Pan-Cancer Analysis of hnRNPAB: Implications in Tumor Progression, Prognosis, and Immune Microenvironment Modulation

doi:10.21203/rs.3.rs-5211868/v1

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Pan-Cancer Analysis of hnRNPAB: Implications in Tumor Progression, Prognosis, and Immune Microenvironment Modulation

https://doi.org/10.21203/rs.3.rs-5211868/v1

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Background: hnRNPAB, a member of the hnRNP protein family, is involved in mRNA cytoplasmic localization, transport, and the regulation of transcription, metabolism, and splicing. It is associated with malignant progression and metastasis in liver cancer and lung adenocarcinoma. However, a systematic pan-cancer analysis exploring its role in diagnosis, prognosis, and immune prediction is lacking.

Methods: This study evaluated hnRNPAB expression across 33 cancers and its association with immune infiltration using UCSC Xena, TIMER, GEPIA, BioGPS, and ARCHS4 databases. Methylation levels were analyzed using UALCAN and MethSurv. Survival analysis was performed with GEPIA and MethSurv platforms, and mutation analysis was conducted via cBioPortal. Protein interaction networks were constructed using STRING and Cytoscape, and functional enrichment analyses were performed with KEGG and GSVA. Immune infiltration was assessed using TIMER, CIBERSORT, and quanTIseq, and the relationship between hnRNPAB and cancer-associated fibroblasts (CAFs) was analyzed using TIMER2.0.

Results: hnRNPAB was significantly overexpressed in various cancers, with high expression correlating with poor prognosis. Mutation analysis revealed that amplification mutations of hnRNPAB were associated with worse survival rates. Low methylation of hnRNPAB was linked to cancer progression. Additionally, hnRNPAB was involved in cell cycle regulation, mTORC1, and PI3K-AKT signaling pathways. Immune cell infiltration analysis demonstrated a significant association between hnRNPAB and CAF infiltration, affecting immune therapy outcomes.

Conclusion: This study highlights the association between hnRNPAB overexpression and poor prognosis across multiple cancers, particularly in kidney and liver cancers. hnRNPAB promotes tumor growth and metastasis by regulating immune cell infiltration and CAF activity. The study also explores its mutations and methylation status, suggesting its potential as a therapeutic target or biomarker in cancer metabolism reprogramming and immune evasion.

hnRNPAB

pan-cancer analysis

prognosis

immune prediction

therapeutic target

Heterogeneous nuclear ribonucleoproteins (hnRNPs), a group of nuclear proteins binding to RNA polymerase II transcripts [1], are RNA-binding proteins essential in numerous biological processes. The hnRNP family comprises over 20 members, labeled from A to U according to molecular weight, with primary members including hnRNPA, hnRNPA/B, and hnRNPC [2]. Each hnRNP protein contains at least one RNA binding domain (RBD) or RNA recognition motif (REM), hnRNA K homology domain (KH), and an Arginine/Glycine-rich domain (RGG), all of which posse RNA binding properties [3]. As key modulators of alternative splicing, these proteins bind to RNA through various RBDs, promoting the maturation of pre-mRNA into functional mRNA and stabilizing mRNA translocation [4]. Furthermore, hnRNPs are involved in alternative splicing [5], polyadenylation [6, 7] of pre-mRNA, regulation of mRNA stability [8], nucleocytoplasmic transport [9], and DNA replication and recombination [1]. Theses evidences highlight the complexity and diversity of hnRNPs functions. Current data indicate a strong correlation between hnRNPs and the malignant biological behavior of various tumors [10]. Notably, hnRNPA1 exhibits significantly elevated expression in lung cancer samples, correlating with tumor proliferation [11]. Similarly, hnRNP A2/B1 is expressed in numerous malignancies, such as pancreatic, lung, liver, gastric, breast, and prostate cancers, and is also associated with conditions including tumors, neurodegenerative disorders, and autoimmune diseases [12]. hnRNPC is involved in the regulation of oncogenes in breast cancer [13], while hnRNPE1 suppresses HPV16E6 oncogene expression, thereby inhibiting cervical carcinogenesis [14]. Furthermore, various hnRNPs are linked to poor prognosis in cancer patients [15, 16]. This connection has prompted the investigation of hnRNPs as therapeutic targets, with some small molecule compounds reported to target these proteins [17]. The small molecule inhibitor VPC-80051 targets hnRNPA1, significantly reducing androgen receptor AR-V7 levels in prostate cancer, thereby diminishing drug resistance. Notably, VPC-80051 is the first small molecule to specifically target hnRNPA1 [18]. Quercetin can bind to the C-terminal region of hnRNPA1, disrupting its interaction with transportin 1 (Tnpo1) and inducing apoptosis in tumor cells [19]. In addition to hnRNPA1, hnRNPA2/B1 is identified as a potential therapeutic target for tumor treatment, highlighting the therapeutic potential of hnRNPs in cancer [20]. The hnRNP protein superfamily exhibits extensive biological functions, with many members implicated in tumorigenesis and progression. However, the roles and mechanisms of some hnRNP family members remain poorly understood. Thus, this study primarily focuses on hnRNPAB.

HnRNPAB, a extensively expressed member of the hnRNP protein family [21], comprises four subtypes: hnRNP A1, hnRNP A2/B1 (hnRNP A2), hnRNP A3, and hnRNP A0, all located in the nucleus of eukaryotic cells. Its primary functions encompass mRNA cytoplasmic localization and transport, and it plays a role in regulating transcription, metabolism, splicing, and the expression of various RNA molecules [1]. Additionally, hnRNPAB is instrumental in the pathogenesis and progression of numerous malignant tumors [22]. It modulates transcriptional and translational processes within cells. Studies have shown that specific hnRNPAB proteins bind to multiple promoter sequences, including the c-myc promoter region, APOE, thymidine kinase (TK), genes encoding G-fibrinogen, and the vitamin D receptor, thereby influencing transcriptional regulation [23]. Research conducted by Yang Y et al. [24] first identified hnRNPAB as a transcriptional inhibitor of hepatocellular carcinoma through its direct interaction with the lncRNA-ELF209 promoter region. In immortalized mouse GT1-7 cells, hnRNPAB was shown to bind to GnRH1, leading to the suppression of hormone transcription and affecting reproductive processes [25]. In rat breast cancer cells, hnRNPAB was found to associate with the positive transcriptional regulatory element HRE on the proto-oncogene H-ras promoter, thereby augmenting Ha-ras transcriptional activity [26]. On the translational front, the knockdown of the hnRNPAB p42 isoform triggered the premature translation of protamine 2 (PRM2) mRNA in mouse spermatids, which consequently disrupted spermatogenesis [27].

The elevated expression of hnRNPAB in metastatic cancers [28] indicates its involvement in tumor metastasis. Similarly, Zhou et al. demonstrated that hnRNPAB activated Snail, triggering epithelial-mesenchymal transition (EMT) and promoting liver cancer metastasis. RNA interference-mediated silencing of Snail, an EMT transcription factor, reduced hnRNPAB's role in promoting lung metastasis in liver cancer [29]. Besides promoting tumor cell migration, hnRNPAB also contributes to cell proliferation. In prostate cancer, hnRNPAB's activation of cell cycle-related genes has been linked to increased growth and metastasis during disease progression [22]. Furthermore, hnRNPAB upregulates c-Myc transcription and accelerates the G1/S cell cycle process, thereby promoting lung adenocarcinoma cell proliferation [30]. Suppressing hnRNPA2/B1 expression in cervical cancer cells hinders proliferation and invasion, induces cell cycle arrest, and triggers apoptosis through the PI3K/AKT signaling pathway activation [31]. Studies on Marek's disease virus reveal that decreased hnRNPAB expression significantly enhances cell survival rates [32]. Utilizing siRNA to knock down hnRNPAB in prostate cancer significantly reduces V16 cell proliferation [22]. Overexpression of hnRNPAB also augments the stemness of colorectal cancer cells, with nude mouse tumor models showing that elevated hnRNPAB levels considerably increase colorectal cancer tumorigenicity [33]. hnRNPAB and its subsets are significantly linked to poor prognosis in various cancer types [34]. In HBV-related liver cancer, hnRNPA1 expression is correlated with patient prognosis, where elevated levels are indicative of worse prognosis [35]. A recent study highlights the overexpression of hnRNPA2/B1 in colorectal cancer patients, with notable expression differences across stages, implicating hnRNPA2/B1 involvement in cancer progression and its prognostic significance [36]. hnRNPAB, a broadly expressed family member, is involved in regulating mRNA transport, metabolism, and splicing [37, 38], influencing tumorigenesis and progression. Comprehensive understanding of hnRNPAB’s biological properties and intricate regulatory roles remains a focal point for future investigations.

2.1. Data Preparation and Analysis

HnRNPAB expression levels were evaluated using UCSC Xena (http://xena.ucsc.edu/) for gene expression and survival/clinical data from TCGA samples across 33 cancer types. Differences in hnRNPAB expressions between tumor and adjacent normal tissues were assessed via the TIMER database (https://cistrome.shinyapps.io/timer). Differential gene expression across 31 cancer types was analyzed using the GEPIA database (http://gepia.cancer-pku.cn), which integrates data from TCGA and GTEx. Gene expression data for cell lines were obtained from the BioGPS public database (http://biogps.org/goto=welcome). Frequency distribution data for hnRNPAB in tissues and cancer cell lines were sourced from the ARCHS4 dataset (https://maayanlab.cloud/archs4/index.html), detailing hnRNPAB expressions in these contexts. The TISIDB portal (http://cis.hku.hk/TISIDB/index.php) was used to explore correlations between gene expression and immune infiltration levels [39]. Pan-cancer data were obtained from the CPTAC data portal. UALCAN provided extensive cancer transcriptome data (http://ualcan.path.uab.edu/) [40]. TCGA DNA methylation analysis was conducted using MethSurv (https://biit.cs.ut.ee/methsurv/). The STRING database (https://string-db.org/) was employed to examine the protein interaction network.

2.2. Survival Analysis

GEPIA, an interactive online platform integrating tumor sample data from the TCGA project and normal sample data from TCGA and GTEx projects, was utilized to evaluate hnRNPAB expression's impact on overall survival (OS) across 33 cancer types. The analysis also examined the correlation between hnRNPAB expression and various prognostic indicators, including OS, DSS, PFS, and disease-free survival. Statistical significance was determined with a p-value threshold of < 0.05. Survival analysis data were summarized and visualized using R software (version 4.2.1, www.R-project.org) with the "survival rate," "survminer," and "forestplot" packages. MethSurv (https://biit.cs.ut.ee/methsurv/) served as a web-based tool for survival analysis based on CpG methylation patterns [41].

2.3. Mutation Analysis

We used the cBioPortal database (https://www.cbioportal.org/) to analyze the gene mutations of hnRNPAB across different cancers. We obtained the frequency and types of genomic alterations in hnRNPAB in various tumors and performed mutation counting. We also acquired copy number data for hnRNPAB across different tumors for differential analysis and determined the correlation between hnRNPAB expression and copy number values using Spearman or Pearson tests. Additionally, we utilized the "Mutations" module of the cBioPortal database to identify the mutation sites and types of the hnRNPAB gene.

2.4. Methylation Analysis

To analyze the relationship between hnRNPAB and its methylation levels, we obtained methylation data from the UALCAN database (https://ualcan.path.uab.edu/index.html) and compared the methylation levels at each site between normal and tumor tissues across different cancers. We also analyzed the correlation between hnRNPAB methylation levels and copy number changes using the MEXPRESS database (https://mexpress.ugent.be).

2.5. Gene Expression and Functional Analysis

We used the STRING database (https://cn.string-db.org/) to obtain genes with expression patterns similar to hnRNPAB and constructed a protein-protein interaction (PPI) network, which was visualized using Cytoscape software. Subsequently, we performed Gene Set Variation Analysis (GSVA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, with p-values < 0.05 and q-values < 0.05 as the criteria for identifying significantly enriched pathways. This analysis revealed the major enriched signaling pathways and biological functions associated with different gene expression patterns.

2.6. Immune Infiltration Analysis

We employed multiple computational methods, including TIMER, CIBERSORT, quanTIseq, xCell, MCP-counter, and EPIC, to assess immune cell infiltration levels in 33 types of cancers. The correlation between hnRNPAB expression and various immune cell infiltration levels was evaluated using the R packages "ggplot2" and "ggpubr." Additionally, we specifically investigated the relationship between tumor-infiltrating lymphocytes (TILs) and hnRNPAB expression, and analyzed the correlation between TIL abundance and hnRNPAB copy number or methylation. Through these analyses, we gained insights into the interaction between hnRNPAB expression and the immune system. Finally, we analyzed the relationship between hnRNPAB expression and tumor mutation burden (TMB) and microsatellite instability (MSI), and calculated TIDE scores for LIHC and KIRP in the TCGA cohort to evaluate whether hnRNPAB expression could serve as a predictive biomarker for immunotherapy effectiveness and assess its potential as a prognostic and clinical biomarker.

2.7. Cancer-Associated Fibroblasts (CAFs) Analysis

Cancer-associated fibroblasts (CAFs) are a crucial component of the tumor microenvironment. They play an active role in cancer onset, progression, metastasis, and treatment resistance, rather than being passive participants. Therefore, we analyzed the relationship between hnRNPAB expression and CAF infiltration in different cancers, and compared the distribution of CAFs among different mutation types. Finally, we used the TIMER2.0 online database (http://timer.cistrome.org) to analyze the correlation between CAF markers and hnRNPAB expression.

2.8. Statistical Analysis

Continuous variables between two groups were compared using the Mann-Whitney test, and comparisons among multiple groups were performed using the Kruskal-Wallis test. Prognostic differences between two groups were analyzed using the Kaplan-Meier method and log-rank test. The correlation between continuous variables was evaluated using the Spearman test. All statistical analyses were conducted using R software or online tools. A p-value < 0.05 was considered statistically significant.

3.1. Expression of hnRNPAB in Pan-Cancer Tissues

The hnRNPAB mRNA expression levels across various cancer tissues were presented in Fig. 1. TIMER database analysis identified significant overexpression in 16 cancer types: BLCA (bladder urothelial carcinoma), BRCA (breast invasive carcinoma), CESC (cervical squamous cell carcinoma and endocervical adenocarcinoma), CHOL (cholangiocarcinoma), ESCA (esophageal carcinoma), GBM (glioblastoma multiforme), HNSC (head and neck squamous cell carcinoma), KIRC (kidney renal clear cell carcinoma), KIRP (kidney renal papillary cell carcinoma), LIHC (liver hepatocellular carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), PRAD (prostate adenocarcinoma), STAD (stomach adenocarcinoma), THCA (thyroid carcinoma), and UCEC (uterine corpus endometrial carcinoma). hnRNPAB mRNA demonstrated low expression in pheochromocytoma and paraganglioma (PCPG) (Fig. 1A). Analysis from the GEPIA database, which integrated TCGA and GTEx data, revealed hnRNPAB expression in 31 cancer types, showing elevated levels in 15 of them (Fig. 1B). Proteomics data from the CPTAC dataset further confirmed hnRNPAB overexpression in nine cancer types and their paired non-tumor samples (Figure S1). Figure 2 illustrated hnRNPAB expression across paired pan-cancer samples, with increased levels observed in 15 cancer types. Expression patterns of hnRNPAB in normal tissues and cells were detailed in Figs. 3A and 3B. The BioGPS database was utilized to examine hnRNPAB expression in both tumor and normal cell lines (Figs. 3C and 3D), showing significant overexpression in cancer cell lines (Fig. 3D). Conversely, hnRNPAB expression in normal cells was predominantly high in immune cells, such as CD56 + NK cell lines (Fig. 3C).

3.2. Effect of hnRNPAB Expression on the Prognosis of Cancer Patients

Patient survival data from TCGA were analyzed using R software, revealing a correlation between high hnRNPAB expression and poor prognosis in several cancers, including adrenocortical carcinoma (ACC, P = 0.044), ESCA (P = 0.047), kidney chromophobe (KICH, P = 0.017), KIRC (P < 0.001), KIRP (P = 0.026), acute myeloid leukemia (LAML, P = 0.003), LIHC (P = 0.016), LUAD (P = 0.035), uterine carcinosarcoma (UCS, P = 0.030), and uveal melanoma (UVM, P < 0.001) (Fig. 4A). Cox regression analysis presented in Fig. 4B corroborated these results, linking high hnRNPAB expression to poor prognosis in 9 of 33 cancers. Furthermore, Figures S2 and S3 showed that hnRNPAB overexpression significantly correlated with unfavorable disease-specific survival (DSS) and progression-free survival (PFS). Thus, hnRNPAB emerges as a potential prognostic marker associated with adverse outcomes in cancer patients.

3.3. Genetic Variation of hnRNPAB in Pan-Cancer

The mutation frequency of hnRNPAB was assessed in 10,953 patients/10,967 samples from The Cancer Genome Atlas (TCGA) Pan-Cancer Study and the CBioPortal dataset. Genomic alterations in hnRNPAB (1.8%) in pan-cancer samples were presented in Fig. 5A. “Amplification” mutations were predominantly observed in KIRC and ACC, with occurrences of 7.24% and 4.4%, respectively, as shown in Fig. 5B. Mutation frequencies in UCEC, sarcoma, and non-small-cell lung carcinoma were recorded at 3.92%, 2.75%, and 2.47%, respectively (Fig. 5C, D). Figure 5E illustrated the mutation types and statuses of hnRNPAB in 9,889 pan-cancer samples. A significant positive correlation between hnRNPAB expression and copy number was identified (P < 0.05) (Fig. 5F). Figure 5G illustrated the mutation types and copy numbers across 30 cancer types. Amplification of hnRNPAB was associated with adverse tumor prognosis. The relationship between hnRNPAB gene alterations and clinical outcomes was investigated in patients with different types of cancer. Data indicated that patients with hnRNPAB alterations in ACC, KIRC, LGG, LUAD, PRAD, and SKCM had poorer overall survival compared to those without such alterations (P < 0.05), highlighting a correlation between hnRNPAB mutations and unfavorable prognosis (Figure S4).

3.4. Methylation of hnRNPAB in Pan-Cancer

Methylation significantly influences tumorigenesis and progression [21]. The UALCAN database provided methylation data, illustrating reduced hnRNPAB methylation levels in 12 cancer types compared to normal tissues (Fig. 6A). The MethSurv tool for survival analysis facilitated a comprehensive pan-cancer analysis of hnRNPAB gene methylation, pinpointing CpG sites with the highest prognostic value for cancer survival. Kaplan-Meier plots (Fig. 6B) illustrated survival disparities between hypomethylated and hypermethylated patient cohorts. Specifically, the CpG site "cg06538757" was associated with poor prognosis in multiple cancer types (P < 0.05, HR > 1.00). The MEXPRESS database (Figure S5) identified a significant correlation between hnRNPAB methylation levels and copy number alterations in BLCA, COAD, ESCA, KIRP, LUAD, LUSC, PAAD, READ, and UCEC samples, with r of 0.469, 0.394, 0.518, 0.385, 0.440, 0.538, 0.224, 0.458, and 0.338, respectively. This evidence indicates that hnRNPAB methylation substantially impacts the onset and progression of various tumors.

3.5. Functional Analysis Based on hnRNPAB Expression

Gene Set Variation Analysis (GSVA) identified potential signaling pathways linked to hnRNPAB across various cancer types (Fig. 7). GSEA pathway analysis demonstrated a marked enrichment of up-regulated genes in cell cycle-related pathways, including "MYC TARGETS V1 and V2," "G2M checkpoint," and "E2F_TARGETS." Furthermore, significant enrichment was observed in classical signaling pathways such as MTORC1 and PI3K-AKT-mTOR, as well as the glycolysis pathway. MTORC1 activation influences multiple metabolic processes, including mitochondrial biogenesis for oxidative phosphorylation, de novo nucleotide synthesis, and lipid production. The unfolded protein response (UPR) plays a vital role in tumor growth and migration. Consequently, targeting the UPR by developing small molecule inhibitors for upstream signaling components like PERK, IRE1, and ATF6, or via inhibiting cytoplasmic protein degradation, may offer novel therapeutic strategies for cancer treatment. Additionally, down-regulated genes associated with KRAS signaling and metabolic pathways showed significant enrichment.

3.6. Correlation Between Different Immune Cell Infiltration Levels and hnRNPAB Gene Expression

The components and abundance of immune cells within the tumor microenvironment (TME) significantly impact tumor progression and immunotherapy efficacy. Tumor-infiltrating lymphocytes (TILs) are especially important in cancer progression. An inverse relationship was observed between hnRNPAB expression and immune infiltration by CD8 + T cells, CD4 + T cells, and NK cells across various cancers (Figures S6, S7). Furthermore, hnRNPAB expression positively correlated with cancer-associated fibroblast (CAF) infiltration in KIRP, LIHC, THCA, and UVM (Rho = 0.251; 0.306; 0.319; and 0.486) (Fig. 8). Figure S8 depicted the distribution of hnRNPAB-related immune infiltration in mutant versus wild-type tumors across various cancer types. Elevated levels of immune infiltration were observed in mutant tumors of READ, STAD, and UCEC. This overexpression of hnRNPAB in these tumors may contribute to a reduction in CD8 + T cells and an increase in CAFs, promoting tumor progression. Figure S9 illustrated the correlation between TIL abundance and hnRNPAB copy number or methylation. A positive association between TIL abundance and both gene copy number variation and methylation level was identified in LUSC, indicating a potential link between TIL abundance and the copy number or methylation status of hnRNPAB in LUSC patients.

3.7. Predictive Value of hnRNPAB in Immunotherapy Response

Tumor mutational burden (TMB) and microsatellite instability (MSI) are essential tumor characteristics. TMB, measured as mutations per megabase (mut/Mb), is a promising biomarker for immunotherapy. MSI, reflecting DNA mismatch repair dysfunction, leads to replication errors and alterations in microsatellite sequence length or base composition, serving as a prognostic marker in cancer patients. The relationship between hnRNPAB expression and TMB and MSI was analyzed, revealing a positive correlation between hnRNPAB expression and TMB across 15 cancer types, including

BLCA, BRCA, and LUSC. Similarly, hnRNPAB expression positively correlated with MSI in 12 cancer types, such as ESCA, HNSC, and KIRC (Figure S10). Additionally, hnRNPAB expression showed the strongest correlation with KIRC stage/grade, highlighting its potential as a prognostic and clinical biomarker.

Given the association between HNRNPAB expression and immune infiltration, we analyzed the association of hnRNPAB with TIDE, a known predictor of immunotherapy efficacy, to predict its potential in immunotherapy response. In liver cancer and KIRP, elevated hnRNPAB expression corresponded with higher TIDE scores in the TCGA cohort (Fig. 9), indicating that patients with lower hnRNPAB expression may achieve better immunotherapy outcomes. Therefore, hnRNPAB expression serves as a promising biomarker for predicting immunotherapy response and optimizing treatment strategies in patients with LIHC and KIRP.

3.8. Gene Expression

Figure S11A presented 20 genes with expression patterns analogous to hnRNPAB in the TCGA tumor dataset. NOP16 exhibited the highest concordance with hnRNPAB expression (PCC = 0.68), followed by CCNB1 (PCC = 0.66). NOP16 encodes a nucleolar protein induced by estrogen and Myc protein, serving as a marker of poor survival in breast cancer patients. Cyclin B1 (CCNB1), a key regulator of the cell cycle, especially during the G2/M phase, plays an important role in initiating mitosis. Elevated CCNB1 expression is documented in various cancers, such as non-small cell lung cancer, breast cancer, gastric cancer, and colorectal cancer. In liver cancer, its high expression correlates with poor prognosis. KEGG enrichment analysis of these 20 genes (Figure S11B) and Metascape database results indicated significant enrichment in pathways involving eukaryotic ribosome synthesis, cell cycle regulation, and movement proteins.

3.9. Correlation Between Marker Genes of Cancer-Related Fibroblasts and hnRNPAB Expression

TME markedly impacts cancer initiation and progression, with CAFs being a predominant component of the tumor stroma. The transition of normal fibroblasts to activated CAFs, induced by tumor cell recruitment and transformation, underpins cancer progression. CAFs secrete cytokines and chemokines that modulate anti-tumor immunity and facilitate tumor growth and progression through mechanisms such as neovascularization, tumor cell proliferation, survival, migration, invasion, and treatment resistance. In some cancers, CAFs may serve as potential biomarkers and therapeutic targets. In liver cancer and KIRP, hnRNPAB expression positively correlates with CAF infiltration. CAF markers include FAP, α-SMA, Vimentin, PDGFR-α/PDGFR-β, and fibroblast-specific protein 1. The TGFβ pathway's regulation of CAF function is well-documented. The TIMER2.0 online database analyzed correlations between CAF markers and hnRNPAB expression. In liver cancer, markers with significant statistical correlations (R values > 0.3) revealed positive associations with CAV1, CD101, COL1A1, COL1A2, FAP, TGF-β3, and VIM (Fig. 10). In KIRP, markers with significant correlations (R values > 0.2) indicated positive associations with VIM, CD101, COL1A1, TGF-β2, TGF-β3, EPCAM, and CAV1 (Figure S12).

Cancer arises from cellular biological anomalies, where genomic mutations disrupt normal growth regulation, leading to uncontrolled cell proliferation [42]. Aberrantly proliferating cells deplete nutrients and invade surrounding tissues, significantly impairing quality of life and posing a life-threatening risk. The 2022 global cancer statistics estimate 20 million new cases and 9.7 million cancer-related deaths worldwide [43]. Cancer is a major global public health problem. Therefore, it is necessary to understand the biological characteristics and physiological processes of tumors.

hnRNPs, as RBPs, exhibit functional diversity and complexity, playing a vital role in cellular nucleic acid metabolism [44]. Among them, hnRNPAB is a key member, though data on hnRNPA3 and hnRNPA0 remain sparse. In contrast, hnRNPA1 and hnRNPA2/B1 are notably expressed and actively involved in mRNA translation [46] and splicing [47], which has led to a research emphasis on these proteins. Recent studies indicate that hnRNPAB is expressed in gastric, colorectal, breast, and liver cancers, contributing significantly to tumorigenesis and progression [48–50].

The TIMER and GEPIA databases were employed to assess hnRNPAB expression across various cancer and normal tissues. Analysis of BRCA, CESC, CHOL, GBM, and STAD indicated significantly elevated hnRNPAB levels in cancerous tissues compared to normal counterparts. This overexpression in multiple cancer types was corroborated by the CPTAC proteomics dataset and the BioGPS database. Notably, in normal cells, hnRNPAB is predominantly expressed at high levels in immune cells, particularly CD56 + NK cell lines. CD56 + NK cells, essential for immune surveillance and response, swiftly target infected or transformed cells [51]. This observation implies that hnRNPAB may influence immune cell activation, differentiation, and cytotoxicity, highlighting its role in immune regulation.

To assess the clinical significance of hnRNPAB expression in cancer patients, survival and Cox regression analyses were performed using the TCGA dataset. The findings indicated that elevated hnRNPAB expression is significantly associated with poor survival outcomes in tumors such as KICH, KIRC, KIRP, LAML, LIHC, LUAD, and UVM. This correlation suggests that hnRNPAB expression could serve as a prognostic marker in cancer. Notably, hnRNPAB expression impacts the prognosis of various renal cancers. Existing evidence has shown that abnormal expression of HNRNP family proteins can lead to renal damage through multiple pathways. For instance, the loss of HNRNPF in renal tubules decreases SGLT2 expression, reducing hyperfiltration and renal injury [52], while HNRNPC targets the miR-182-5p/CYP1B1 axis to regulate KIRC metastasis [53]. Nevertheless, research on hnRNPAB's role in renal injury and cancer remains sparse, highlighting the need for further investigation.

Genetic variation of hnRNPAB in various cancers was investigated, with a focus on mutation status in the TCGA cohort. Amplification emerged as the predominant mutation, with rates of 7.24% in KIRC and 4.4% in ACC, occurring at different stages of cancer progression. Validation through the CBioPortal dataset established a significant positive correlation between hnRNPAB expression and copy number. Furthermore, analysis of the correlation between hnRNPAB mutation rates and clinical outcomes indicated that higher mutation rates in ACC, KIRC, LGG, LUAD, PRAD, and SKCM were associated with poorer overall survival, suggesting a strong link between hnRNPAB mutations and unfavorable prognosis.

DNA methylation epigenetic characteristics are recognized as valuable cancer biomarkers [54]. Analysis via the UALCAN database indicated a general reduction in hnRNPAB gene methylation in various tumor tissues compared to normal tissues. Investigation of hnRNPAB methylation patterns across different cancers identified specific CpG sites, such as "cg06538757," associated with poor patient prognosis. Additionally, analysis using the MEXPRESS database confirmed a significant correlation between hnRNPAB methylation levels and copy number variation in various cancer samples. Phosphorylation, with its intricate and diverse effects, significantly influences cancer progression. Abnormal proliferation, invasion, and metastasis of cancer cells often correlate with dysregulation in multiple signaling pathways, including protein phosphorylation modifications [55]. Analysis of the CPTAC dataset revealed variable phosphorylation levels of hnRNPAB across different cancers compared to normal tissues, with significant alterations at the S255 gene locus in most tumor samples. This study establishes a foundation for further investigation into the mechanisms of hnRNPAB methylation and phosphorylation in tumorigenesis and progression, providing valuable insights for its potential as a therapeutic target or biomarker.

GSVA analysis predicted signaling pathways associated with hnRNPAB across various tumor types. Up-regulated genes were significantly enriched in cell cycle-related pathways, indicating hnRNPAB's role in promoting tumorigenesis and progression via cell cycle regulation. Additionally, up-regulated genes were enriched in signaling pathways such as MTORC1, a key regulator of cell growth and metabolism that activates various metabolic processes [56]. This suggests hnRNPAB supports tumor cell proliferation by modulating the MTORC1 pathway and its downstream effects. Moreover, down-regulated genes were significantly enriched in metabolism-related pathways, suggesting hnRNPAB's involvement in metabolic reprogramming.

Mounting evidence highlights the significant role of the TME in tumor onset and progression [57]. Recent advancements in cancer research and treatment have shifted focus from a tumor-centric model to one centered on the TME [58]. Analysis of hnRNPAB expression in relation to pan-cancer immune cell infiltration revealed a negative correlation with the infiltration of CD8 + T cells, CD4 + T cells, and NK cells in most cancers. Tumors with mutant hnRNPAB exhibited significantly higher levels of immune infiltration compared to those with wild-type hnRNPAB. This indicates that hnRNPAB may enhance the TME by suppressing the infiltration of these critical immune cells, and mutations in hnRNPAB could influence tumor behavior by altering the immune microenvironment. hnRNPAB exhibited a significant positive correlation with CAF infiltration levels in KIRP, LIHC, THCA, and UVM. CAFs promote primary tumor initiation and progression by modulating tumor cell proliferation, survival, migration, invasion, and treatment resistance through cytokine secretion [59]. Analysis using the TIMER2.0 database indicated that in liver cancer, hnRNPAB expression positively correlated with CAF markers such as CAV1, CD101, COL1A1, COL1A2, FAP, TGF-β3, and VIM. In KIRP, hnRNPAB expression also showed positive correlations with VIM, CD101, COL1A1, TGF-β2, TGF-β3, EPCAM, and CAV1.

In summary, this study examines the biological and clinical significance of hnRNPAB across various cancers, demonstrating that high hnRNPAB expression is associated with poor prognosis in cancers such as those of the kidney and liver. Additionally, hnRNPAB appears to promote tumor growth and metastasis by modulating immune cell infiltration and CAF activity within the TME. The investigation also covers hnRNPAB gene mutation and methylation status, and their association with tumor metabolic reprogramming and immune evasion. These results provide a foundational basis for further research on hnRNPAB as a potential therapeutic target or biomarker.

cancer-associated fibroblasts (CAFs)

Heterogeneous nuclear ribonucleoproteins (hnRNPs)

RNA binding domain (RBD)

RNA recognition motif (REM)

K homology domain (KH)

Arginine/Glycine-rich domain (RGG)

thymidine kinase (TK)

transportin 1 (Tnpo1)

protamine 2 (PRM2)

epithelial-mesenchymal transition (EMT)

overall survival (OS)

protein-protein interaction (PPI)

Gene Set Variation Analysis (GSVA)

Kyoto Encyclopedia of Genes and Genomes (KEGG)

tumor-infiltrating lymphocytes (TILs)

tumor mutation burden (TMB)

microsatellite instability (MSI)

bladder urothelial carcinoma (BLCA)

breast invasive carcinoma (BRCA)

cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC)

cholangiocarcinoma (CHOL)

esophageal carcinoma (ESCA)

glioblastoma multiforme (GBM)

head and neck squamous cell carcinoma (HNSC)

kidney renal clear cell carcinoma (KIRC)

kidney renal papillary cell carcinoma (KIRP)

liver hepatocellular carcinoma (LIHC)

lung adenocarcinoma (LUAD)

lung squamous cell carcinoma (LUSC)

prostate adenocarcinoma (PRAD)

stomach adenocarcinoma (STAD)

thyroid carcinoma (THCA)

uterine corpus endometrial carcinoma (UCEC)

pheochromocytoma and paraganglioma (PCPG)

Cyclin B1 (CCNB1)

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The original contributions presented in the study are included in the article and supplementary material, further inquiries can be directed to the corresponding authors.

Competing interests

The authors declare no conflicts of interest.

Funding

This research was supported partly by grants from the National Natural Science Foundation of China (82273601) and Open Projectof Key Laboratory of Science and Engineering for the Multi-modal Prevention and Control of Major Chronic Diseases, Ministry of Industry and Information Technology (MCD-2023-1-11).

Authors' contributions

Conceptualization, Rui Xin; Resources, Yao Fu and Yong-Hui Wu; Software, Qian-Qian Sun; Writing – original draft, Rui-Ze Wu; Writing – review & editing, Yu-Lin Pan. All authors will be informed about each step of manuscript processing including submission, revision, revision reminder, etc.

Acknowledgements

Not applicable

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No competing interests reported.

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Pan-Cancer Analysis of hnRNPAB: Implications in Tumor Progression, Prognosis, and Immune Microenvironment Modulation

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Abstract

Figures

1. Introduction

2. Methods

2.1. Data Preparation and Analysis

2.2. Survival Analysis

2.3. Mutation Analysis

2.4. Methylation Analysis

2.5. Gene Expression and Functional Analysis

2.6. Immune Infiltration Analysis

2.7. Cancer-Associated Fibroblasts (CAFs) Analysis

2.8. Statistical Analysis

3. Results

3.1. Expression of hnRNPAB in Pan-Cancer Tissues

3.2. Effect of hnRNPAB Expression on the Prognosis of Cancer Patients

3.3. Genetic Variation of hnRNPAB in Pan-Cancer

3.4. Methylation of hnRNPAB in Pan-Cancer

3.5. Functional Analysis Based on hnRNPAB Expression

3.6. Correlation Between Different Immune Cell Infiltration Levels and hnRNPAB Gene Expression

3.7. Predictive Value of hnRNPAB in Immunotherapy Response

3.8. Gene Expression

3.9. Correlation Between Marker Genes of Cancer-Related Fibroblasts and hnRNPAB Expression

4. Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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