Pan-cancer analysis of alternative splicing regulator heterogeneous nuclear ribonucleoproteins (hnRNPs) family and their prognostic potential
Hao Li1 | Jingwei Liu2 Shixuan Shen3 Di Dai1 Shitong Cheng1 Xiaolong Dong1
Liping Sun3 | Xiaolin Guo1 D
1Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, China
2Department of Anorectal Surgery, the First Affiliated Hospital of China Medical University, Shenyang, China
3Tumor Etiology and Screening Department of Cancer Institute and General Surgery, Key Laboratory of Cancer Etiology and Prevention, The First Hospital of China Medical University, China Medical University, Shenyang, China
Correspondence
Xiaolin Guo, Department of Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang 110011, China.
Email: xiaolinguo@cmu.edu.cn
Liping Sun, Tumor Etiology and Screening Department of Cancer Institute and General Surgery, The First Hospital of China Medical University, Key Laboratory of Cancer Etiology and Prevention, China Medical University, Shenyang 110011, China. Email: lpsun@cmu.edu.cn
Funding information
Ministry of Public Health and Sanitation, Grant/Award Number: 201402018; National Natural Science Foundation of China, Grant/ Award Number: 81902958; The Liaoning Key R&D Program, Grant/Award Number: 2020JH2/10300063; Cohort study on non- AIDS-related diseases in the First Affiliated Hospital of China Medical University, Grant/ Award Number: 2017ZX10202101-004-006
Abstract
As the most critical alternative splicing regulator, heterogeneous nuclear ribonu- cleoproteins (hnRNPs) have been reported to be implicated in various aspects of cancer. However, the comprehensive understanding of hnRNPs in cancer is still lack- ing. The molecular alterations and clinical relevance of hnRNP genes were system- atically analysed in 33 cancer types based on next-generation sequence data. The expression, mutation, copy number variation, functional pathways, immune cell cor- relations and prognostic value of hnRNPs were investigated across different cancer types. HNRNPA1 and HNRNPAB were highly expressed in most tumours. HNRNPM, HNRNPUL1, and HNRNPL showed high mutation frequencies, and most hnRNP genes were frequently mutated in uterine corpus endometrial carcinoma (UCEC). HNRNPA2B1 showed widespread copy number amplification across various cancer types. HNRNPs participated in cancer-related pathways including protein secretion, mitotic spindle, G2/M checkpoint, DNA repair, IL6/JAK/STAT3 signal and coagula- tion, of which hnRNP genes of HNRNPF, HNRNPH2, HNRNPU and HNRNPUL1 are more likely to be implicated. Significant correlation of hnRNP genes with T help cells, NK cells, CD8 positive T cells and neutrophils was identified. Most hnRNPs were as- sociated with worse survival of adrenocortical carcinoma (ACC), liver hepatocellular carcinoma (LIHC) and lung adenocarcinoma (LUAD), whereas hnRNPs predicted bet- ter prognosis in kidney renal clear cell carcinoma (KIRC) and thymoma (THYM). The prognosis analysis of KIRC suggested that hnRNPs gene cluster was significantly as- sociated with overall survival (HR = 0.5, 95% CI = 0.35-0.73, P = 0.003). These find- ings provide novel evidence for further investigation of hnRNPs in the development and therapy of cancer in the future.
KEYWORDS
alternative splicing, hnRNPs, pan-cancer
Li and Liu are contributed equally to this work.
@ 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.
RNA splicing procedure removes introns and combines exons of pre-mature mRNA, which is essential for cellular homoeostasis, functional regulation, tissue development and species diversity.1,2 Almost each transcript derived from human genes undergoes di- verse patterns of alternative splicing (AS) including exclusion or in- clusion of “cassette” exons, changes of AS sites, intron retentions, alternative promoter or terminator, and mutually exclusive exons.3,4 Alternative splicing of pre-mRNA is responsible various aspects of biological processes and aberrant AS contribute to a series of disor- ders even cancer.5,6 Emerging evidence has demonstrated that can- cer cells hijack and alter AS process, thereby facilitating its growth and metastasis.7,8
As the most critical alternative splicing regulator, heteroge- neous nuclear ribonucleoproteins (hnRNPs) family are responsible for the maturation of pre-mRNAs into functional mRNAs as well as the stabilization of mRNA translocation.9,10 Through the RNA bind- ing domains (RBDs), hnRNPs accomplish the recognition of specific RNA sequences and control various biological processes of RNA function and metabolism.11,12 Mechanistically, hnRNPs constitute mRNA-protein 40S core complex via binding to RNA elements in- cluding exon and intron splicing regulators, which precisely control the alternative splicing of pre-mRNAs.13 Until now, approximately twenty key members of hnRNPs family have been identified includ- ing hnRNP A-U, which share common characteristics but differ in biological properties.14
Emerging evidence has suggested close relationship between hnRNPs and multiple malignant behaviours of cancer.15 For instance, hnRNP A1 modulates the alternative splicing of CDK2, thereby con- tributing to oral squamous cell carcinoma by altering cell cycle pro- gression.16 In pancreas cancer, hnRNP E1 cancer cell metastasis via controlling the alternative splicing of integrin §1, a membrane recep- tor involved in cell adhesion, immune response and metastatic diffu- sion of cancer cells.17 Studies have suggested that hnRNP A1, A2/B1 and K bind to the promoter of tumour suppressor Annexin-A7, which alters Annexin-A7 splicing patterns and leads to prostate cancer.18 In addition, hnRNP L has been found to regulate VEGFA mRNA trans- lation and induce apoptosis of cancer cells, thereby inhibiting the development of cancer.19
In spite of the current reports indicating the significant contri- bution of hnRNPs in carcinogenesis, our knowledge of the specific implication concerning hnRNPs still remains limited. Considering the increasing essential role of hnRNPs in cancer, it is of great interest to unravel the whole landscape of expression, mutation and copy number variation of alternative splicing regulator hn- RNPs family as well as their prognostic potential. Through analys- ing multiple levels of data from The Cancer Genome Atlas (TCGA) including 33 types of cancers, we described the specific implica- tion of alternative splicing regulator hnRNPs in various cancers in this study. It is anticipated that the comprehensive pan-cancer analysis could shed light on the way alternative splicing lead to cancer.
2 MATERIALS AND METHODS |
2.1 Collection of hnRNP genes
We collected 22 hnRNP genes from recently published review pa- pers. All these gene symbols were converted into Ensemble gene IDs and HGNC symbols by manually curated from GeneCards (https:// www.genecards.org/).
2.2 | Genome-wide omics data across 33 cancer types from next-generation sequence data
The results in our analysis were based upon omics datasets generated by TCGA Research Network (http://cancergenome.nih.gov/). We totally analysed 33 different TCGA projects, and each project represented a specific cancer type, including KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; KICH, kidney chromo- phobe; LGG, brain lower-grade glioma; GBM, glioblastoma multiforme; BRCA, breast cancer; LUSC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma; READ, rectum adenocarcinoma; COAD, colon ade- nocarcinoma; UCS, uterine carcinosarcoma; UCEC, uterine corpus en- dometrial carcinoma; OV, ovarian serous cystadenocarcinoma; HNSC, head and neck squamous carcinoma; THCA, thyroid carcinoma; PRAD, prostate adenocarcinoma; STAD, stomach adenocarcinoma; SKCM, skin cutaneous melanoma; BLCA, bladder urothelial carcinoma; LIHC, liver hepatocellular carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; ACC, adrenocortical carcinoma; PCPG, pheochromocytoma and paraganglioma; SARC, sarcoma; LAML, acute myeloid leukaemia; PAAD, pancreatic adenocarcinoma; ESCA, oesophageal carcinoma; TGCT, testicular germ cell tumours; THYM, thymoma; MESO, mesothelioma; UVM, uveal melanoma; DLBC, lym- phoid neoplasm diffuse large B-cell lymphoma; CHOL, cholangiocar- cinoma. All of the TCGA data including TPM (Transcripts Per Kilobase Million) expression, copy number variation, mutation and clinical infor- mation (survival status, stages, grades, survival time) were download from UCSC XENA (https://xenabrowser.net/).
2.3 | Identification of differentially expressed genes
To identify the alternation of gene expression in each cancer type, we used the Deseq2 package in R to identify differentially expressed genes. Genes with adjusted P-values < 0.05 and at least twofold changes in expression were identified as differentially expressed genes in each cancer type.
2.4 | Protein-wide omics data across pan-cancer from protein expression data
The protein expression data of hnRNP genes were obtained from ‘The Human Protein Atlas’ database (https://www.proteinatlas.org/).
We totally analysed 20 cancer types on hnRNP genes protein ex- pression, including BRCA (breast cancer), carcinoid (carcinoid), CECA (cervical cancer), COCA (colorectal cancer), glioma (glioma), HNSC (head and neck cancer), LIHC (liver cancer), LUCA (lung cancer), lymphoma (lymphoma), melanoma (melanoma), OV (ovarian cancer), PACA (pancreatic cancer), RACA (renal cancer), SKCA (skin cancer), STCA (stomach cancer), TECA (testis cancer), THCA (thyroid can- cer), URCA (urothelial cancer), ENCA (endometrial cancer) and PRCA (prostate cancer).
2.5 Genome-wide mutation data across pan- cancer cell lines from CCLE datasets
Mutation frequency of hnRNP family genes in pan-cancer cell lines were obtained from Cancer Cell Line Encyclopedia (CCLE) datasets (https://portals.broadinstitute.org/ccle).
2.6 | Oncogenic pathway activity across cancer types
In order to calculate the activity of cancer hallmark-related path- ways, the TPM gene expression was subjected to gene set variation analysis (GSVA), which is a non-parametric unsupervised method for estimating variation of gene set enrichment through the samples of an expression dataset. To identify the hnRNP genes that were cor- related with activation or inhibition of certain pathway, we calcu- lated the Pearson correlation coefficient (PCC) between expression of hnRNP genes and pathway activity. The regulator-pathway pairs with |PCC|>0.3 and adjusted P-value < 0.05 were identified as sig- nificantly correlated hnRNP genes.
2.7 | Correlation of hnRNP genes with immune- related genes
The major immune cells related genes were shown in Table S1. In order to explore the correlation between hnRNP genes and immune-related genes, we calculated the Spearman correlation coefficient (SCC) between expression of hnRNP genes and im- mune-related genes. The regulator-pathway pairs with |PCC|>0.3 and adjusted P-value < 0.05 were identified as significantly corre- lated hnRNP genes.
2.8 | Clinical significance of hnRNP genes
To explore whether the expression of hnRNP genes was associated with patient survival, we divided all the patients into two groups based on the median expression of each hnRNP gene. The log-rank test was used to test the different survival rates between the two groups. The P-values < 0.05 were considered as statistical significance.
|
3 RESULTS
3.1 Expression profile of hnRNP genes across different cancer types
A total of 22 hnRNP genes were identified after searching the pub- lished review papers, the information of which was summarized in Table S1. Using the count data of TCGA, we described the differential expression of these genes across different cancer types. As shown in Figure 1A, hnRNP genes demonstrated heterogeneous distributions in different cancer types: HNRNPA1 and HNRNPAB were highly ex- pressed in most tumours; HNRNPA1P33 expression was increased in COAD, READ and LUAD whereas decreased in CHOL, PRAD and BLCA. The detailed LogFC changes were listed in Table S2. Next, we visualized the differential expression of HNRNPAB in each can- cer (Figure 1B). Based on the immunohischemistry results of Protein Atlas database, we showed the protein expression of hnRNP genes in various cancer types (Figure 1C). In addition, immunohischemistry results of HNRNPD based on ‘The Human Protein Atlas’ database representing the protein expression was shown in Figure 1D.
3.2 | Pan-cancer genetic alternations of hnRNP genes
The mutation frequency of hnRNP genes were analysed, and the results indicated that most hnRNP genes were frequently mutated in UCEC (Figure 2A). The overall average mutation frequency ranged from 0% to 14.9%, and hnRNP genes including HNRNPM, HNRNPUL1, HNRNPL showed relatively high mutation frequencies. Several cancers such as THCA, PCPG and UVM demonstrated rare hnRNP gene mutations. In order to show more detailed information about hnRNP mutation, we then visualized the mutation details of hnRNP genes in UCEC by on- coplot (Figure 2B). Besides, CCLE database was used to demonstrate the mutation status of hnRNP genes in various human cancer cell lines (Figure 2C). The results indicated that colorectal cancer and lung can- cer cell lines suggested frequent mutations of most hnRNP genes. In addition, the copy number variations of hnRNP genes were also inves- tigated across different cancer types (Figure 2D): HNRNPA2B1 gene showed widespread copy number amplification across various cancer types whereas almost no CNV was detected in LAML.
3.3 | Association of hnRNPs with cancer-related pathways and immune status
In order to elucidate the molecular implication of hnRNPs in car- cinogenesis, the relation of hnRNPs with cancer-related pathways was analysed and visualized in Figure 3A. The findings suggested that hnRNP expressions significantly correlated with the acti- vation or suppression of various oncogenic pathways. It could be concluded that hnRNP genes mainly participated in cancer- related pathways including protein secretion, mitotic spindle,
| HNRNPCL1 | ||||||||||||||||||
| HNRNPAB | ||||||||||||||||||
| HNRNPA1 | ||||||||||||||||||
| HNRNPD | ||||||||||||||||||
| HNRNPH1 | ||||||||||||||||||
| HNRNPA1L2 | ||||||||||||||||||
| HNRNPUL2 | ||||||||||||||||||
| HNRNPUL1 | ||||||||||||||||||
| HNRNPAO | ||||||||||||||||||
| HNRNPUL2-BSCL2 | ||||||||||||||||||
| HNRNPA281 | ||||||||||||||||||
| HNRNPL | ||||||||||||||||||
| HNRNPF | ||||||||||||||||||
| HNRNPC | ||||||||||||||||||
| HNRNPU | ||||||||||||||||||
| HNRNPR | ||||||||||||||||||
| HNRNPM | ||||||||||||||||||
| HNRNPDL | ||||||||||||||||||
| HNRNPLL | ||||||||||||||||||
| HNRNPH3 | ||||||||||||||||||
| HNRNPH2 | ||||||||||||||||||
| HNRNPA1P33 | ||||||||||||||||||
A
logFC
Adj.P.value * P<0.05 ** P<0.01 *** P <0.001
C
Protein Expression
High
Medium
Low
Not.detected
-2-1 0 1 2
HNRNPUL1
HNRNPL
HNRNPAB
HNRNPR
HNRNPH2
HNRNPM
HNRNPA2B1
HNRNPLL
HNRNPD
HNRNPC
HNRNPU
HNRNPA1
HNRNPF
HNRNPH1
HNRNPDL
HNRNPUL2
HNRNPAO
CHOL
PRAD
BLCA
ESCA
STAD
HNSC
KIRP
KIRC
THCA
COAD
READ
LUSC
LIHC
BRCA
UCEC
LUAD
KICH
glioma
melanoma
BRCA
TECA
COCA
SKCA
URCA
PACA
CECA
ENCA
OV
LUCA
STCA
PRCA
lymphoma
LIHC
RACA
THCA
carcinoid
HNSC
B
D
HNRNPAB
300
300
100
HNRNPAB
300
HNRNPAB
100
HNRNPAB
80
100
30
100
10
30
3
10
10
30
10
cancer
normal
cancer
normal
3
cancer
normal
cancer
normal
BLCA
BRCA
CHOL
COAD
Glioma
Melanoma
Lymphoma
SKCA
HNRNPAB
300
HNRNPAB
300-
100
100
100
HNRNPAB
HNRNPAB
100
30
30
30
30
10
10
10
3
10
cancer
normal
cancer
normal
cancer
normal
cancer
normal
ESCA
HNSC
KICH
KIRC
OV
CECA
BRCA
PRCA
300-
HNRNPAB
HNRNPAB
300 -
100
HNRNPAB
100
HNRNPAB
30
100
300
ER
30
10
10
30
100
3
10
30
3
cancer
normal
cancer
normal
cancer
normal
cancer
normal
KIRP
LIHC
LUAD
LUSC
URCA
RECA
PACA
LIHC
300
HNRNPAB
100
HNRNPAB
300
HNRNPAB
300
HNRNPAB
300
100
100
30
100
30
10
30
10
30
.
10
3
cancer
normal
cancer
normal
cancer
normal
cancer
normal
PRAD
READ
STAD
UCEC
STCA
HNSC
COCA
LUCA
G2/M checkpoint, DNA repair, IL6/JAK/STAT3 signal and co- agulation. At the same time, the numbers of the correlated path- ways of each gene were summarized, of which hnRNP genes of HNRNPF, HNRNPH2, HNRNPU and HNRNPUL1 are more likely to be implicated in oncogenic processes (Figure 3B). As pathways of adipogenesis, androgen response and hypoxia showed differ- ent correlations with diverse hnRNP genes, we summarized the correlations among different hnRNP genes as well as the specific correlation with adipogenesis, androgen response and hypoxia in Figure 3C. We found that hnRNPs might work together in carcinogenesis as significant correlations were detected such as HNRNPL-HNRNPAB (r = 0.83), HNRNPUL2-HNRNPA0 (r = 0.58) and HNRNPAB-HNRNPLL (r = 0.57). At last, the effect of hnRNP genes on immune cell infiltration was shown in Figure 3D. The
most relevant immune cells included T help cells, NK cells, CD8 positive T cells and neutrophils. HNRNPH2, HNRNPU, HNRNPDL and HNRNPA0 all demonstrated significant correlation with im- mune cell infiltration.
3.4 Prognostic significance of hnRNP genes
The prognostic significance of hnRNP genes in different cancer types was analysed by Cox regression (Figure 4A). In cancers including ACC, LIHC and LUAD, most hnRNPs were associated with worse survival of cancer patients. In contrast, hnRNPs predicted better prognosis in cancers such as KIRC and THYM. In addition, certain hnRNP gene might exert obvi- ous different prognostic effect across various cancer types. For instance,
|
A
Mutation Frequency
B
Altered in 137 (25.85%) of 530 samples.
19526
0
0.02 0.04 0
0.06
0
40
HNRNPM
0
HNANPM
14%
HNRNPUL1
HNRNPULT
13%
HNRNPL
HNANPH2
12%
HNRNPU
HNRNPU
12%
HNRNPA2B1
HNRNPR
10%
HNRNPF
HNRNPD
9%
HNRNPR
HNANPL
HNRNPH2
HNANDE
HNRNPA1
HARNPAAR
7%
HNRNPD
HNANPH!
7%
HNRNPUL2
HNANPLE
5%
HNRNPAO
HNANPOL
0%
HNRNPLL
HNRNRAZB1
6%
HNRNPA1L2
HNRNPC
6%
HNRNPDL
HNANPA!
5%
HNRNPH3
HNANPH3
5%
HNRNPCLT
2%
HNRNPH1
MNANPULZ
2%
HNRNPC
HNANPAIL2
HNRNPAB
HNANPAO
HNRNPCL1
HNRNPA1P33
Missense_Mutation
Frame_Shift_Ins
HNRNPUL2-BSCL2
In_Frame_Ins
Translation_Start_Site
UCEC
COAD
STAD
BLCA
CESC
READ
SKCM
DLBC
ESCA
LUAD
LUSC
HNSC
CHOL
UCS
GBM
PAAD
OV
BRCA
SARC
LAML
ACC
LIHC
KIRP
KIRC
KICH
TGCT
THYM
THCA
UVM
Splice_Site
Nonstop_Mutation
LGG
MESO
PRAD
PCPG
Nonsense_Mutation
· Multi_Hit
Frame_Shift_Del
CNV Frequency
C
D
0.5 Loss 0 Gain 0.5
HNRNPL
HNRNPLL
HNRNPM
HNRNPR
Copy number variation across cancer types
HNRNPH3
HNRNPU
HNRNPAO
HNRNPUL1
HNRNPA1
HNRNPH2
HNRNPA1L2
HNRNPA2B1
HNRNPAB
HNRNPUL2
0.2
Mutation Frequence
HNRNPC
HNRNPCL1
HNRNPH1
HNRNPD
HNRNPDL
HNRNPF
HNRNPAO
HNRNPH1
HNRNPH2
HNRNPF
HNRNPH3
0
HNRNPL
HNRNPLL
HNRNPM
HNRNPDL
HNANPA1
HNRNPR
HNRNPU
HNRNPUL1
HINRNPA1L2
HNRNPUL2
HNRNPD
ACC
LUAD
ESCA
BLCA
CESC
BRCA
LIHC
CHOL
KIRC
KICH
GBM
COAD
KIRP
HNSC
UCEC
DLBC
LGG
LAML
HNRNPCL1
HNRNPA2B
HNRNPC
HNRNPAB
HNRNPA1 and HNRNPC showed different prognostic association in di- verse cancer types, which were therefore shown by forest plot to illustrate the specific predictive effect in diverse types of cancers (Figure 4B). As many hnRNP genes demonstrated influence on KIRC prognosis, we per- formed clustering analysis of prognosis-related hnRNP genes (Figure 4℃). The prognosis analysis of the cluster C1 and C2 suggested that C2 cluster was significantly associated with better survival compared with C1 cluster (HR = 0.50, 95% CI = 0.35-0.73, P = . 003), indicating the promising po- tential of hnRNP genes in the prediction of cancer prognosis (Figure 4D).
4 DISCUSSION |
In order to clarify the critical role of alternative splicing regulator heterogeneous nuclear ribonucleoproteins family across various types of cancer, we comprehensively analysed the core genes which belong to hnRNPs family. Based on multiple levels of data from TCGA, genomic and transcriptomic landscape of key hnRNPs fam- ily genes was investigated by pan-cancer analysis. The results sug- gested that hnRNPs were differentially expressed in certain cancers and corresponding controls, which also correlated with prognosis of
patients. The identified correlation between hnRNPs with multiple cancer-related pathways suggested close implication of hnRNPs in the development of various types of cancers.
By comprehensively analysing the transcriptional data of 22 core hnRNP genes in TCGA, we describe the expression landscape of hnRNP genes across different cancer types. Heterogeneous dis- tributions of hnRNP genes were observed in different cancer types: HNRNPA1 and HNRNPAB were highly expressed in most tumours. It has been reported that hnRNPA1 was highly expressed in gastric can- cer tissues, which promote proliferation, migration and EMT of gastric cancer cells.20 In lung cancer, knockdown of HNRNPA1 suppressed the viability and growth as well as induced cell cycle arrest of lung cancer cells.21 The results of previous studies and our analysis all suggested the critical role of HNRNPA1 in the initiation and development of dif- ferent types of cancers. Besides, HNRNPAB overexpression has been found in metastatic cells or cancer tissues in hepatocellular carcinoma patients, which lead to EMT and metastasis of hepatocellular carcinoma cells in vivo.22 The oncogenic effect of HNRNPA1 and HNRNPAB is of great interest to understand the underlying mechanisms of alternative splicing in carcinogenesis, which might provide novel insights into an- ti-tumour therapy. Moreover, HNRNPA1P33 expression was increased
WILEY
A
B
Nog_HEVE METABOLISM
Nog_HEDGEHOG_SIGNALINGNing_HYPONIA
Nog_ ESTROGIIN RESPONSE LIMATOGENESS
HNANPA ! ANPULA-ESCLA
POR_PS]_PATHWAY POR ADIPOGENESIS
POR BILE ACID_METABOLISM
Positive
Negative
POR NOTCH SIGNALING
Ning PANCREAS BETA CELLS
10
ALLOGRAFT_REJECTION
NONPOL
HNANPAZ51
POR_MTORCI_SIGNRUINHEDGEHOG SIGNALING
Ning KRAS_SIGNALING_ON
Ning_INFA_SIGNALING_VIA_NFKB
NINH2
HINHINPL
POR: KRF_TARGETS
POR_SPERMATOGENESE
Number of Pathway
5
Neg. BILE ACID METABOLISM
Neg. PEROXISOME
HMENPE
HIFINPULZ
Pom MEG TARGETS_V2
POR_XENOBIOTIC_METABOLISM
APICAL JUNCTION
0
ANGIOGENESIS
INENPO
HNANPU
PER MYC_TARGETS_VI
POLANDROGEN RESPONSE
Neg INFLAMMATORY_RESPONSE
5
Ning_CHOLESTEROL HOMEOSTASIS
İNANPULI
HNANPAR
POIK DNIA REPAR
POR GZM-CHECKPOINT
Ning ESTROGEN RESPONSE_BAFLY
Nog_COAGULATION
10
NONPR
INPM
APICAL SURFACE
POR_WANT_BIETA CATENIN_SIGNALING POR HEME_METABOLISM
Nog_IL6_JAK_STATE_SIGNALING
HORNPLL
HNANPAI
OUV RESPONSE ON POR MITOTIC SPINDLE
HNRNPAO
HNRNPA1
HNRNPA1L2
HNRNPA2B1
HNRNPAB
HNRNPC
HNRNPD
HNRNPDL
HNRNPF
HNRNPH1
HNRNPH2
HNRNPH3
HNRNPL
HNRNPLL
HNRNPM
HNRNPR
HNRNPU
HNRNPUL1
HNRNPUL2
HNRNPUL2-BSCL2
Nog_UV_RESPONSE_DN
OR UNFOLDED PROTEIN R
Ng. MYOGENESIS
IHNENPC
Nog_PROTEIN_SECRETION
HNAINPAO
HNFINPHI
AKT_MITOR_SIGNALING
Ning_PI3K_AKT_MTOR_SIGNALING CON NEXTNODOTIC METABOLISM
POR TOF BETA SIGNINGPROTEIN SECRETION
SSONY
1800
CHIT1
COLZA2
CTSK
LaNa MSR1
SCARB2
SCG5
SGMS1
CORNZAIP
D
SLC2SA12
DNASB1
DI ASUS
€ T300
ABTT
CAMLG
HAUS3
KATGA
SEC240
KLF9
LEPROTL 1
MAPKAPKS-AS1
Macrophages
C
HNRNPLL
HNRNPAB
HNANPL
HNRNPA1
HNRNPA1L2 HNRNPD
PPP1R2
HNRNPDL HNRNPU
HNRNPH2
HNRNPH3
HNRNPUL2-BSCL2
HNRNPUL1
HNANPUL2
HNRNPA2B1 HNRNPF
HNRNPCL1
PPPERSO
APA1
HNRNPC
HNANPH1
HNRNPAO
HNRNPM
HNRNPR
NUP107
PHF10
PRRS
r
helper cells
RØM3
CD8
SF1
GOLGABA
LRBA
HNANPD
HNRNPC
HNANPAB
HNRNPLL
cells
SRSF7
HNRŅPDL
TBCC
o
THUMPD1
O
HNRNPAB
HNRNPL
FAMILIA
FRYL
HNANPA2B1
DOXSO
HNANPF
o
TMC6
TMEM259
O
HNRNPA1
BORA
HNANPA1L2
o
4
HNRNPA1L2
BATE
HNANPH1
TSC2203
ADIPOGENESIS
o
N
HNRNPD
VAMP2
ATFE
o
N
HNRNPDL
HNRNPA1
HNRNPU
ASF1
ZFP3612
O
ZNF22
O
ST
HNRNPC
ANP32B
HNRNPH2
-
-
ZNF609
ZNF741
O
HNRNPH2
HNRNPA0
ZNF91
HNRNPUL2-BSCL2
ZNF528
CEACAMB
ANDROGEN_RESPONSE
o
0
HNRNPH3
ZNF205
HNANPH3
HPGDS
HNRNPH1
KIT
.
o
corr
Mast cells
SLC30AL
0
A
HNRNPUL1
PSMD
@LINC01140
O
S
HNRNPAO
PRIX
HNANPL
PPMTH
o
J
HNRNPUL2
MRCZ
HNRNPUL2-BSCL2
o
HNRNPA2B1
MCM3AP
SPTGS1
-
MAPRE3
HNANPLL
ØSCG2
o
HNRNPF
KANKE
cells
HNANPUL2
OSLC24A3
ΗΥΡΟΧΙΑ
o
N
HNRNPM
IGFBPS
HNRNPM
Neutrophils
TALI
TPS82
O
J
HNRNPR
HNANPR
HNANPUL1
HNRNPCL1
HNANPU
o
-
FZR
COCS
BCL
·HPSE
COR3
VWASA
APBBZ
SGCB
Th1
-
-
LAAN3
LAP8
cells
EGFLO
B cells
OUSPS DGKI
ENNA
ECPR2
SLC25A37
SLC2244
cells
CTLA4 @ CSF26
CD72
SLC15A2
ORSL1
MICAL3
OU53W
LOND
ATP9/
APOD
PRKCO
NCALD
VZWILL
CD3D
SCNJA
in COAD, READ and LUAD whereas decreased in CHOL, PRAD and BLCA, which indicated that hnRNPs might exert different functions in diverse kinds of tumours.
Pan-cancer genetic alternations of hnRNP genes indicated that the overall average mutation frequency ranged from 0% to 14.9%, and hnRNP genes including HNRNPM, HNRNPUL1, HNRNPL showed high mutation frequencies. The critical role of HNRNPM in the development and metastasis has been investigated in colon cancer,23 prostate cancer24 and breast cancer.25,26 Importantly, next-generation sequencing has suggested HNRNPL as a key regula- tor of prostate cancer via modulating the alternative splicing of multi- ple RNAs such as the core oncogene androgen receptor.27 It is worth noting that most hnRNP genes were frequently mutated in UCEC, a certain type of cancer with high global mutation burden.28 Several cancers such as THCA, PCPG and UVM demonstrated rare hnRNP gene mutations. Besides, human cancer cell lines analysis based on CCLE demonstrated that colorectal cancer and lung cancer cell lines possess frequent mutations of most hnRNP genes. Future investiga- tions concerning the mutations of hnRNP genes in lung cancer and colorectal cancer might reveal critical evidence of contribution of
hnRNPs in the development of cancer. In addition, the copy number variations investigation revealed that HNRNPA2B1 gene showed widespread copy number amplification across various cancer types whereas almost no CNV was detected in LAML.
The correlation analysis of hnRNPs with cancer-related path- ways suggested that hnRNPs significantly contributed to the ac- tivation or suppression of various oncogenic pathways including protein secretion, mitotic spindle, G2/M checkpoint, DNA repair, IL6/JAK/STAT3 signal and coagulation. Different hnRNPs were found to be associated with distinct cancer pathway alterations, suggesting different functional effects of hnRNPs within the same alternative splicing regulator family. HNRNPA1 was significantly associated with pathways including DNA repair, G2/M checkpoint, E2F targets and myc targets. HNRNPAB showed correlation with G2/M checkpoint and wnt-ß-catenin pathways. In addition, hnRNP genes of HNRNPF, HNRNPH2, HNRNPU and HNRNPUL1 are more likely to be implicated in oncogenic processes. Previously, HNRNPU has been reported to facilitate chromatin looping and p300-mediated transactivation of transcription factor early growth response 1, thus promoting cancer progression.29 Furthermore,
|
| 2.61 | 0.76 | 0.97 | 0.79 | 1.38 | 0.82 | 0.94 | 1.09 | 1.03 | 1.16 | 0.64 | 1.82 | 1,39 | 0,83 | 1.56 | 1.49 | 0.96 | 0.75 | 0.96 | 0.84 | 1.44 | 2.02 | 0,47 | 0.99 | 1.13 | 0.89 | 0.71 | 1.17 | 0.12 | 0.95 | 1.43 | 0.96 | HNRNPAO |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5.32 | 0.81 | 0.73 | 1.07 | 1.21 | 1.03 | 0.74 | 0.84 | 1.12 | 3.67 | 0.73 | 2.05 | 0.84 | 0.64 | 1.40 | 1.41 | 0.80 | 0.96 | 0.93 | 1.55 | 1.59 | 1.33 | 0.81 | 1.41 | 1.02 | 1.15 | 2.28 | 0.54 | 0.18 | 0.58 | 0.79 | 0.68 | HNRNPA1 |
| 3.96 | 0.92 | 0.71 | 0.83 | 0.97 | 1.23 | 1.02 | 0.83 | 0.94 | 8.25 | 0.91 | 0.77 | 0.70 | 0.90 | 1.03 | 0.85 | 0,99 | 0.94 | 1.01 | 0.65 | 0.98 | 8.87 | 0.69 | 1.11 | 0.97 | 1.18 | 0.33 | 0.37 | 0.61 | 1.01 | 1.02 | 0.83 | HNRNPA1L2 |
| 0.86 | 1.10 | 0.96 | 0,85 | 0.55 | 1.03 | 0,87 | 0.87 | 1.00 | 1.69 | 0.91 | 1.30 | 0.88 | 0.73 | 1.33 | 1.04 | 0.99 | 0,75 | 0,09 | 1.78 | 1.12 | 1.30 | 0.05 | 0.91 | 1.08 | 1.22 | 0.61 | 1.00 | 1.91 | 0.59 | 0.76 | HNRNPA1P33 | |
| 8.48 | 0.83 | 0.98 | 1.00 | 0.86 | 1.05 | 1.12 | 0.95 | 1.24 | 9.05 | 1.03 | 1.13 | 1.01 | 1.63 | 1.49 | 1.47 | 0.89 | 1.53 | 0.87 | 1.22 | 0.70 | 3.78 | 0.62 | 1.46 | 0.97 | 0.92 | 0.43 | 0.85 | 0.21 | 0.80 | 1.25 | 0.98 | HNRNPA2B1 |
| 3.55 | 1.11 | 0.97 | 0.79 | 0.59 | 0.76 | 1.83 | 1.22 | 1.19 | 3.49 | 1.28 | 1.72 | 2.04 | 1.09 | 1.00 | 1.52 | 0.91 | 1.57 | 0.79 | 1.32 | 0.65 | 3.15 | 0.60 | 1.04 | 1.20 | 0.86 | 0.74 | 0.69 | 0.33 | 0.80 | 1.98 | 6.09 | HNRNPAB |
| 3.71 | 0.91 | 0.73 | 1.13 | 0.51 | 1.13 | 0.97 | 0.67 | 1.30 | 4.03 | 0.78 | 1.89 | 1.38 | 1.09 | 1.42 | 1.85 | 0.94 | 1.20 | 0.96 | 2.06 | 0.93 | 2.08 | 0.99 | 1.70 | 1.11 | 0.94 | 0.38 | 0.28 | 0.13 | 0.81 | 1.00 | 1.45 | HNRNPC |
| 1.31 | 0.71 | 1.02 | 0.85 | 1.13 | 1.01 | 2.82 | 0.96 | 1.22 | 1.39 | 0.65 | 1.73 | 0.74 | 1.15 | 1.48 | 1.09 | 0.83 | 1.52 | 0.91 | 1.18 | 1.19 | 1.04 | 1.00 | 1.34 | 0.89 | 1.23 | 0.00 | 0.86 | 0.13 | 0.99 | 1.53 | 1.40 | HNRNPCL1 |
| 3.75 | 0.77 | 0.82 | 0.71 | 0.75 | 1.02 | 0.86 | 0.88 | 1.16 | 3.86 | 0.74 | 1.57 | 1.53 | 1.35 | 1.45 | 1.34 | 1.07 | 1.02 | 0.91 | 1.07 | 0.51 | 8.10 | 0,57 | 1.25 | 1.05 | 0.95 | 0.70 | 1.01 | 0.00 | 1.04 | 0.90 | 1.83 | HNRNPD |
| 5.16 | 0.01 | 0.91 | 0.72 | 1.18 | 1.03 | 1.12 | 0.02 | 1.04 | 1.99 | 0.71 | 0.06 | 0.05 | 0.60 | 1.26 | 1.09 | 1.00 | 1.27 | 1.00 | 1.01 | 1.54 | 0.29 | 0.61 | 1.07 | 1.07 | 1.02 | 0.45 | 0.04 | 0.31 | 0.69 | 1.00 | 1.03 | HNRNPDL |
| 4.31 | 0.84 | 1.04 | 0.81 | 0.62 | 0.78 | 1.35 | 0.96 | 0.86 | 2.16 | 0.70 | 1.51 | 1.08 | 1.30 | 1.76 | 1.55 | 0.92 | 1.07 | 0.88 | 1.32 | 0.53 | 2.35 | 0.99 | 1.04 | 1.08 | 0.78 | 0.45 | 0.76 | 0.11 | 0.68 | 1.11 | 1.15 | HNRNPF |
| 4.90 | 0.83 | 0.75 | 0.90 | 0.66 | 0.78 | 1.35 | 1.04 | 0.77 | 8.77 | 1.05 | 1.15 | 1.04 | 1.75 | 1.94 | 0.99 | 0.96 | 1.36 | 0.97 | 0.92 | 1.99 | 2.59 | 0.26 | 1.27 | 1.02 | 0.85 | 0.96 | 1.64 | 0.11 | 1.00 | 1.30 | 1.18 | HNRNPH1 |
| 0.49 | 1.05 | 1.00 | 0.86 | 1.09 | 1.17 | 1.28 | 0.73 | 1.19 | 3.34 | 0.61 | 1.54 | 1.58 | 0.75 | 1.17 | 1.00 | 0.90 | 0.52 | 0.99 | 0.93 | 0.61 | 1.52 | 0.97 | 1.17 | 1.01 | 0.94 | 0.35 | 0.57 | 0.48 | 0.91 | 1.25 | 1.70 | HNRNPH2 |
| 7.31 | 0.76 | 0.95 | 0.93 | 0.98 | 0.97 | 0.79 | 0.79 | 1.04 | 3.81 | 0.65 | 1.23 | 1.23 | 0.57 | 1.62 | 1.54 | 0.87 | 1.52 | 0.93 | 1.13 | 0.56 | 2.81 | 0.58 | 1.68 | 0.80 | 1.30 | 1.39 | 0.47 | 0.10 | 0.82 | 0.79 | 0.80 | HNRNPH3 |
| 3.62 | 0.97 | 0.01 | 0.78 | 0.67 | 1.04 | 0,43 | 0.88 | 0.00 | 3.86 | 0.99 | 1.51 | 1.75 | 1.01 | 1.55 | 1.54 | 0.83 | 1.50 | 0.00 | 1.41 | 0.62 | 1.33 | 0.58 | 1.20 | 1.25 | 0.94 | 0.73 | 0,47 | 0.00 | 0.87 | 0.72 | 1.90 | HNRNPL |
| 2.76 | 0.94 | 0.92 | 0.96 | 0.56 | 0.70 | 0.62 | 0.74 | 1.28 | 3.95 | 0.65 | 1.29 | 1.27 | 1.30 | 1.70 | 0.96 | 0.83 | 1.43 | 1.00 | 1.14 | 0.54 | 1.66 | 0.90 | 1.28 | 0.85 | 1.04 | 0.44 | 1.06 | 0,24 | 0.87 | 1.40 | 2.61 | HNRNPLL |
| 5.58 | 0.90 | 0.79 | 0.65 | 0.52 | 0.98 | 0.38 | 0.85 | 0.92 | 3.70 | 0.00 | 1.18 | 1.20 | 1.42 | 1.72 | 1.39 | 0.90 | 1.30 | 1.00 | 0.82 | 0.63 | 3.10 | 0.51 | 1.06 | 1.02 | 0.90 | 0.95 | 0.57 | 0.21 | 0.66 | 0.94 | 0.85 | HNRNPM |
| 5.17 | 0.98 | 0.92 | 1.04 | 1.16 | 0.76 | 0.79 | 0.87 | 1.11 | 4.18 | 0.71 | 1.59 | 0.92 | 1.57 | 1.61 | 1.58 | 0.94 | 0.89 | 0.97 | 1.31 | 0.59 | 1.93 | 0.80 | 1.66 | 1.09 | 0.96 | 0.42 | 0.91 | 0.22 | 0.94 | 1.01 | 1.21 | HNRNPR |
| 4.65 | 1.01 | 0.91 | 0.77 | 1.12 | 0.98 | 0.75 | 0.81 | 1.07 | 4.18 | 0.55 | 1.33 | 1.21 | 1.45 | 1.46 | 1.30 | 0.86 | 1.73 | 0.97 | 1.28 | 1.49 | 1.57 | 0.61 | 1.38 | 1.23 | 0.98 | 0.40 | 0.55 | 0.20 | 1.05 | 1.00 | 1.00 | HNRNPU |
| 3.72 | 1.00 | 0.75 | 0.88 | 0.47 | 0.88 | 1.21 | 0.93 | 1.10 | 2.04 | 0.78 | 1.28 | 1.65 | 1.42 | 1.35 | 1.32 | 0.87 | 1.75 | 0.96 | 1.18 | 0.99 | 0.99 | 0.76 | 1.13 | 1.41 | 1.06 | 0.44 | 0.69 | 0.45 | 1.12 | 0.95 | 0.73 | HNRNPUL1 |
| 4.52 | 1.00 | 0.63 | 0.99 | 1.16 | 1.00 | 0.88 | 0,85 | 1.05 | 2.11 | 0.83 | 0.91 | 1.87 | 0.75 | 1.37 | 1.30 | 0.96 | 1.50 | 0,89 | 1.20 | 1.47 | 2.48 | 1.19 | 1.24 | 1.04 | 1.05 | 0.33 | 0.40 | 0.27 | 1.00 | 0.73 | 2.79 | HNRNPUL2 |
| 2.62 | 0.99 | 1.21 | 1.67 | 0.98 | 1.18 | 1.23 | 0.84 | 1.04 | 3.91 | 0.54 | 1.04 | 1.49 | 0.99 | 1.06 | 1.37 | 0.97 | 1.08 | 1.13 | 0.90 | 1.71 | 2.75 | 0.58 | 1.15 | 0.91 | 1.00 | 1.01 | 0.74 | 0.39 | 1.02 | 1.15 | 1.20 | HNRNPUL2-BSCL2 |
A
High Risk
Low Risk
P >= 0.05
C
Group Event
2
Group
High
Gender
1
Low
Age
HNRNPCL1
Event
0
Alive
HNRNPA2B1
Dead
Gender
HNRNPDL
-1
Male Female
HNRNPH3
-2
Age
90
HNRNPUL2-BSCL2
HNRNPAO
30
ACC BLCA
BRCA
CESC
CHOL
COAD
DLBC
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
HNRNPM
B
Hazard Ratio (95% CI)
Hazard Ratio (95% CI)
Cancer Patients
HNRNPF
ACC
79
5.82(2.50-$1.30)
3.71(1.74-7.89)
HNRNPH2
BLCA 411
0.81(0.60- 1.08)
0.91(0.68-1.21)
BRCA 1104
0.73(0.53- 1.00)
0.73(0.53- 1.00)
HNRNPR
CESC 306
1.07(0.68- 1.70)
1.13(0.71- 1.80)
CHOL
HNRNPU
36
+1.21(0.48-3.06)
0.51(0.20- 1.28)
COAD
471
1.03(0.69- 1.53)
1.13(0.76- 1.68)
DLBC
48
0.74(0.20-2.72)
0.97(0.24-3.86)
Strata + C1
+ C2
GBM
168
0.84(0.60- 1.17)
0.67(0.48-0.94)
HNSC
502
1.12(0.86- 1.46)
1.30(0.99- 1.69)
D
KICH
65
3.67(0.99-13.55)
+4.03(1.09-14.93)
1.00
KIRC
535
0.73(0.54- 0.99)
0.78(0.58- 1.06)
KIRP
289
2.05(1.13- 3.71)
1.89(1.05-3.41)
LAML
151
0.84(0.54- 1.30)
1.38(0.89-2.14)
Survival probability
0.75
LGG
529
0.64(0.46- 0.90)
1.09(0.77-1.52)
LIHC
374
1.40(0.99- 1.98)
1.42(1.01-2.01)
LUAD 526
1.41(1.06- 1.89)
1.85(1.39-2.47)
LUSC 501
0.80(0.61- 1.05)
0.94(0.72- 1.23)
0.50
MESO
86
0.96(0.61- 1.53)
1.20(0.75- 1.90)
OV
379
0.93(0.72- 1.20)
0.96(0.74-1.24)
PAAD
178
1.56(1.03- 2.35)
2.06(1.36-3.12)
0.25
P = 0.003
PCPG
183
1.59(0.40-6.36)
0.93(0.23-3.72)
PRAD 499
1.33(0.38-4.60)
2.08(0.60-7.22)
Hazard Ratio = 0.5
READ
167
0.81(0.38- 1.74)
0.99(0.46-2.10)
95% CI: 0.35 - 0.73
SARC
263
1.41(0.95-2.09)
1.70(1.14-2.52)
SKCM
471
1.02(0.78- 1.33)
1.11(0.85- 1.45)
0.00
STAD
375
1.15(0.83- 1.59)
0.94(0.68-1.30)
0
1000
2000
3000
4000
TGCT
156
2.28(0.31-16.46)
0.38(0.05-2.73)
Time
THCA
510
0.54(0.20- 1.44)
0.28(0.10-0.74)
THYM
119
0.18(0.05-0.68)
0.13(0.03-0.47)
Number at risk
UCEC
548
0.58(0.38- 0.87)
0.81(0.54- 1.22)
Strata
UCS
56
0.79(0.40- 1.55)
1.00(0.51- 1.95)
C1
426
243
90
27
3
UVM
80
0.68(0.30- 1.55)
1.45(0.64-3.31)
C2
107
65
32
13
0
0
1000
2000
3000
4000
0
0.5
1
1.5
2
0
0.5
1
1.5
2
Time
HNRNPA1
HNRNPC
we also found that hnRNPs might work together in carcinogen- esis as significant correlations were detected such as HNRNPL- HNRNPAB, HNRNPUL2-HNRNPA0 and HNRNPAB-HNRNPLL. As for immune cell infiltrations, the most relevant immune cells of hnRNPs included T help cells, NK cells, CD8 positive T cells and neutrophils. Genes of HNRNPH2, HNRNPU, HNRNPDL and HNRNPA0 all demonstrated significant correlation with immune cell infiltration. HNRNPU has been found to interact with NF-KB- responsive Long Non-coding RNA FIRRE to modulate the mRNAs of certain inflammatory genes in innate immune system.3º The close relation between alternative splicing regulator hnRNPs and immune system might offer new idea for future studies on immune therapy against cancer.
Pan-cancer prognostic analysis of hnRNP genes suggested that most hnRNPs were associated with worse survival of can- cer patients in cancers including ACC, LIHC and LUAD. However, hnRNPs predicted better prognosis in cancers such as KIRC and THYM. In addition, HNRNPA1 predicted worse prognosis of can- cers including ACC, KIRP, LUAD and PAAD but was associated with better survival in cancers of KIRC, LGG, THYM and UCEC. These results suggested that HNRNPA1 might exert obviously dif- ferent prognostic effect across various cancer types. Previously, high HNRNPUL2 expression has been reported to predict poor survival of multiple cancers. 31 Significant association of HNRNPH expression and prognosis of colorectal cancer patients has been suggested by tissue microarray.32 Oral squamous cell carcinoma
patients with increased HNRNPD expression significantly cor- related with shorter recurrence-free survival.33 These findings indicated that hnRNPs were closely implicated in the prognosis of various cancers. As many hnRNP genes demonstrated influence on KIRC prognosis, we further performed clustering analysis of prognosis-related hnRNP genes. The prognosis analysis of the cluster C1 and C2 suggested that C2 cluster was significantly as- sociated with better survival compared with C1 cluster, indicating that hnRNP genes might be used as a prognostic predictor of can- cer in the future.
5 CONCLUSION
In summary, our study systematically demonstrated the expres- sion, mutation, copy number variation, functional pathways and prognostic value of alternative splicing regulator hnRNPs across a series of cancers. The expressions of hnRNPs suggested signifi- cant association with oncogenic pathways including protein se- cretion, mitotic spindle, G2/M checkpoint, DNA repair, IL6/JAK/ STAT3 signal and showed correlation with immune regulations of T help cells, NK cells, CD8 positive T cells and neutrophils. The evaluation of hnRNPs distributions could predict prognosis of can- cer patients. These findings provide novel evidence for the inves- tigation of hnRNPs in the development and therapy of cancer in the future.
CONFLICT OF INTEREST
All of the authors declare that there is no conflict of interest.
AUTHOR CONTRIBUTION
Hao Li: Formal analysis (equal); Writing-original draft (equal). Jingwei Liu: Formal analysis (equal); Writing-original draft (equal). Shixuan Shen: Investigation (lead); Methodology (lead). Di Dai: Validation (equal); Visualization (equal). Shitong Cheng: Data curation (equal); Investigation (equal). Xiaolong Dong: Formal analysis (supporting); Visualization (supporting). Liping Sun: Investigation (equal); Writing- review & editing (equal). Xiaolin Guo: Project administration (equal); Writing-review & editing (equal).
DATA AVAILABILITY STATEMENT
All of the data in this article were used the TCGA datasets (https:// www.cancer.gov/about-nci/organization/ccg/research/structural -genomics/tcga).
ORCID
İD
Xiaolin Guo https://orcid.org/0000-0001-8197-690X
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SUPPORTING INFORMATION
Additional supporting information may be found online in the Supporting Information section.
How to cite this article: Li H, Liu J, Shen S, et al. Pan-cancer analysis of alternative splicing regulator heterogeneous nuclear ribonucleoproteins (hnRNPs) family and their prognostic potential. J Cell Mol Med. 2020;24:11111-11119. https://doi. org/10.1111/jcmm.15558