RESEARCH
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Pan-cancer analysis from multi-omics data reveals AAMP as an unfavourable prognostic marker
Yang Wang1+, Ting Liu2+, Ke Zhang1, Rong-hai Huang1 and Li Jiang1*
Abstract
Objectives Angio-associated migratory cell protein (AAMP) is a protein that participates in cell migration and is reported to be involved in cancer progression. However, the molecular mechanism of AAMP in pan-cancer is not known.
Methods We used multi-omics data, such as TIMER, TCGA, GTEx, CPTAC, HPA, and cBioPortal to analyze AAMP expres- sion, and gene alteration in pan-cancer. Univariate Cox regression and Kaplan-Meier were utilized to explore prognos- tic significance of AAMP expression level. We applied Spearman analysis to investigate the correlation between AAMP and TMB, MSI, immune cell infiltration, immune checkpoints. Moreover, we mainly studied liver hepatocellu- lar carcinoma(LIHC) to explore AAMP expression, clinical significance, and prognosis. Cox regression analysis was used to study independent factor to predict prognosis for AAMP in LIHC. GSEA was utilized to investigate the biological function for AAMP in LIHC.
Results AAMP was overexpressed in most cancers, and high AAMP expression was associated with worse overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI) for LIHC and adrenocortical carcinoma (ACC). Moreover, AAMP had the highest mutation frequency in uterine corpus endometrial carcinoma (UCEC). AAMP was correlated with TMB and MSI in esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), lung squamous cell carcinoma (LUSC), and thyroid carcinoma (THCA). Then, we focus on LIHC to investigate the expression and prog- nosis of AAMP. AAMP overexpression was related to histological grade and pathological stage in LIHC. Multivariate Cox regression analysis revealed that AAMP overexpression was an independent adverse prognostic marker for LIHC. AAMP expression was correlated with immune cell infiltration and immune checkpoints in LIHC. Function enrichment analysis indicated the participation of AAMP in the cell cycle and DNA replication.
Conclusions AAMP was a latent prognostic indicator for pan-cancer and had high potential as a biomarker for the diagnosis and prognosis of LIHC.
Keywords AAMP, Pan-cancer, Prognostic, Multi-omics analysis
+Yang Wang and Ting Liu have contributed equally to this work.
*Correspondence:
Li Jiang jiangli1903@163.com
Full list of author information is available at the end of the article
☒ BMC
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Introduction
Cancer is a major cause of death affecting human health worldwide. There are an estimated 19.3 million new can- cer cases and almost 10.0 million cancer deaths occurred in 2020 globally [1]. Since the occurrence and develop- ment of tumors may be accompanied by different gene alterations, it is an important problem to find biomarkers for the diagnosis of different tumors. Pan-cancer research is to analyze multiple aspects of a large number of tumors and examine the differences in genes in different tumor types, so as to have a fuller understanding of tumors and find therapeutic and diagnostic targets for a variety of tumors.
Angio-associated migratory cell protein (AAMP) was first discovered by Beckner when they screened cell sur- face proteins related to cell motility in melanoma cells [2]. It belonged to the immunoglobulin superfamily and had homologous domains with cell adhesion molecule proteins NCAM, LFA-2, PECAM, etc. The structural characteristics of AAMP protein suggested that it may be involved in cell adhesion and migration. Subsequently, it was found that AAMP was involved in cancer occurrence and development. For example, AAMP could accelerate the adhesion and proliferation of breast cancer cells, and high AAMP expression had a worse prognosis for breast cancer patients [3, 4]. AAMP interacted with EGFR to enhance the proliferation and drug resistance of non- small cell lung cancer cells [5]. The interaction between AAMP and CDC42 could accelerate non-small cell lung cancer cells’ metastasis [6]. However, there are few stud- ies on the AAMP gene expression pattern and latent function in pan-cancer.
To study the latency effect of AAMP in pan-cancer, we analyzed the transcription level of AAMP and its rela- tionship with clinical pathology from multiple public databases. Then, we conducted bioinformatics analysis to investigate the biological function and prognostic signifi- cance of AAMP in pan-cancer.
Materials and methods
Expression of AAMP in pan-cancer
Tumor Immune Estimation Resource (TIMER) is an online platform for analyzing immune cell infiltration in tumors and gene expression differences between tumors and normal tissues in the TCGA database [7]. We ana- lyzed AAMP expression in pan-cancer from TIMER. Because there were no normal tissues in some tumors from TIMER, so we downloaded the expression pro- file data of 33 tumors in TCGA and GTEx to compare AAMP gene expression in pan-cancer. The Wilcoxon test was investigated the difference in AAMP gene expression between tumors and normal tissues. AAMP expression in tumors and its matched normal tissues was studied
by paired sample T test. AAMP expression at the pro- tein level was analyzed using the CPTAC data set from the UALCAN and immunohistochemical image analysis from the HPA database. In addition, the ROC curve was used to estimate the diagnostic significance of AAMP using the pROC package in R. Then, we downloaded RNA sequence and clinical information for liver hepa- tocellular carcinoma(LIHC) from LIRI-JP in the ICGC database to verify the expression and prognosis of AAMP.
Prognosis analysis in pan-cancer
According to the median value of AAMP, we separated AAMP expression into AAMP high-expression and low- expression groups. Univariate regressive analysis was used to explore the impact of AAMP on the prognosis of 33 kinds of tumors using a survival package and for- est plots for visualization. The prognostic indicators included overall survival (OS), disease-specific survival (DSS), and progress-free interval (PFI).
Gene mutation, TMB, and MSI analysis in pan-cancer
Gene mutation is a common mode of epigenetics. cBio- Portal is an online website for studying gene mutation analysis in tumors [8, 9]. Gene alteration contains muta- tion, structural variant, amplification, and deep deletion. Moreover, tumor mutation burden (TMB) and micros- atellite instability (MSI) are two highly effective markers for tumor immunotherapy. Tumors with higher TMB can recruit more neoantigens on the surface of tumor cells, increase the immunogenicity of tumors, and improve the efficacy of immunotherapy [10]. Studies have shown that tumors with high MSI are highly sensitive to immune checkpoint inhibitor treatment [11]. Spearman cor- relation analysis was applied to discuss the correlation between AAMP expression and TMB, MSI.
Association of AAMP expression with clinicopathological characteristics in LIHC
The clinical information of LIHC patients was acquired from TCGA data, including age, sex, T stage, N stage, M stage, clinical stage, and other clinical features. Moreo- ver, the Chi-Square test was used to study the correlation between AAMP and clinical parameters.
Relationship of AAMP expression with the prognosis of LIHC and its nomogram
We used the Kaplan-Meier curve to analyze the effect of AAMP expression on OS, PFI, and DSS in LIHC. Moreo- ver, we explored the independent prognostic factors of AAMP in LIHC by univariate and multivariate regression analysis. The 1- year, 3- year, and 5- year survival rate of AAMP in LIHC was investigated using a time ROC
curve. A calibration curve was drawn to evaluate the pre- cision accuracy of the nomogram.
Immune cell infiltration and immune checkpoints analysis in LIHC
We used the ssGSEA algorithm in R packet-GSVA to discuss the correlation of AAMP with 24 kinds of immu- nocyte infiltration [12, 13]. Moreover, we used the ESTI- MATE package in R to estimate the stromal cells and immune cells in tumor tissue, predict tumor microenvi- ronment (TME) by immune and stromal scores, and ana- lyze the association of AAMP with stromal and immune score in LIHC [14]. The presence of immune checkpoint inhibitors has made significant progress in immune ther- apy. We analyzed the difference in expression of eight common immune checkpoints between the high and low AAMP expression groups to predict the effect of immu- notherapy. Tumor Immune Dysfunction and Exclusion (TIDE) algorithm is used to predict the response to can- cer immunotherapy. The efficacy of immune checkpoint blockade (ICB) is poorer with a higher TIDE score, and the survival time is shorter after receiving ICB treatment [15, 16]. We predicted immunotherapy response by ana- lyzing TIDE scores of high and low AAMP expression groups in LIHC.
Gene enrichment analysis in LIHC
The AAMP was classified into high- and low-expres- sion groups according to the median expression. We explored the DEGs between high- and low-expression groups in LIHC using the DESeq2 (version 1.26.0) pack- age. Gene Ontology (GO) enrichment and GSEA were performed to explore the relevant pathways involved in AAMP in LIHC using the “Cluster Profiler” package of R language. GO enrichment contains molecular function (MF), cellular component (CC), and biological process (BP). P<0.05, and FDR <0.25 are defined as significantly enriched.
Statistical analysis
Wilcoxon test was utilized to analyze the differen- tial expression of two groups. The chi-square test was explored the association between AAMP expression and clinical features. Univariate regression analysis and the Kaplan-Meier curve were used to investigate the effect of AAMP on prognosis. We predicted the impact of AAMP expression in LIHC on 1-, 3-, and 5-year’s OS by tim- eROC package. Spearman correlation analysis was used to research the correlation of AAMP expression with TME and immune checkpoints.
Results AAMP expression analysis and diagnostic value
in pan-cancer
TIMER results demonstrated that AAMP expression was highly expressed in BRCA, CHOL, ESCA, HNSC, LIHC, LUAD, LUSC, PAAD, PRAD, READ, and STAD while lowly expressed in KICH, KIRC, KIRP, and THCA (Fig. 1A). In TCGA, because some tumors lack normal samples, we combined TCGA with GTEx to analyze AAMP expression in tumors and normal tissues. We found that AAMP expression was overexpressed in 18 tumors, while AAMP was lowly expressed in KICH, LAML, OV, and STAD (Fig. 1B). The ROC curve was used to evaluate the diagnostic value of AAMP in pan- cancer. The results demonstrated that AAMP had a certain accuracy (AUC>0.7) in predicting 15 tumors, including BRCA, CHOL, COAD, ESCA, HNSC, KICH, LIHC, LUAD, LUSC, PAAD, READ, SARC, SKCM, STAD, and THYM.
Prognostic analysis in pan-cancer
We discussed the prognosis of patients with LIHC in the high- and low-expression groups of AAMP. The results of OS in pan-cancer demonstrated that the expression of AAMP correlated with the OS for LIHC, ACC, LAML, GBM, STAD, and KIRC. The high expres- sion of AAMP had poorer OS in LIHC, ACC, LAML, and GBM and better OS in STAD and KIRC (Fig. 2A). Regarding DSS, AAMP was a risk factor in KIRP, LIHC, ACC, and GBM and a protective factor in KIRC (Fig. 2B). In terms of PFI, overexpression AAMP in ACC, LUSC, KIRP, and LIHC had poor PFI (Fig. 2C). The highly expressed AAMP had inferior OS, DSS, and PFI in ACC and LIHC.
Gene mutation, TMB, and MSI analysis of AAMP in pan-cancer
cBioPortal was used to research AAMP gene altera- tion in pan-cancer. The highest frequency of gene alterations was found in UCEC(mutation 1.89%, amplification 1.32%, deep deletion 0.19%), followed by CESC (mutation 1.35%, deep deletion 2.02%) and SARC(amplification1.57%, deep deletion 1.18%) (Fig. 3A). The results of the TMB correlation analysis exhibited that AAMP expression had a positive corre- lation with TMB in ACC, ESCA, LUAD, STAD, LUSC, THYM, and PAAD while negative correlation in KIRP, THCA, and UCS (Fig. 3B). As for MSI, AAMP was positively related to MSI in ESCA, STAD, KIRC, LUSC, LIHC, and UVM, and negatively in THCA (Fig. 3C).
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AAMP Expression Level (log2 TPM)
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ACC.Tumor (n=79)
BLCA.Tumor (n=408)
BLCA.Normal (n=19)
BRCA.Tumor (n=1093)
BRCA.Normal (n=112)
BRCA-Basal.Tumor (n=190)
BRCA-Her2.Tumor (n=82)
BRCA-LumA.Tumor (n=564)
BRCA-LumB.Tumor (n=217)
CESC.Tumor (n=304)
CESC.Normal (n=3)
CHOL.Tumor (n=36)
CHOL.Normal (n=9)
COAD.Tumor (n=457)
COAD.Normal (n=41)
DLBC.Tumor (n=48)
ESCA.Tumor (n=184)
ESCA.Normal (n=11)
GBM.Tumor (n=153)
GBM.Normal (n=5)
HNSC.Tumor (n=520)
HNSC.Normal (n=44)
HNSC-HPV+.Tumor (n=97)
HNSC-HPV -. Tumor (n=421)
KICH.Tumor (n=66)
KICH.Normal (n=25)
KIRC.Tumor (n=533)
KIRC.Normal (n=72)
KIRP.Tumor (n=290)
KIRP.Normal (n=32)
LAML.Tumor (n=173)
LGG.Tumor (n=516)
LIHC.Tumor (n=371)
LIHC.Normal (n=50)
LUAD.Tumor (n=515)
LUAD.Normal (n=59)
LUSC.Tumor (n=501)
LUSC.Normal (n=51)
MESO.Tumor (n=87)
OV.Tumor (n=303)
PAAD.Tumor (n=178)
PAAD.Normal (n=4)
PCPG.Tumor (n=179)
PCPG.Normal (n=3)
PRAD.Tumor (n=497)
PRAD.Normal (n=52)
READ.Tumor (n=166)
READ.Normal (n=10)
SARC.Tumor (n=259)
SKCM.Tumor (n=103)
SKCM.Metastasis (n=368)
STAD.Tumor (n=415)
STAD.Normal (n=35)
TGCT.Tumor (n=150)
THCA.Tumor (n=501)
THCA.Normal (n=59)
THYM.Tumor (n=120)
UCEC.Tumor (n=545)
UCEC.Normal (n=35)
UCS.Tumor (n=57)
UVM.Tumor (n=80)
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The expression of AAMP Log2 (TPM+1)
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E Tumor
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ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
C
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
AAMP
0.2
AAMP
0.2
AAMP
0.2
AAMP
0.2
AAMP
BRCA
AUC: 0.711
CHOL
AUC: 1,000
COAD
AUC: 0.769
ESCA
AUC: 0.730
CI: 0.667-0.754
HNSC
AUC: 0.738
0.0
0.0
CI: 1.000-1.000
0.0
Cl: 0.719-0.820
0.0
CI: 0.569-0.890
0.0
CI: 0.676-0.799
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
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
AAMP
0.2
AAMP
0.2
AAMP
0.2
AAMP
0.2
AAMP
KICH
AUC: 0.988
LIHC
AUC: 0.836
LUAD
AUC: 0.748
CI: 0.708-0.788
LUSC
AUC: 0.875
CI: 0.844-0.906
PAAD
AUC: 0.818
0.0
CI: 0.971-1.000
0.0
CI: 0.795-0.878
0.0
0.0
0.0
CI: 0.711-0.926
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
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
AAMP
0.2
AAMP
0.2
AAMP
0.2
AAMP
0.2
AAMP
READ
AUC: 0.890
SARC
AUC: 0.738
SKCM
AUC: 0.824
STAD
AUC: 0.770
THYM
AUC: 0.708
0.0
CI: 0.803-0.978
0.0
Cl: 0.539-0.937
0.0
CI: NA-NA
0.0
Cl: 0.689-0.850
0.0
Cl: 0.500-0.917
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
| OS | |||
|---|---|---|---|
| Cancer Type | HR(95%CI) | P | |
| LIHC | 1.99(1.38,2.87) | 0.00023 | |
| ACC | 2.44(1.24,4.80) | 0.01 | |
| LAML | 1.80(1.09,2.97) | 0.02 | |
| GBM | 1.83(1.10,3.05) | 0.02 | |
| LUAD | 1.33(0.99,1.80) | 0.06 | |
| KICH | 3.62(1.01,12.98) | 0.06 | |
| SKCM | 1.27(0.95,1.72) | 0.06 | |
| KIRP | 1.63(0.85,3.14) | 0.15 | |
| THYM | 3.59(0.37,34.39) | 0.27 | |
| PRAD | 3.20(0.39,26.48) | 0.29 | |
| COAD | 1.31(0.77,2.24) | 0.33 | |
| THCA | 1.91(0.50,7.32) | 0.35 | |
| CHOL | 1.79(0.44,7.34) | 0.42 | |
| OV | 1.10(0.86,1.40) | 0.46 | |
| PAAD | 1.15(0.74,1.78) | 0.54 | |
| ESCA | 1.11(0.68,1.80) | 0.67 | |
| UVM | 1.20(0.49,2.98) | 0.69 | |
| LUSC | 1.05(0.81,1.37) | 0.7 | |
| BLCA | 1.04(0.76,1.40) | 0.82 | |
| BRCA | 1.04(0.74,1.45) | 0.83 | |
| HNSC | 1.01(0.75,1.37) | 0.92 | |
| STAD | 0.71(0.54,0.93) | 0.01 | |
| KIRC | 0.73(0.57,0.93) | 0.01 | |
| MESO | 0.58(0.31,1.08) | 0.08 | |
| DLBC | 0.14(0.01,1.55) | 0.12 | |
| TGCT | 0.51(0.13,2.04) | 0.33 | |
| SARC | 0.81(0.51,1.27) | 0.35 | |
| READ | 0.60(0.19,1.93) | 0.4 | |
| LGG | 0.78(0.40,1.52) | 0.46 | |
| CESC | 0.85(0.54,1.36) | 0.5 | |
| PCPG | 0.79(0.12,4.99) | 0.8 | |
| UCS | 0.92(0.39,2.19) | 0.85 | |
| UCEC | 0.97(0.66,1.44) | 0.89 | |
B
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| DSS | ||
|---|---|---|
| Cancer Type | HR(95%CI) | P |
| KIRP | 4.35(1.76,10.74) | 0.0016 |
| LIHC | 2.00(1.26,3.16) | 0.0033 |
| ACC | 2.38(1.19,4.78) | 0.02 |
| GBM | 1.78(1.04,3.04) | 0.04 |
| PRAD | 22.20(0.81,608.61) | 0.08 |
| KICH | 3.66(0.84,15.97) | 0.09 |
| LUAD | 1.36(0.94,1.96) | 0.11 |
| LUSC | 1.36(0.90,2.06) | 0.14 |
| COAD | 1.91(0.81,4.51) | 0.15 |
| SKCM | 1.25(0.92,1.72) | 0.16 |
| HNSC | 1.15(0.78,1.70) | 0.48 |
| THYM | 3.10(0.13,73.85) | 0.49 |
| UVM | 1.37(0.50,3.72) | 0.54 |
| PAAD | 1.14(0.68,1.90) | 0.61 |
| CHOL | 1.44(0.34,6.13) | 0.62 |
| BLCA | 1.04(0.72,1.50) | 0.83 |
| THCA | 1.21(0.19,7.78) | 0.84 |
| READ | 1.20(0.09,15.61) | 0.89 |
| OV | 1.02(0.78,1.32) | 0.9 |
| UCS | 1.06(0.43,2.62) | 0.9 |
| ESCA | 1.01(0.56,1.82) | 0.98 |
| KIRC | 0.68(0.51,0.91) | 0.01 |
| DLBC | 0.07(2.4E-3,2.00) | 0.14 |
| STAD | 0.80(0.57,1.14) | 0.22 |
| SARC | 0.77(0.47,1.27) | 0.31 |
| MESO | 0.73(0.34,1.56) | 0.41 |
| TGCT | 0.55(0.12,2.44) | 0.42 |
| LGG | 0.76(0.38,1.53) | 0.44 |
| CESC | 0.87(0.52,1.47) | 0.61 |
| UCEC | 0.91(0.58,1.42) | 0.68 |
| BRCA | 0.93(0.60,1.45) | 0.76 |
| PCPG | 0.71(0.07,6.84) | 0.76 |
C
| PFI | |||
|---|---|---|---|
| Cancer Type | HR(95%CI) | P | |
| ACC | 4.60(2.26,9.38) | 4.50-05 | |
| LUSC | 1.54(1.11,2.14) | 0.01 | |
| KIRP | 2.11(1.15,3.89) | 0.02 | |
| LIHC | 1.42(1.05,1.91) | 0.02 | |
| UVM | 2.13(0.92,4.89) | 0.08 | |
| HNSC | 1.31(0.94,1.82) | 0.11 | |
| PRAD | 1.65(0.88,3.08) | 0.12 | |
| KICH | 2.51(0.79,7.95) | 0.12 | |
| CESC | 1.37(0.86,2.19) | 0.18 | |
| COAD | 1.29(0.82,2.04) | 0.27 | |
| SKCM | 1.11(0.87,1.42) | 0.4 | |
| LUAD | 1.09(0.84,1.42) | 0.51 | |
| TGCT | 1.21(0.58,2.55) | 0.61 | |
| READ | 1.40(0.37,5.30) | 0.62 | |
| CHOL | 1.38(0.36,5.25) | 0.64 | |
| PCPG | 1.25(0.41,3.82) | 0.69 | |
| GBM | 1.08(0.69,1.69) | 0.73 | |
| PAAD | 1.01(0.66,1.55) | 0.97 | |
| ESCA | 1.00(0.63,1.59) | 0.99 | |
| STAD | 0.79(0.60,1.06) | 0.12 | |
| OV | 0.87(0.70,1.08) | 0.21 | |
| LGG | 0.73(0.44,1.23) | 0.24 | |
| BRCA | 0.85(0.62,1.16) | 0.31 | |
| UCS | 0.65(0.27,1.54) | 0.32 | |
| KIRC | 0.89(0.68,1.16) | 0.38 | |
| MESO | 0.76(0.36,1.62) | 0.48 | |
| THYM | 0.75(0.31,5.72) | 0.52 | |
| DLBC | 0.61(0.06,5.72) | 0.66 | |
| SARC | 0.92(0.61,1.37) | 0.67 | |
| THCA | 0.96(0.50,1.84) | 0.89 | |
| BLCA | 0.99(0.74,1.33) | 0.94 | |
| UCEC | 1.00(0.71,1.41) | 0.99 | |
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Fig. 2 Prognosis of AAMP in TCGA for pan-cancer. A Overall survival (OS) of AAMP. B Disease-specific survival (DSS)of AAMP. C Progress-free interval (PFI) of AAMP
Expression of AAMP and the relationship between AAMP expression and clinical pathology in LIHC
AAMP had higher expression in LIHC than in matched normal tissues (Fig. 4A). Furthermore, the CPTAC data set in the UALCAN database revealed that AAMP pro- tein expression had over-expression in LIHC (Fig. 4B). The immunohistochemical staining also confirmed the increased expression of AAMP in liver cancer (Fig. 4C).
Meanwhile, we acquired the clinical characteristics of LIHC from the TCGA database, and our results revealed that AAMP expression was related to histo- logical grade and pathological stage of LIHC. The high expression rate of AAMP (G3+G4,80/136) in LIHC with a high histological grade was higher than that with a low histological grade (G1+G2,105/233). The increased expression rate of AAMP (55/90) in advanced HCC (Stage III+IV) was higher than that in early LIHC (Stage I+II) (120/260) (Table 1). These results
indicated that the expression of AAMP promoted LIHC progression.
Prognosis and nomogram of AAMP in LIHC
Kaplan-Meier survival curve also revealed that in LIHC, AAMP in the higher expression group had shorter OS, DSS, and PFI than in the lower expression group (Fig. 5A,B,C). Moreover, 1-, 3-, and 5-year OS was 0.701, 0.657, and 0.674, respectively (Fig. 5D). The univari- ate and multivariate analysis results considered AAMP expression as an independent prognostic marker in LIHC (Fig. 5E). A nomogram was constructed to predict the 1, 3, and 5 year OS for LIHC (Fig. 5F).
Verify the expression and prognosis of AAMP in LIHC from ICGC
To verify the expression and prognosis of AAMP in LIHC, we acquired AAMP gene expression from ICGC.
A
3%-
Mutation
Structural Variant
Amplification
Deep Deletion
Alteration Frequency
2%
1%-
Structural variant data
Mutation data
CNA data
Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlar
Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Sarcoma (TCGA, PanCancer Atlas)
Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)
Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)
Head and Neck Squamous Cell Carcinoma (TCGA, PanCancer /
Stomach Adenocarcinoma (TCGA, PanCancer Atlas)
Uveal Melanoma (TCGA, PanCancer Atlas)
Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Esophageal Adenocarcinoma (TCGA, PanCancer Atlas)
Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)
Liver Hepatocellular Carcinoma (TCGA, PanCancer Atlas)
Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas’ Kidney Renal Papillary Cell Carcinoma (TCGA, PanCancer Atla
Lung Adenocarcinoma (TCGA, PanCancer Atlas)
Breast Invasive Carcinoma (TCGA, PanCancer Atlas) Prostate Adenocarcinoma (TCGA, PanCancer Atlas)
Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)
Glioblastoma Multiforme (TCGA, PanCancer Atlas)
Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)
Brain Lower Grade Glioma (TCGA, PanCancer Atlas)
Thymoma (TCGA, PanCancer Atlas)
Mesothelioma (TCGA, PanCancer Atlas)
Thyroid Carcinoma (TCGA, PanCancer Atlas)
Diffuse Large B-Cell Lymphoma (TCGA, PanCancer Atlas)
Cholangiocarcinoma (TCGA, PanCancer Atlas)
Uterine Carcinosarcoma (TCGA, PanCancer Atlas)
Kidney Chromophobe (TCGA, PanCancer Atlas)
Adrenocortical Carcinoma (TCGA, PanCancer Atlas) Acute Myeloid Leukemia (TCGA, PanCancer Atlas)
Pheochromocytoma and Paraganglioma (TCGA, PanCancer At
Testicular Germ Cell Tumors (TCGA, PanCancer Atlas)
B
C
ACC
* UCS
UVM
BLCA
** UVM
ACC
BRCA
BLCA
BRCA
UCEC
0.4
UCS
CESC
UCEC
0.4
CESC
*** THYM
0.2
CHOL
THYM
0.3
CHOL
THCA
COAD
*THCA
0.2
COAD
0
TCT
0.1
TGCT
DLBC
0
DLBC
*** STAD
-0.2
ESCA*
*** STAD
-0.1
ESCA*
SKCM
-0.4
GBM
SKCM
-0.2
GBM
SARC
HNSC
SARC
HNSC
READ
KICH
READ
KICH
PRAD
KIRC
PRAD
KIRC ***
PCPG
KIRP*
PCPG
KIRP
*** PAAD
LAML
PAAD
LAML
OV
LGG
OV
MESQUSC*
*LUADIHC
MESQUSC
LGG
LUABIHC ***
TMB
MSI
We found that AAMP had higher expression in LIHC than in normal liver tissue, and AAMP overexpres- sion had an adverse outcome in LIHC (Fig. 6A, B). These results were consistent with the TCGA database.
The time ROC curve showed that the OS for 1-, 2- and 3-years was 0.662, 0.737, and 0.726, respectively (Fig. 6C). These findings showed that AAMP had an excellent prediction ability in TCGA and ICGC.
A
7.5
P<0.001
The expression of AAMP Log2 (TPM+1)
7.0
6.5
6.0
5.5
5.0
T
T
Normal
Tumor
TCGA database
B
Protein expression of AAMP in Hepatocellular carcinoma
3
2
1
Z-value
0
-1
-2
P<0.05
-3
Normal (n=165)
Primary tumor (n=165)
CPTAC samples
C
Normal Liver
Liver cancer
Relevance analysis of AAMP with immunocyte infiltration and immune checkpoints in LIHC
The ssGSEA algorithm showed that AAMP in LIHC was positively correlated with T helper cells, Th2 cells, and Tcm, and negatively linked with DC, Cytotoxic cells, pDC, neutrophils, B cells, Th17 cells, Treg, mast cells, eosinophils, iDC, Th1 cells, Tgd, T cells, NK CD56 dim cells. Among them, AAMP had the highest positive cor- relation coefficient with T helper cells and the highest negative correlation coefficient with DC (Fig. 7A). The results of the ESTIMATE algorithm demonstrated that the stromal score, immune score, and estimate score of
AAMP in the high-expression group were lower than in the low-expression group, which showed that AAMP influenced cancer development through immune cell infiltration (Fig. 7B). Immune checkpoints are closely linked with tumor proliferation, invasion, metastasis, and prognosis of patients, so they are good targets for tumor treatment. The immune checkpoints can activate the intracellular signal pathway to promote the immune response and the escape of tumor cells [17, 18]. The expression of CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT in the high AAMP expression was higher than in the low AAMP expression group, which showed
| Characteristics | Low expression of AAMP | High expression P value of AAMP | |
|---|---|---|---|
| n | 187 | 187 | |
| Age | 0.570 | ||
| <= 60 | 86 | 91 | |
| >60 | 101 | 95 | |
| Gender | 0.740 | ||
| Female | 59 | 62 | |
| Male | 128 | 125 | |
| Pathologic T stage | 0.052 | ||
| T1&T2 | 146 | 132 | |
| T3&T4 | 38 | 55 | |
| Pathologic N stage | 0.693 | ||
| N0 | 121 | 133 | |
| N1 | 1 | 3 | |
| Pathologic M stage | 1.000 | ||
| M0 | 133 | 135 | |
| M1 | 2 | 2 | |
| Histologic grade | 0.011* | ||
| G1&G2 | 128 | 105 | |
| G4&G3 | 56 | 80 | |
| Pathologic stage | 0.014* | ||
| Stage I&Stage II | 140 | 120 | |
| Stage III&Stage IV | 35 | 55 | |
*P < 0.05
that AAMP expression was critical immunotherapeutic targets in LIHC (Fig. 7C). In our study, AAMP in high expression had a higher TIDE score and a lower response rate to immunotherapy (Fig. 7D).
Gene set enrichment analysis
We conducted an enrichment analysis to elucidate the biological mechanism of AAMP involvement in LIHC. The BP of GO enrichment showed that AAMP was involved in regulation of hormone levels, regulation of membrane potential, stress response to copper ion, and detoxification of copper ion. The results of CC in GO suggested that AAMP participated in collagen-contain- ing extracellular matrix, synaptic membrane, postsyn- aptic membrane, and blood microparticle. As for ME, AAMP mainly focused on passive transmembrane trans- porter activity, channel activity, ligand-gated channel activity, and ligand-gated ion channel activity (Fig. 8A). KEGG database from GSEA enrichment showed that high expression of AAMP participated in the cell cycle, DNA replication, axon guidance, neuroactive ligand- receptor interaction, gap junction, ECM receptor inter- action, and mismatch repair in TCGA database (Fig. 8B).
Furthermore, ribosome, DNA replication, spliceosome, cell cycle, mismatch repair, oxidative phosphorylation, and proteasome were enriched in the high AAMP expres- sion in ICGC database (Fig. 8C). In short, in the two gene sets, the highly expressed AAMP was mainly enriched in cell cycle, DNA replication, and mismatch repair, which participated in the progress of LIHC.
Discussion
AAMP is a member of the immunoglobulin superfam- ily and is widely distributed in various types of cells. It is essential in transcriptional activation, cell cycle regu- lation, protein-protein interaction, and signal trans- duction [19-21]. AAMP is expressed in a variety of cell types, including endothelial cells, aortic smooth muscle cells, dermal fibroblasts, renal proximal tubular cells, glomerular mesangial cells, human breast cancer cells, human melanoma cells and prostate cancer cells [22-24]. Recent studies have shown that AAMP mainly locates in the cytoplasm and membrane of vascular endothelial cells, affecting the angiogenesis, diffusion, migration, and cytoskeleton remodeling processes of endothe- lial cells [25, 26]. In addition, it has been reported that AAMP is abnormally up-regulated in metastatic CRC and boosts the occurrence of colorectal cancer by inhib- iting SMURF2-mediated RhoA liquefaction and degra- dation [27]. Furthermore, AAMP is highly expressed in invasive gastrointestinal and breast carcinoma cells and is a marker of poor prognosis [4, 28]. In addition, AAMP plays a positive role in angiogenesis and is regulated by Astrocytes in coculture [29]. These results showed that AAMP is vital in cancer occurrence and progression.
Our study explored AAMP expression, prognos- tic value, gene alteration, TMB, and MSI in pan-cancer through multiple databases. AAMP had high expression in 18 kinds of tumors, while AAMP had low expression in four tumors. The highly expressed AAMP had infe- rior OS, DSS, and PFI in ACC and LIHC. We used cBio- Portal to study AAMP gene alteration in pan-cancer. In UCEC, AAMP had the highest gene alteration frequency, including mutation 1.89%, amplification 1.32%, and deep deletion 0.19%. TMB and MSI are considered as two bio- markers of response to immunotherapy. Tumors with high TMB and high MSI are sensitive to immunother- apy response [30]. Therefore, we study the correlation between AAMP and TMB, MSI in pan-cancer. AAMP expression was positively correlated with TMB for ACC, ESCA, LUAD, STAD, LUSC, THYM, and PAAD while negatively correlated for KIRP, THCA, and UCS. Regard- ing MSI, AAMP had a positive relation to MSI in ESCA, STAD, KIRC, LUSC, LIHC, and UVM, and negative rela- tion in THCA. AAMP correlated with TMB and MSI
A
B
C
D
1.0
AAMP
1.0
AAMP
1.0
AAMP
1.00
Low
Low
Low
High
High
High
Survival probability
0.8
Survival probability
0.8
Survival probability
0.8
0.75
0.6
0.6
0.6
True positive fraction
0.50-
0.4
0.4
0.4
0.2
0.25
Overall Survival HR = 1.79 (1.26-2.53)
0.2
Disease Specific Survival HR = 1.82 (1.16-2.84)
0.2
Progress Free Interval HR = 1.44 (1.08-1.93)
Type
INean,AUC-4).701.95%C1(0.643-4.76)
0.0
P = 0.001
0.0
P = 0.009
0.0
P = 0.013
3Years,AUC-4.657,95%C1(0.592-4.723)
0.00
5-Years,AUC-4:674.95%C1(0.595-0.753)
0
2.5
5
7.5
10
0
2.5
5
7.5
10
0
2.5
5
7.5
10
0.00
0.25
False positive fraction
6.50
0.75
1.00
Time (years)
Time (years)
Time (years)
E
Multivariate Regression
1
2
3
0
5
10
15
F
Points
0
20
40
60
80
100
T stage
T3&T4
Pathologic stage
T1&T2
Stage I&Stage II
N stage
Stage III&Stage IV
N1
M stage
NO
M1
MO
AAMP
High
Total Points
Low
0
40
80
Linear Predictor
120
160
200
1-year Survival Probability
-0.6
-0.2
0.2
0.6
1
1.4
1.8
3-year Survival Probability
0.9
0.8
0.7 0.6 0.5 0.4 0.3
5-year Survival Probability
0.8
0.6
0.4
0.2
0.6
0.4
0.2
| Univariate Regression | |||
|---|---|---|---|
| Characteristics | Total(N) | HR(95% CI) | P value |
| Gender | 373 | ||
| Female | 121 | ||
| Male | 252 | 0.793 (0.557-1.130) | 0.2 |
| T stage | 370 | ||
| T1&T2 | 277 | ||
| T3&T4 | 93 | 2.598 (1.826-3.697) | <0.001 |
| Histologic grade | 368 | ||
| G1&G2 | 233 | ||
| G3&G4 | 135 | 1.091 (0.761-1.564) | 0.636 |
| N stage | 258 | ||
| NO | 254 | ||
| N1 | 4 | 2.029 (0.497-8.281) | 0.324 |
| Pathologic stage | 349 | ||
| Stage I&Stage II | 259 | ||
| Stage III&Stage IV | 90 | 2.504 (1.727-3.631) | <0.001 |
| AAMP | 373 | ||
| Low | 187 | ||
| High | 186 | 1.786 (1.259-2.533) | 0.001 |
| Characteristics | Total(N) | HR(95% CI) | P value |
|---|---|---|---|
| Gender | 373 | ||
| Female | 121 | ||
| Male | 252 | ||
| T stage | 370 | ||
| T1&T2 | 277 | ||
| T3&T4 | 93 | 1.988 (0.272-14.540) | 0.498 |
| Histologic grade | 368 | ||
| G1&G2 | 233 | ||
| G3&G4 | 135 | ||
| N stage | 258 | ||
| NO | 254 | ||
| N1 | 4 | ||
| Pathologic stage | 349 | ||
| Stage I&Stage II | 259 | ||
| Stage III&Stage IV | 90 | 1.202 (0.165-8.759) | 0.856 |
| AAMP | 373 | ||
| Low | 187 | ||
| High | 186 | 1.581 (1.088-2.297) | 0.016 |
A
B
C
6.5
1.0
Low
1.00
High
Expression of AAMP
6.0
Survival probability
0.8
0.75
5.5
0.6
True positive fraction
5.0
I
0.50
0.4
4.5
4.0
0.2
0.25
HR = 4.64 (2.22-9.69)
Type
3.5
0.0
P < 0.001
1-Years,AUC=0.662,95%CK(0.552-0.772)
2-Years,AUC=0.737,95%CK(0.661-0.812)
0.00
normal
tumor
0
1
2
3
4
5
3-Years,AUC=0.726,95%CK(0.641-0.811)
6
0.00
0.25
0.30
0.75
1.00
Time
False positive fraction
Fig. 6 Expression and prognostic analysis of AAMP in the ICGC database. A mRNA expression of AAMP in LIHC and normal tissues. B Kaplan-Meier survival curve of AAMP in ICGC. C ROC survival curve of 1-, 2-, and 3- year OS in ICGC.
A
AAMP
T helper cells Th2 cells
R = 0.227
C
R = 0.190
**
**
**
*
**
**
Tcm
R = 0.115
8
NK CD56bright cells
R = 0.046™s
Tem
R = - 0.015™s
aDC
R = - 0.033”s
6
TFH
CD8 T cells
R = - 0.038”s
R = - 0.048™s
P value
Immune checkpoint
Group
Macrophages
R =- 0.073™s
NK cells
R = - 0.079ns
0.6
GI
NK CD56dim cells
R = - 0.109
0.4
4.
G2
T cells
R = - 0.119
0.2
Tgd
Th1 cells
R = - 0.119
R = - 0.126
|Cor|
2
iDC
Eosinophils
R = - 0.133
R = - 0.154
0.1
Mast cells
R = - 0.168
0.2
TReg
0.3
0
Th17 cells
R = - 0.172
R = - 0.222
CD274
CTLA4
HAVCR2
LAG3
PDCDI
PDCDILG2
TIGIT
SIGLEC15
B cells
Neutrophils
R = - 0.256
R = - 0.273
pDC
Cytotoxic cells
R = - 0.310
R = - 0.310
DC
R = - 0.311
D
-0.3
-0.2
-0.1
0.0
0.1
0.2
True
71
Correlation
Responder
40
False
114
146
B
wilcox.tests p=0.0033
4000
..
**
2
Enrichment score
2000
1
AAMP
Low
TIDE score
High
0
0
G
D
-I
-2000
StromalScore ImmuneScore ESTIMATEScore
T
-2
GI
G2
Groups E3 G1 3 G2
A
B
TCGA
C
ICGC
regulation of hormone levels
[KEGG] DNA Replication
[KEGG] Ribosome
regulation of membrane potential
[KEGG] Mismatch Repair
[KEGG] DNA Replication
6
stress response to copper ion
.
[KEGG] Pentose Phosphate Pathway
[KEGG] Mismatch Repair
detoxification of copper ion
·
P adj
NES
NES
80-05
[KEGG] Proteasome
2.8
6e-05
[KEGG] Cell Cycle
2.6
collagen-containing extracellular matrix
40-05
1.8
2.4
2e-05
[KEGG] Axon Guidance
1.7
[KEGG] Spliceosome
2.2
synaptic membrane
1.6
2.0
8
Counts
postsynaptic membrane
[KEGG] Ecm Receptor Interaction
1.5
[KEGG] Oxidative Phosphorylation
10
Set size O
20
Set size
50
blood microparticle
30
[KEGG] Gap Junction
100
[KEGG] Cell Cycle
100
40
200
150
C
50
passive transmembrane transporter activity
[KEGG] Small Cell Lung Cancer
[KEGG] Nucleotide Excision Repair
60
channel activity
[KEGG] Neuroactive Ligand Receptor Interaction
[KEGG] Parkinsons Disease
4
ligand-gated channel activity
[KEGG] Wnt Signaling Pathway
[KEGG] Alzheimers Disease
ligand-gated ion channel activity
0.45 0.50 0.55 0.60
0.5
0.6
0.7
0.02
0.04
0.06
Enrichment score
Enrichment score
GeneRatio
in ESCA, STAD, and LUSC. It can be speculated that in ESCA, STAD, and LUSC, tumors with high expression of AAMP have better responses to immunotherapy.
We focused on LIHC after screening and discussed the AAMP expression, prognosis, clinical features, and immunity. AAMP was over-expressed at the mRNA expression level from the TCGA database and protein expression level from CPTAC. The immunohistochemi- cal staining in the HPA database also confirmed AAMP’s high expression in LIHC. Moreover, AAMP overexpres- sion was correlated with histological grade and patho- logical stage of LIHC. AAMP had higher expression in the higher histological grade and advanced pathological stage. Kaplan-Meier survival curve demonstrated that AAMP’s high expression was related to unfavorable OS, DSS, and PFI. The univariate and multivariate analysis results showed that AAMP was an independent adverse prognostic factor for LIHC patients. Furthermore, ROC curve analysis demonstrated that OS of 1, 3 and 5 years was more than 0.6. We also confirmed the high expres- sion of AAMP in LIHC in another different database. The results showed that 1, 2 and 3 year OS was 0.662, 0.737, and 0.726 by ROC curve analysis. These findings sug- gested that AAMP expression had an excellent prediction ability in TCGA and ICGC and could predict LIHC prog- nosis, supporting AAMP expression as a new predictor of survival for LIHC.
TME is essential in tumor development and is closely related to patient outcomes [31]. In recent years, increas- ing studies have confirmed that different TME of patients is vital in mediating late metastasis, immune escape, and immunosuppression. We used the ssGSEA algorithm to explore the association between AAMP and immune cell infiltration in LIHC. With the increase of AAMP expression, the number of DC, Cytotoxic cells, pDC, neutrophils, B cells, Th17 cells, Treg, mast cells, eosino- phils, iDC, Th1 cells, Tgd, T cells, NK CD56 dim cells decreased, and the number of T helper cells, Th2 cells, and Tcm increased. Therefore, changes in AAMP expres- sion lead to changes in Th1/Th2. The immunosuppres- sive state will affect the body’s anti-tumor immunity and ultimately result in tumors [32]. Immune and stromal cells, as the non-tumor components of TME, have gradu- ally attracted the attention of researchers due to their essential roles in tumor genesis, metastasis, drug resist- ance, and prognosis [33, 34]. We found that the stromal score and immune score of AAMP over-expression were lower than AAMP low-expression, indicating that AAMP affected the occurrence and development of LIHC through immunocyte infiltration. Recently, the treatment of advanced malignant patients has been revolutionized by the introduction of immune checkpoint inhibitors [35]. Moreover, common checkpoints included CD274,
CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, TIGIT, and SIGLEC15. The expression of CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT in the AAMP high- expression group was higher than AAMP low expression group, which showed that AAMP was the vital immuno- therapeutic target in LIHC. TIDE is considered an indi- cator of cancer’s immune intelligence response rate. The patients with higher TIDE scores have lower immune responses. In our study, the patients with increased expression of AAMP have low immune response rates and cannot benefit from immune checkpoint inhibitors. This may be because high AAMP expression induces a decrease in B and T cells, while low lymphocyte counts indicate a poor host anti-tumor immune response [36].
To further investigate the biological process of AAMP in LIHC, we analyzed GSEA in TCGA and ICGC data- bases. Interestingly, the highly expressed AAMP in the two databases is mainly concentrated in cell cycle, DNA replication, and mismatch repair, indicating that AAMP causes liver cancer through the above pathways.
We discussed the expression pattern and prognostic significance of AAMP in pan-cancer and LIHC from a bioinformatics perspective, providing a basis for further research on the mechanism of AAMP for LIHC. How- ever, our study had some limitations. We studied AAMP expression in pan-cancer and LIHC only by bioinfor- matics. Furthermore, many experiments to explore the mechanism of AAMP for LIHC will help developing a more accurate prognosis model for patients and provid- ing a basis for more personalized treatment.
Conclusion
AAMP is a better molecular marker with diagnostic and prognostic value in pan-cancer, especially in LIHC. High expression of AAMP is significantly associated with poor prognosis of LIHC and is considered an independent prognostic marker by Cox regression. This study provides a theoretical basis for more comprehensive analysis of the clinical application of this molecule in tumor therapy in the future.
Abbreviations
| ACC | Adrenocortical carcinoma |
| BLCA | Bladder urothelial carcinoma |
| BRCA | Breast invasive carcinoma |
| CESC | Cervical squamous cell carcinoma and endocervical adenocarcinoma |
| CHOL | Cholangio carcinoma |
| COAD | Colon adenocarcinoma |
| DLBC | Lymphoid neoplasm diffuse large B-cell lymphoma |
| ESCA | Esophageal carcinoma |
| GBM | Glioblastoma multiforme |
| HNSC | Head and neck squamous cell carcinoma |
| KICH | Kidney chromophobe |
| KIRC | Kidney renal clear cell carcinoma |
KIRP LAML LGG
Kidney renal papillary cell carcinoma
Acute myeloid leukemia
Brain lower grade glioma
LIHC
Liver hepatocellular carcinoma
LUAD Lung adenocarcinoma
LUSC Lung squamous cell carcinoma
Mesothelioma
MESO OV PAAD
Ovarian serous cystadenocarcinoma
Pancreatic adenocarcinoma
PCPG
Pheochromocytoma and paraganglioma
PRAD
Prostate adenocarcinoma
READ SARC SKCM Skin cutaneous melanoma
Rectum adenocarcinoma Sarcoma
STAD
Stomach adenocarcinoma
TGCT THCA
Testicular germ cell tumors Thyroid carcinoma
THYM
Thymoma
UCEC
Uterine corpus endometrial carcinoma
UCS
Uterine carcinosarcoma
UVM
Uveal melanoma
Acknowledgements
We sincerely acknowledge the contributions from the TCGA, ICGC, UALCAN, cBioPortal, and HPA databases.
Author contributions
All authors participated in the design, interpretation of the studies and, analysis of the data, and review of the manuscript; YW and TL have equal contributions, YW and TL wrote this paper. KZ and RHH provided experimental concepts and design, offered scientific direction and reviewed the manuscript. LJ contributed to the conception, project administration, writing-review and editing. All authors read and approved the final manuscript.
Funding
This study had no external funding.
Availability of data and materials
The original manuscript contained in the research report is included in the article. Further inquiries can be made directly to the corresponding author.
Declarations
Competing interests
The authors declare no competing interests.
Author details
1 Department of General Surgical Department, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Chaoyang District, Bei-
jing 100015, People’s Republic of China. 2 Department of Pathology, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Chaoy- ang District, Beijing 100015, People’s Republic of China.
Received: 25 June 2023 Accepted: 16 July 2023 Published online: 27 July 2023
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