP692
CHOTO G
RF29PI
KITSA
TEGID ET
TRE25
MEDOS
RUMBLE
LIGA
MOCN/14
NỤPER
PRIMIT1
NUP62
7
SKCM-Metastasis (n=368)
L
SKCM-Primary (n=103)
CCDC97
DHX34
6
®
2
UVM (n=80) UCS (n=57)
UCEC (n=545)
THYM (n=120)
THCA (n=509)
TGCT (n=150)
STAD (n=415)
SKCM (n=471)
SARC (n=260)
READ (n=166)
PRAD (n=498)
PCPG (n=181)
PAAD (n=179)
MESO (n=87)
WSC (n=501)
WLAD (n=515)
UHC (n=371)
LGG (n=516)
KIRP (n=290)
KIRC (n=533)
KICH (n=66)
HNSC HPV+ (n=98)
HNSC-HPV- (n=422)
HNSC (n=522)
GBM (n=153)
ESCA (n=185)
DLBC (n=48)
COAD (n=458)
CHOL (n=36)
CESC (n=306)
BRCA-LumB (n=219)
BRCA-LumA (n=568)
BRCA Her2 (n=82)
BRCA-Basal (n=191)
BRCA (n=1100)
BLCA (n=408)
PYGO2
0
1
4
OV (n=303)
ACC (n=79)
0
DAZAP1
A
.
0
CIORF77
STRN4
o.
CCDC97
.
8
p = 0.05
p > 0.05
Spearman_Cor
P
4
SRRT
0
2
DAZAP1
!
9
NUP62
0
pi?
PTOVI
PYGO2
CHTOP
SNRNP70
SCAF1
1
€
0
SNRNP70
4
PTOV1
A
0
NUP62
SART
8
2
0
8
STRN4
SCAF1
Correlation
-1
1
×
M
EIF4A3
DHX34
UVM (n=80) UCS (n=57)
UCEC (n=545)
THYM (n=120)
THCA (n=509)
TGCT (n=150)
SKCM (n=471)
C
STAD (n=415)
SKCM-Primary (n=103)
SKCM-Metastasis (n=368)
SARC (n=260)
READ (n=166)
PRAD (n=498)
PCPG (n=181)
PAAD (n=179)
OV (n=303)
MESO (n=87)
LUSC (n=501)
LUAD (n=515)
LIHC (n=371)
LGG (n=516)
KIRP (n=290)
KIRC (n=533)
KICH (n=66)
HNSC-HPV+ (n=98)
HNSC-HPV- (n=422)
HNSC (n=522)
GBM (n=153)
ESCA (n=185)
DLBC (n=48)
COAD (n=458)
CHOL (n=36)
CESC (n=306)
BRCA-LumB (n=219)
BRCA-LumA (n=568)
BRCA-Her2 (n=82)
BRCA-Basal (n=191)
BRCA (n=1100)
BLCA (n=408)
ACC (n=79)
PRPF19
2
4
7
e
0
5
2
SMG1
P
3
V
9
0
C17ORF71
a
«
SMG8
2
C19ORF61
8
O
1
2
V
CDC40
p = 0.05
p > 0.05
Spearman_Cor
CDC40
CDC5L
o
2
EIF4A3
CDC5L
SMG9
€
PRPF19
v
0
SMG1
0
*
-
UPF1
1
UPF3A
UPF2
2
UPF1
0
d
E
0
9
UPF3A
UPF2
Correlation
-1
1
A
B
C
D
E
1.0
CCDC97
1.0
CHTOP
1.0
DAZAP1
1.0
EIF4A3
1.0
NUP62
Low
Low
- Low
Low
- Low
High
High
- High
High
- High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
Overall Survival HR = 1.79 (1.26 - 2.54)
Overall Survival HR = 1.77 (1.25 - 2.52)
Overall Survival HR = 1.65 (1.17 - 2.35)
Overall Survival HR = 1.57 (1.11 - 2.23)
Overall Survival
0.2
HR = 1.55 (1.09 - 2.19)
P = 0.001
P= 0.001
0.2
P = 0.005
0.2
P = 0.011
0.2
P= 0.014
0
30
60
90
120
0
30
60
90
120
0
30
60
90
120
0
30
60
90
120
0
30
60
90
120
Time (months)
Time (months)
Time (months)
Time (months)
Time (months)
F
G
H
J
1.0
PRPF19
1.0
PYGO2
1.0
SCAF1
1.0
SMG8
1.00
SMG9
Low
Low
Low
Low
Low
High
High
High
High
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
Survival probability
0.75
0.6
0.6
0.6
0.6
0.50
0.4
0.4
0.4
0.4
Overall Survival HR = 1.65 (1.17 - 2.34)
Overall Survival HR = 1.59 (1.12 - 2.25)
Overall Survival HR = 1.49 (1.05 - 2.11)
Overall Survival HR = 1.51 (1.06 - 2.13)
0.25
Overall Survival
P = 0.005
HR = 1.63 (1.14 - 2.31)
0.2
4 #
0.2
P = 0.009
0.2
P = 0.026
0.2
P = 0.021
P = 0.007
0
30
60
90
120
0
30
60
90
120
0
30
60
90
120
0
30
60
90
120
0
30
60
90
120
Time (months)
Time (months)
Time (months)
Time (months)
Time (months)
K
L
O
1.0
SNRNP70
1.0
SRRT
Low
Low
High
High
UPF2
12
0
0
12
Survival probability
Survival probability
0.8
STRN4 25
0
12
0
0
12
12
12
25
0
12
0
38
0.8
SRRT
12
12
25
12
12
0
12
12
0
12
12
0
25
12
0
0
25
0 25
12
0
SNRNP70
0
12
25
12
0
0
12
0.6
0.6
SMG9
38
0
12
0
25
0
0
12
12
12
12
12
12
0
25
12
25
Symbol
SMG8
12
0
12
0
25
12
0
12
0
SCAF1
0
0
38
0.4
0.4
PYGO2
0
12
12
12
0
12
12
25
12
0
0
25
0
25
Overall Survival
Overall Survival HR = 1.51 (1.07 - 2.14)
PRPF19
12
0
25
0
12
12
0
0 0
12
0
25
12
12
12 25
0
0
38 12
0
12
HR = 1.53 (1.08 - 2.16)
0
25
P = 0.017
0.2
P= 0.019
NUP62
50
0
62
0
12
0
12
12
0
12
0
12
12
12
0 12
EIF4A3 DHX34
25
0
25
0
12
0
12
0
12
0
0
12
0
25
0
12
0
30
60
90
120
0
30
60
90
120
Time (months)
Time (months)
25
12
25 25
0
0
12
12
25
12
0
0 0
25
0
25
DAZAP1
12
0
0
0
12
25
0
25
0
12
M
N
CHTOP
12
12
25
0
12 12
12 25
0
0
0
12
0
12
CCDC97
12
0
12
0 25
0
12
25
12
0
12
12
12
12
12
25
12
12
25
12
0
25
1.00
STRN4
1.0
UPF2
EMT_A
EMT_I
RTK A
RTK
Low
Low
High
High
Survival probability
0.75
Survival probability
Apoptosis_A
Apoptosis_I
CellCycle_A
CellCycle
DNADamage_A
DNADamage
Hormone AR_A
Hormone AR
Hormone ER_A
Hormone ER
PI3KAKT_A
PI3KAKT_I
RASMAPK_A
RASMAPK
TSCmTOR A
TSCmTOR_I
0.8
0.50
0.6
Pathway (A: Activate; I: Inhibit)
0.25
0.4
Overall Survival
HR = 1.60 (1.13 - 2.27)
Overall Survival HR = 1.55 (1.09 - 2.20)
P=0.008
0.2
P=0.014
Percent
38
0
62
0
30
60
90
120
0
30
60
90
120
Time (months)
Time (months)
Inhibit
Activate
The immunotherapy and chemotherapy response analysis of DHX34
To evaluate the clinical potential of DHX34 in immunotherapy, we analyzed the ICI responses in DHX34 high and low samples across various cancer types. Employing the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, we estimated the potential ICI response. Our calculations revealed that the DHX34 low group exhibited lower TIDE scores in KIRP, LGG, LIHC, and SKCM, indicating that lower DHX34 expression predicts a more favorable ICI treatment response in these cancers (Fig. 12A-D).
For drug therapy, DHX34 was found to be inversely correlated with the sensitivities of most drugs. Notably, BHG712, WZ3105, and Methotrexate emerged as the top three drugs with the highest negative correlation in the GDSC database (Fig. 12E).
Similarly, COL-3, docetaxel, and linifanib ranked as the top three drugs with the strongest negative correlation in the CTRP database (Fig. 12F). These findings suggest that DHX34 may serve as a potential biomarker for predicting drug therapy responses.
Correlation of DHX34 with ferroptosis and m6A-related genes in LIHC
In LIHC, we performed a correlation analysis between DHX34 expression and Ferroptosis-related genes, and found that DHX34 was significantly correlated with most Ferroptosis-related genes (CISD1, EMC2, FANCD2, FDFT1, GPX4, HSPA5, HSPB1, MT1G, NFE2L2, SAT1, SLC1A5, SLC7A11, ACSL4, ATL1, ATP5MC3, CARS1, CS, GLS2, LPCAT3, RPL8, TFRC) (Fig. 13A). We also found that DHX34 expression was significantly correlated with most m6A-related genes (CBLL1, METTL14,
METTL16, METTL3, RBM15, RBM15B, VIRMA, WTAP, YTHDC1, YTHDC2, YTHDF3, ZC3H13, ALKBH5, EIF3A, FTO, HNRNPA2B1, HNRNPC,
IGF2BP1, IGF2BP2, IGF2BP3, RBMX, YTHDF1, YTHDF2) (Fig. 13B).
A
ACC
B
KIRP
C
LGG
D
LIHC
Up
Not sig
Down
Up
Not sig
Down
Up
Not sig
Down
Up
Not sig
Down
·
50
10
120
30
-Log 10 (P.adj)
100
40
-Log 10 (P.adj)
8
-Log 10 (P.adj)
-Log 10 (P.adj)
80
6
20
30
60
4
20
10
40
2
·
20
10
0
Down:
Up: 433
0
Jown: 232
Up: 570
0
Down. 291%
Up: 506
0
Down:
Up: 1037
-4
0
4
-5.0
-2.5
0.0
2.5
5.0
-3
0
3
-4
0
4
Log2 (Fold Change)
Log2 (Fold Change)
Log2 (Fold Change)
Log2 (Fold Change)
E
MESO
F
PAAD
G
SARC
H
SKCM
Up
Not sig
Down
Up
Not sig
Down
Up
Down
Up
Not sig
Down
30
Not sig
50
12
25
30
10
40
-Log 10 (P.adj)
-Log 10 (P.adj)
20
-Log 10 (P.adj)
-Log 10 (P.adj)
8
20
30
6
15
6
4
10
20
10
2
.
5
10
0
Down: 102
Up: 123
0
Down: 146
Up: 94
0
Down: 506
Up: 519
0
Down:
620
Up: 114
-3
0
3
-4
-2
0
2
4
-2.5
0.0
2.5
-6
-3
0
3
6
Log2 (Fold Change)
Log2 (Fold Change)
Log2 (Fold Change)
Log2 (Fold Change)
I
ACC
J
KIRP
K
LGG
L
LIHC
NES =- 4.287
NES =- 3.436
NES
5.108
NES A-4.032
Immunoglobulin Complex
P.adj 50
Small Molecule Metabolic Process
Padi sophia
Synapse
0.001
Organic Acid Metabolic Process
Pal 0.001
NES =- 3.968
NES =- 2.919
NES A-4.862
NES A-3.521
Antigen Binding
Pady SOPpt
Organophosphate Metabolic Process
Padi 5.0.001
Neuron Projection
P.aos <0.001 +
Oxidoreductase Activity
Peal $10.001
NES =- 3.841
NES = - 2.902 Padj covid
NESA-4.346 Pag 0.001
NES
3.288
Adaptive Immune Response
Pady SO
Organic Acid Metabolic Process
Intrinsic Component of Plasma Membrane
Monocarboxylic Acid Metabolic Process
0.001
NES =- 3.364
NES =- 2.877
NES — 4.214
NES 7-3.266
Immune Response
Padi s.0.001
Active Transmembrane Transporter Activity
Pagina000
Somatodendritic Compartment
P.agi <0.001
Response To Xenobiotic Stimulus
Pat 10.001
NES =- 3.356
NES =- 2.858
NESA-4.023
P.adj < 90ft
NES 3.253
Immunoglobulin Production
Secondary Active Transmembrane Transporter
Pagina.det
Q.001
Ban-10.001
NES = 3.147
Activity
Synaptic Signaling
Cellular Response To Xenobiotic Stimulus
NES =2.102
NES =3.750
NES =2.232
Sister Chromatid Segregation
P.adj < 0.001
Negative Regulation of Nucleobase Containing Compound Metabolic Process
Padj = 0.021
Embryonic Morphogenesis
P.adý < 0.001
1
Embryonic Morphogenesis
P.adj = 0.006
NES =3.181
NES =2.116
NES =3.787
NES =2.254
Cell Cycle Process
P.adj < 0.001
Sequence Specific DNA Binding
Padi = 0.028
Regionalization
P.ady < 0.001
Epithelial Cell Differentiation
P.adj = 0.010
NES =3.207
NES = 2.123
NES =3.798
NES =2.374
Chromosome
P.adj < 0.001
Negative Regulation of Cell Population
Padi = 0.027
Anterior Posterior Pattem Specification
Pady < 0.001
Developmental Process Involved In Reproduction
Pady = 0.003
Proliferation
+
NES =3.376
NES =2.188
NES = 3.810
NES =2.383
Chromosome Organization
P.adj < 0.001
Developmental Growth
P.adj = 0.015
Transcription Regulator Activity
P.ady < 0.001
Regionalization
P.adj = 0.003
NES = 3.624
NES =2.192
NES = 3.914
NES =2.483
Cell Cycle
P.adj < 0.001
Tissue Development,
Padj = 0.014
Pattern Specification Process
Pady < 0.001
Pattern Specification Process
Pacy < 0.001
-4
-2
0
2
4
4
0
4
-2.5
0,0
2.5
5.0
-4
-2
0
2
4
6
M
MESO
N
PAAD
O
SARC
P
SKCM
NES =- 34414
NEŞ =- 2:028
NES =- 5.998
NES = - 3.417
Adaptive Immune Response
Padsopen
Digestion
Podi - Dong
Immunoglobulin Complex
Skin Development
P.adj $ 0.001
NES =- 3/287
NES =- 2048
NES =- 5.683
NES =- 3.415
Immunoglobulin Complex
Pad $0.001
NES =- 3,221
Peptidase Activity
Pages Rose
Adaptive Immune Response
Pami _< 0,001
Keratinization
P.adj < 0.001
NES
5.303
NES =- 3.377
Contractile Fiber
Pag 90 201
NES = 2018
Antigen Binding
001
Epidermis Development
P.adj < 0.001
NES =- 2/787
Serine Hydrolase Activity
Paghe pose
NES =- 5.069
NES =- 3.310
Antigen Binding
Padi ≤ 0.001
Immune Response
Pam < 0001
Keratinocyte Differentiation
P.adj < 0.001,
NES == 2.710
NEŞ -=- 1,983 P.poj = 0,060
NES -4.678
NES =- 3.213
I Band
Park s0 001
Lipid Catabolic Process
Immunoglobulin Production
Epidermal Cell Differentiation
P.adj < 0.001,
NES =2.328
NES = 1.888
NES =4.408
NES =2.220
Sequence Specific DNA Binding
Pad = 0,006
Endopeptidase Activity
Page 1 104
Transcription Regulator Activity
P.adj < 0.001
Antigen Binding
Pady = 0.002
NES =2.328
NES = 4.487
NES =2.401
DNA Binding Transcription Factor Activity
Padi = 0.006
NES = 2.548
Chromatin
P.adj < 0.001
Negative Regulation of Biosynthetic
Padi = 0.006
Adaptive Immune Response
P.adj < 0.001
NES =2.440
Process
NES =4.491
NES =2.420
Chromatin
Padi = 0.003
NES =2.686
Chromosome
P.adj < 0.001
Immunoglobulin Complex
P.adj < 0.001
NES =2.567
Organelle Subcompartment
Padi = 0,002
Negative Regulation of Transcription By RNA Polymerase li
Padj = 0.001
NES = 4.634
NES =1.934
DNA Binding Transcription Factor Activity
P.adj < 0.001
Endocytosis
P.adj = 0.014
NES =2.617
NES =2.751
NES =4.639
NES =1.972
Nuclear Outer Membrane Endoplasmic Reticulum
Pad = 0.001
Negative Regulation of Nucleobase Containing Compound Metabolic Process
Padj = 0.001
P.ad) < 0.001
P.ady = 0.012
Membrane Network
Sequence Specific DNA Binding
Immunoglobulin Production
III
-4
0
4
-4
-2
0
2
4
-5.0
-25
0.0
2.5
5.0
-6
4
-2
A
lymphocytes
B
Immunostimulator
Act CD8
LGG (530 samples)
C10orf54
KIRP (291 samples)
Tem CD8
6
.
Tem CD8
-
Act CD4
Mem_B_abundance
0.4
CD40LGJ
TNFRSF25_exp
4
Tem CD4
9838-
Tem CD4
0.0
CDBD_
N
Tfh
CXCL12- CXCLIE
Tgd
CASA
Th1
-0.4
HHLA2
0
Th17
Icocio-
Th2
IL2RA
-2
Treg
3
4 DHX34_exp
5
ILGR-
BIRCIJ
4
6
Act B
DHX34_exp
5
Imm B
Spearman Correlation Test: rho = 0.304, p = 1.19e-12
LTA ]
Spearman Correlation Test: rho = 0.621, p < 2 2e-16
Mem B
MICB-
NTSE-
NK
PVR
RAETTE
CD56bright
KIRP (291 samples)
TGCT (156 samples)
TMEM173
CD56dim
TNFRSF13B-
MDSC
0.3
INFRSF13C TNERSE14-
6
NKT
iDC_abundance
INFRSF17 J
Act DC
INFRSF18-
TMEM173_exp
PDC
0.0
TNFRSF25”
TINFRSF4”
IDC
TNFRSF87 INFRSF9-
1
Macrophage
Eosinophil
-0.3
TNFSF13B-
TNFSF14-
3-
Mast
TNFSF157
Monocyte
TNFSF18]
2
-0.6
Neutrophil
4
DHX34_exp
5
6
THEGEO - TNFSF9
ULBP1
5
%
DHX34_exp
5
7
Q
Spearman Correlation Test: rho = - 0.518, p < 2.2e-16
Spearman Correlation Test: rho = - 0.632, p < 2.2e-16
4
V
C
Immunoinhibitor
D
MHC molecule
ADORAZA
LIHC (373 samples)
B2M
PAAD (179 samples)
BTLA
HLA-A
-
CD160
6
CD244
HLA-B
9-
CD274
KDR_exp
HLA-C
TAPBP_exp
CD96
O
HLA-DMA
CSF1R
HLA-DMB
8
CTLA4
2
HLA-DOA
HAVCR2
HLA-DOB
IDO1
0
7
IL10
2
3
DHX34_exp
4
5
6
7
HLA-DPA1
3
4
6
IL10RB
Spearman Correlation Test: rho = - 0.484, p < 2.2e-16
5 DHX34_exp
HLA-DPB1
Spearman Correlation Test: rho = 0.383, p = 1.57e-07
KDR
HLA-DQA1
KIR2DL1
UVM (80 samples)
HLA-DQA2
READ (167 samples)
KIR2DL3
HLA-DQB1
.
LAG3
8
*
HLA-DRA
13
LGALS9
HLA-DRB1
PDCD1
PVRL2_exp
12
PDCD1LG2
HLA-E
B2M_exp
PVRL2
HLA-F
11
TGFB1
HLA-G
6
10
TGFBR1
TAP1
TAP2
9
TIGIT
VTCN1
5
4
6
TAPBP
DHX34_exp
5
8
.
4
Spearman Correlation Test: rho = 0.66, p < 2.20-16
5 6 DHX34_exp
7
C
3
8
9
Spearman Correlation Test: Tho = - 0.539, p < 2.20-16
E
Chemokine
F
Receptor
CCL1 ]
LIHC (373 samples)
LGG (530 samples)
CCL2-
CCR1
CCL4-
CCR2
čCL77
2
CCL14_exp
5
CCR3
CCR10_exp
COLLE
celá- CCL14
CCR4
0
CCL15 CCL16 CCL17 ]
CCR5
0
CCL18- CCL197
CCR6
-2.
CCL20”
CCR7
1
CCL217
.
3
6
3
CCL23-
2
3
DHX34_exp
4
CCR8
4
5
CC124
Spearman Correlation Test: rho = - 0.314, p = 6.68e-10
DHX34_exp
CCR9
Spearman Correlation Test: ho = 0.4, p < 2.2e-16
COL26-
CCL27 ]
CCR10
4CL28_
TGCT (156 samples)
CXCR1
PAAD (179 samples)
CX3CL1
CACLI_
CXCL2
CXCR2
CXCL3”
5.0
-1
:
CXCL5 7
CXCR3
4
CXCL6 CXCLB-
CCL26_exp
CXCL9-
2.5
CXCR4
CCR1_exp
CXCL10-
cxCL11 -
2
15-
0.0
CXCR5
cxci13-
CXCR6
CXCL14
CXCL16
-2.5
CXCL17”
XCR1
D
XCL1
XCL2-
5
DHX34_exp
6
7
CX3CR1
3
4
DHX34_exp
5
6
?
93
10
6
Spearman Correlation Test: rho = 0.433, p = 2.34e-08
A
Y
O
2
A
Spearman Correlation Test: rho = - 0.466, p = 6.76e-11
Experimental validation based on clinical samples
The expression of DHX34 was further validated in our cancer cohorts using qRT-PCR and WB among 4 different types of cancer, including colonic carcinoma, LIHC, LUAD, and STAD. As shown in Fig. 14A-D, DHX34 was overexpressed in those cancer tissues compared to their corresponding non-tumor tissues. Specifically, we randomly selected 24 LIHC samples and analyzed the correlation between the
expression level of DHX34 and the pathological stage. IHC results revealed a positive correlation between high DHX34 expression and advanced pathologic stages in LIHC (Fig. 14E-F). Moreover, we randomly selected 6 LIHC samples and analyzed the correlation between DHX34 expression levels and CD68 expression levels. DHX34 expression exhibited a positive correlation with CD68 expression in LIHC (Fig. 14G). Finally, we analyzed the correlation of DHX34 expression level with OS and PFI in 50 LIHC samples. Survival analysis further indicates that
patients with higher DHX34 expression exhibit shorter OS (HR = 0.41, 95% CI: 0.21-0.81, p = 0.031)
and PFI (HR = 0.50, 95% CI: 0.26-0.96, p = 0.035) in LIHC (Fig. 14H-I).
A
DHX34
log(TPM/10+1)
LIHC GSE 125449 aPDL1aCTLA4
0.02
0.08
0
0.07
0.03
0.02
0.03
0.6
LIHC GSE140228 10X
0.02
0.02
0.03
0.02
0.02
0.02
0.01
0.02
0
0.04
0.11
0.04
0.4
LIHC GSE140228 Smartseg2
0.09
0.14
0.07
0.09
0.08
0.06
0.02
0.22
0.57
0.13
LIHC GSE146115
0.13
0.07
0.03
0.28
0.23
0.2
LIHC GSE146409
0.04
0.03
0.03
0.03
0.06
0
LIHC GSE166635
0.03
0.06
0.02
0.01
0.07
0.06
0.03
0.04
0.05
0.04
0.05
LIHC GSE179795
0.04
0.04
0.02
0.17
LIHC GSE98638
0.16
0.14
0.19
0.18
0.16
CD4Tconv
Treg
Tprolif
CD8T
CD8Tex
NK
ILC
B
Plasma
DC
Mono/Macro
Mast
Endothelial
Fibroblasts
Epithelial
Malignant
B
DHX34
?
8
$
LIHC_GSE140228_Smartseq2,
2
1
0
CD4Tconv
Tprolif
CD8Tex
NK
ILC
8
Plasma
DC
Mono/Macro
Mast
C LIHC_GSE140228_Smartseq2
DHX34
3.5
CD4Tconv
Plasma
- 3.0
CD8Te
Celltype (major-lineage)
B
2.5
CD4Tconv
៛
prolif
B
CDBTex
€
*
DC
2
2.0
ILC
Mast
Mast
ILC
Mono/Macro
1.5
NK
DC
Plasma
Tprolif
1.0
Mono/Macro
0.5
0.0
D
DHX34
>
.
5
LIHC_GSE146115
2
.
·
Tprolif
CD8T
8
Mono/Macro
Malignant
E
LIHC_GSE146115
DHX34
.
- 3.5
3.0
2.5
Celltype (major-lineage)
Malignant
B
CDBT
2.0
Mono/Macro
Malignant
.
Mono/Macro
2
Tprolif
1.5
CD8T
1.0
Tprolif
B
0.5
0.0
A
KIRP
B
LGG
C
LIHC
D
SKCM
3-
.
.
4
-
1-
2
2
1-
I
1.
1
TIDE score
TIDE score
TIDE score
TIDE score
1
D
0
-I
-1
4
2
2
3
1
DHX34High
DHX34Low
DHX34High
DHX34Low
DHX34High
DHX34Low
DHX34High
DHX34Low
E
Correlation between GDSC drug sensitivity and mRNA expression in pan-cancer
F
Correlation between CTRP drug sensitivity and mRNA expression in pan-cancer
FDR
FDR
0 ⇐ 0.05
o ⇐ 0.05
FDR
FDR
Symbol
0.001
0.001
⇐ 0.0001
Symbol
⇐ 0.0001
DHX34-
0
DHX34-
Correlation
Correlation
-0.2
-0.3
0.0
-0.1
0.2
0.0
Bleomycin (50 uM)
AP-24534 Docetaxel
AT-7519 BNG712
BIX02189
BMS345541
1-BET-762
KIN001-102
KIN001-236
KIN001-260
LAQ824
Masitinib
Methotrexate
Nilotinib
NPK76-11-72-
OSI-930
PHA-793887
Phenformin
PIK-93
TAK~715
THZ-2-102-
THZ-2-49
IL-1-85
TL-2-105
Tubastatin
VX-11e
WZ3105
alvocidib
bardoxolone methyl
B1-2536
BMS-345541 BRD-K61166597
NG-25
TPCA-
BRD-K97651142
brivanib
CD-437
clofarabine
COL-3
cytarabine hydrochloride
dinaciclib
docetaxel
gemcitabine
GSK461364
GW-405833
KU-60019
KX2-391
linifanib
LY-2183240
methotrexate
nakiterplosin
NVP-231
oligomycin A
phloretin
SB-743921
SCH-79797
triazolothiadiazine
valdecoxib
vincristine
Drug
Drugs
Discussion
The present study focused on elucidating the function of DHX34 in pan-cancer through a bioinformatics approach. Initially, we examined the expression levels of DHX34 across various human organs and tissues. Subsequently, a comparison was made between the mRNA and protein expression levels of DHX34 in tumor tissues versus those in normal tissues. Additionally, we delved into the prognostic and diagnostic significance of DHX34 in diverse cancer forms. Furthermore, we explored the genetic variants of DHX34. Then, we detected the correlation of DHX34 expression with both TMB and MSI in pan-cancer. To further understand the functional annotation of DHX34, we constructed PPI and GSEA networks. Moreover, we examined the interplay between DHX34 expression levels and TIME in pan-cancer. Finally, we explored the relationship between DHX34 expression and the sensitivity of cancers to immune or targeted therapies. This comprehensive study provides insights into the role of DHX34 as a therapeutic target in pan-cancer.
TCGA data analysis showed that DHX34 was found in BLCA, BRCA, CHOL, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, STAD, THCA, UCEC were highly expressed in these tumors, In the experimental validation part of this
paper, we collected clinical samples from four tumors, COAD, LIHC, LUAD, and STAD, and verified the expression of DHX34 in these tumors using PCR and Western bolt, which showed that DHX34 was highly expressed in these tumors. We chose these four tumors for experimental validation for the following reasons: These four tumors are of great significance due to their high incidence and mortality rates globally; meanwhile, our institution has relatively abundant clinical sample resources for these cancer types, which facilitates the conduct of high-quality validation experiments. In addition, the high expression results of these cancer types in TCGA data are particularly significant, suggesting that DHX34 may play an important role in their development, which is worthy of further clinical validation; Considering the depth and breadth of the study and the limited resources, it is reasonable to selectively focus on those cancer types with the most significant high expression trend and prognostic value; Although the current study focused on four cancer types, COAD, LIHC, LUAD, and STAD, we also recognize that DHX34 may be equally important in other tumor types. Future studies will consider expanding the validation scope to include BRCA, CHOL PRAD, etc., to comprehensively assess the expression pattern and potential function of DHX34 in a wide range of tumors.
A
DHX34 G1:Low; G2:High
Group
G1
G2
CDKNI A
CISD1
EMC2
FANCDŻ
FDFTI
GPX4
HSPAS
HSPB1
MTIG
NFEIL2
SAT1
SLC1A5
SLC7A11
n$
.
…
.
…
…
…
..
…
…
15
Ferroptosis Genes Expression
10
5
0
Q
3
0
0
0
2
0
3
0
3
0
&
0
2
0
2
0
2
0
3
3
0
0
3
3
2
Group
G1
G2
ACSLA
ALOXI 5
ATLI
ATP5MC3
CARSI
CS
DPP4
GLS2
LPCAT3
NCOA4
RPL8
TFRC
15
ns
*
…
ns
…
ns
…
Ferroptosis Genes Expression
10
5
0
G
2
0
0
0
3
3
Q
0
3
G
3
3
Q
0
2
0
Q
6
2
3
2
0
8
B
DHX34 G1:Low; G2:High
Group
G1
G2
CELL1
METTL14
METTL16
METTL3
RBMIS
RBM15B
VIRMA
WTAP
YTHDCI
YTHDC2
YTHDF3
ZC3H13
…
**
…
…
…
…
…
…
…
…
.
6
m6A Genes Expression
4-
2-
0
B
2
0
3
0
2
0
&
0
2
0
&
0
0
0
8
0
&
0
2
0
02
Group
G1
G2
ALKBH3
ALKBH5
EIF3A
FTO
HNRNPA2B1
HNRNPC
IGF2BP1
IGF2BP2
IGF2BP3
HEMX
YTHDFI
YTHDF2
ns
*
7.5
m6A Genes Expression
5.0
2.5
0.0
0
2
0
&
0
2
0
8
0
2
0
Q
0
2
0
8
0
8
0
Q
0
8
0
Q
A
Relative mRNA expression of
*
Colonic carcinoma Patients
5-
*
B
**
Relative expression of
Hepatocellular carcinoma Patients
*
5-
DHX34/GAPDH
Relative mRNA expression of
10-
1.5-
1
2
3
4
5
2
1
2
3
4
5
DHX34/GAPDH
A
3-
T
N
T
N
DHX34/GAPDH
Relative expression of
DHX34/GAPDH
8-
T
N
T
N
T
N
S
1.0-
T
N
T
N
T
N
T
N
T
N
Ø
1
DHX 34
NO
DHX 34
+
1
0.5-
GAPDH
GAPDH
2-
0
0
0.0
0
Tumor
Nomal
Tumor
Normal
Tumor
Nomal
Tumor
Normal
C
Relative mRNA expression of
.
Lung adenocarcinoma Patients
2.0-
2.0-
20-
DHX34/GAPDH
5
Relative expression of DHX 34 / GAPDH
D
Relative mRNA expression of
15-
*
Stomach adenocarcinoma Patients
Relative expression of DHX 34 / GAPDH
*
1
2
3
4
DHX34/GAPDH
1
2
3
4
5
15
1.5-
1.5-
T
N
T
N
T
N
T
N
T
N
10-
T
N
T
N
.0
T
N
T
N
T
N
O
1.0-
DHX 34
i
5-
.5
En
DHX 34
0.5-
GAPDH
0
0.0
0
GAPDH
0
Tumor
Normal
Tumor
Nomal
Tumor
Normal
Tumor
Nomal
E
Stage
Stage II
Stage III
F
*
8×104-
ns
*
IL
T
IOD of DHX34
6×104
4×104
2×104
0
Stage I
Stage II
Stage III
Pathologic stage
G
DHX34Low
DHX34High
Sample #1
Sample #2
Sample #3
Sample #4
DHX34
.-
CD68
H
100
DHX34
100
DHX34
Survival probability
80-
Low
High
Survival probability
80
--- Low
60-
+ High
60-
40-
40-
20
P=0.031
HR=0.41(0.21-0.81)
20
P=0.035
Overall Survival
HR=0.50(0.26-0.96)
0
Progress Free Interval
0
12
24
36
48
60
72
0
0
12
24
36
48
60
72
Time(months)
Time(months)
Alterations in the DHX34 gene were observed in approximately 5% of pan-cancer patients, with amplification representing the largest proportion of these changes. Additionally, an analysis of mutation frequencies of the DHX34 gene indicated that amplification emerges as the most prevalent type. Consistent with these findings, the analysis of data from TCGA and GEO databases revealed that the expression of DHX34 is significantly elevated in the majority of malignancies when compared to normal tissues. Given that the AUC of the ROC exceeded 0.7 in 17 malignancies and 0.9 in 7 cancers, increased DHX34 expression holds promise as a novel
diagnostic marker in clinical practice. To further assess the predictive significance of DHX34 in malignancies, we employed survival analysis and found that high DHX34 expression in ACC, LGG, MESO, and SARC is associated with poor OS. Similarly, high expression of DHX34 in ACC, LGG, HCC, MESO, and SARC is predictive of poor DSS. Additionally, high DHX34 expression in ACC, KIRP, LGG, HCC, and SKCM is indicative of poor PFI.
The correlation analysis, logistic regression analysis, and subgroup analysis revealed a significant association between DHX34 and multiple clinical-pathological factors associated with cancers.
Crucially, our qRT-PCR, WB, and IHC experiments confirmed that both the mRNA and protein expression of DHX34 are elevated in tumor tissues compared to normal tissues. Especially in LIHC, a high expression of DHX34 coincides with a high expression of CD68, suggesting an enhanced macrophage infiltration. However, interestingly, the TISIDB database reveals a negative correlation between DHX34 expression and monocyte expression in LIHC. This discrepancy can be attributed to two primary factors. Firstly, our clinical sample size was limited, necessitating an expansion of the sample pool for further validation. Secondly, the diverse algorithms employed for tumor immune infiltration analysis may yield varying analytical outcomes.
TMB and MSI are dependable indicators of prognosis and immunotherapeutic impact in several tumors [34, 35]. Studies have demonstrated a heightened response to immunotherapy in tumors exhibiting high levels of both TMB and MSI [36,37]. Consistent with these findings, our analysis revealed a positive association between DHX34 expression and both TMB and MSI in some tumor types. We, therefore, hypothesize that cancer patients with high DHX34 expression would experience improved survival following immunotherapy. This result underscores the potential of DHX34 as a novel therapeutic target for immunotherapy in cancer treatment.
To gain more insight into the biological role of DHX34, a PPI network was constructed. This analysis identified ten hub genes, and we subsequently explored their relationship with DHX34 expression across various cancer types. These hub genes exhibited a positive correlation with DHX34 expression, suggesting their comparable involvement in cancer biology. Among these hub genes, Cell Division Cycle 40 Homolog (CDC40) plays a pivotal role in enhancing cell cycle progression, cell proliferation, and migration in LIHC [38]. Cell Division Cycle 5-Like (CDC5L), a regulator of the G2/M transition in the cell cycle, has demonstrated potential oncogenic activity in colorectal tumors, bladder cancer, cervical tumors, and osteosarcoma [39-42]. Furthermore, upregulated Pre-mRNA Processing Factor 19 (PRPF19) expression is associated with poorer outcomes in tongue cancer patients [43]. UPF1 modulates TOP2A activity and maintains stemness in colorectal cancer, thereby increasing chemoresistance to oxaliplatin [44]. Additionally, UPF3a may contribute to the aggressive nature and unfavorable prognosis of colorectal cancer [45]. More importantly, utilizing the GSCALite tool, we discovered that DHX34 and their ten hub genes may promote LIHC progression through the
regulation of the cell cycle.
Our GSEA results further revealed that DHX34 positively correlates with the processes of cell cycle and mitosis, encompassing chromosome organization and sister chromatid segregation. Previous studies have demonstrated that abnormally expressed cancer-related genes can foster cancer development by accelerating the cell cycle. For instance, ERCC6L enhances the malignancy of breast cancer and promotes the development of mammary neoplasia by speeding up the cell cycle [46]. Similarly, in gastric cancer, HER2 fuels tumor growth by regulating cell mitotic progression through the Shc1-SHCBP1-PLK1- MISP pathway [47]. Our findings suggest that the high expression of DHX34 genes in tumor cells may promote mitosis and expedite the cell cycle, thereby contributing to accelerated tumor growth. Furthermore, DHX34 positively correlated with the process of gene transcription regulation, which involves sequence-specific DNA binding, transcription regulator activity, and DNA-binding transcription factor activity, which aligns with previous research on DHX9. It was shown that DHX9 supports NF-KB-mediated transcriptional activity by increasing p65 phosphorylation and nuclear translocation, another, DHX9 interacts with p65 and RNA polymerase II to bolster the expression of NF-KB’s downstream targets, such as Snail and Survivin, thus intensifying the cancerous characteristics of colorectal cancer [48]. Another study revealed that HIF1A-As2 epigenetically activates MYC by attracting DHX9 to the MYC promoter, thereby promoting the transcription of MYC and its target genes in KRAS-driven non-small cell lung cancer [49]. Our findings collectively suggest that DHX34, through its involvement in cell cycle regulation and gene transcription, may play a pivotal role in tumor development and progression.
The TIME plays a crucial role in tumor progression and immunotherapy, as evidenced by an increasing number of studies [50,51]. Utilizing the TISIDB database, we observed a negative correlation between the expression of DHX34 and T cells. T cells, which occupy a pivotal position in the immune system, are responsible for recognizing and eliminating tumor cells. However, as the expression of DHX34 intensifies, it appears to suppress the function or quantity of T cells. Consequently, this suppression enables tumor cells to evade immune attack, ultimately contributing to tumor growth and dissemination. Additionally, our analysis of the scRNA-seq TISCH2 database revealed that the expression level of the DHX34 gene is highest in monocytes or macrophages in LIHC. we, therefore, hypothesize that DHX34 may enhance the function or
quantity of tumor-associated macrophages, further exacerbating the growth and aggressiveness of the tumor.
Cancer patients exhibiting elevated TIDE scores are predisposed to tumor immune escape, leading to a decreased response rate to immunotherapy with ICI [52,53]. Notably, a correlation was observed between DHX34 expression and TIDE score in KIRP, LGG, LIHC, and SKCM, suggesting that DHX34 could serve as a predictor for ICI therapy responsiveness. Furthermore, our research has uncovered an association between DHX34 overexpression and reduced sensitivity of cancer cells to multiple anticancer drugs, which provides a compelling rationale that DHX34 may act as a target for cancer-specific chemotherapeutic agents.
While DHX34’s impact on pan-cancer was discussed, it is important to acknowledge that we did not look into the molecular mechanism of DHX34 in malignancies in our study. In the future, more research on the mechanism of DHX34 in malignancies will be required. In summary, our investigation clarified the function of DHX34 in pan-cancer from several perspectives, including its relationship to mutational status, TMB, MSI, diagnosis, prognosis, clinical features, PPI, GESA, TIME, TIDE, and drug sensitivity, suggesting that it may be a viable diagnostic and prognostic marker for a variety of malignancies.
Acknowledgements
We thank Shemin Lu, Guangyao Kong, Pengfei Liu, Jing Geng, Junan Qi, Na Huang, and Chongyu Zhang for their contributions to this research.
Funding
The study was supported by National Natural Science Foundation of China (No: 82173207).
Ethics statement
The Second Affiliated Hospital of Xi’an Jiaotong University approved this study. Human tissue was used in strict accordance with the guidelines of the Declaration of Helsinki, and the patients provided written informed consent to participate in this study.
Author contributions
H.T. and Z.L. designed this research, P.Z. and G.X. collected the raw data, Z.L, J.Y and T.C. was responsible for the data analyses, T.L. made revisions for manuscript draft, N.L. and Q.W. conducted experiments and bioinformatics analysis and wrote the first draft of this manuscript. All authors have read and agreed to the published version of the manuscript.
Competing Interests
The authors have declared that no competing interest exists.
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