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. 2026 Feb 7;17:370. doi: 10.1007/s12672-026-04600-5

Multiomics analysis identifies the prognostic significance and biological roles of the HNRNP family in lung adenocarcinoma

Jingyi Li 1, Zhe Jin 2, Jiahui Li 1, Ruhui Zhang 3,✉, Chunqing Cai 1,✉
PMCID: PMC12949152  PMID: 41653346

Abstract

Purpose

The heterogeneous nuclear ribonucleoprotein (HNRNP) family plays pivotal roles in multiple aspects of RNA metabolism. Recent studies suggest that HNRNP dysregulation can promote tumor development. Therefore, this study aims to systematically characterize the expression profiles, immunological associations, and prognostic significance of HNRNP family members in LUAD.

Methods

Comprehensive transcriptomic and proteomic analyses were conducted using TCGA, GTEx, GEO, and CPTAC LUAD cohorts. Differential expression, immune infiltration, and survival analyses were performed using bioinformatics approaches including ssGSEA, TIDE, and Cox regression modeling. Functional enrichment and alternative splicing profiling were further applied to explore potential mechanisms, with a focus on HNRNPC.

Results

Multiple HNRNP genes were significantly overexpressed in LUAD tissues across datasets. Their expression levels positively correlated with tumor stage, metastasis, recurrence, and TP53 mutation status. High expression of several HNRNPs was associated with poor overall survival, with HNRNPC identified as an independent prognostic indicator in both TCGA and GEO cohorts. Elevated HNRNP expression was linked to reduced immune cell infiltration and lower stromal, immune, and ESTIMATE scores, alongside increased TIDE and Exclusion scores, suggesting immunosuppressive roles in the tumor microenvironment. Functionally, HNRNPC was associated with the activation of cell cycle progression and DNA damage repair. Alternative splicing analysis revealed that HNRNPC predominantly regulates exon skipping events, with enriched downstream pathways involved in chromatin remodeling and transcriptional regulation.

Conclusion

This study highlights the critical roles of HNRNP family members in LUAD, identifying HNRNPC as a key prognostic biomarker and potential intervention candidate to improve patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-026-04600-5.

Keywords: HNRNP family, HNRNPC, LUAD, Prognosis, Immune evasion, Alternative splicing

Introduction

Lung adenocarcinoma (LUAD), the most prevalent histological subtype of non-small cell lung cancer, remains one of the leading causes of cancer-related mortality worldwide [1, 2]. Despite recent advances in early detection, molecular subtyping, and targeted therapies, the overall prognosis for LUAD patients remains unsatisfactory, particularly for those diagnosed at advanced stages. Current standard treatments—comprising surgery, chemotherapy, radiotherapy, and immunotherapy—are often hindered by drug resistance, tumor heterogeneity, and immune evasion [3–5]. Recent bioinformatics and molecular analyses have further revealed that metabolic reprogramming and aberrant nuclear signaling contribute to LUAD tumorigenesis and therapeutic resistance [6, 7]. Consequently, identifying robust molecular markers with both diagnostic and therapeutic potential is essential for improving clinical outcomes in LUAD.

Alternative splicing (AS), a highly conserved post-transcriptional regulatory mechanism, enables the generation of diverse mRNA and protein isoforms from a single gene [8, 9]. This process is tightly regulated by cis-acting elements and trans-acting splicing factors, many of which are frequently dysregulated in cancer [10]. Aberrant AS contributes to various hallmarks of tumorigenesis, including uncontrolled proliferation, apoptosis resistance, metastasis, and immune modulation [11–13]. Recent studies have highlighted the importance of splicing regulatory networks in shaping the oncogenic transcriptome and influencing the tumor microenvironment, thus representing attractive targets for precision oncology.

The heterogeneous nuclear ribonucleoprotein (HNRNP) family constitutes a large group of RNA-binding proteins that are critically involved in RNA splicing, transport, stability, and translation [14, 15]. Most HNRNP proteins contain RNA recognition motifs and auxiliary domains that facilitate dynamic interactions with RNA and other regulatory proteins [16, 17]. Increasing evidence suggests that HNRNPs are aberrantly expressed in a variety of malignancies, where they participate in key processes such as chromatin remodeling, transcriptional control, and the regulation of oncogenic AS events. Several HNRNP members have been implicated in cancer cell proliferation, metastasis, and immune escape [18–20]. Recent studies have also highlighted the prognostic and regulatory relevance of epigenetic and splicing-associated mechanisms in LUAD [21, 22]. However, a comprehensive understanding of their expression landscape, immunological relevance, and prognostic implications in LUAD is still lacking.

In this study, we conducted an integrative multi-omics analysis to systematically explore the expression profiles, clinical relevance, and functional roles of HNRNP family members in LUAD. Using large-scale datasets from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO), and Clinical Proteomic Tumor Analysis Consortium (CPTAC), we assessed their associations with tumor progression, molecular alterations, immune infiltration, and patient prognosis. HNRNPC was further investigated for its involvement in cell cycle regulation, DNA repair, and alternative splicing. These findings highlight the oncogenic potential of HNRNPs, particularly HNRNPC, and their value as prognostic biomarkers and therapeutic targets in LUAD.

Methods

Data collection and batch effect correction

RNA sequencing (RNA-seq) data and corresponding clinical information for LUAD patients were obtained from the Cancer Genome Atlas (TCGA) program (https://portal.gdc.cancer.gov/) via the UCSC Xena browser (https://xenabrowser.net/datapages/). Normal lung tissue RNA-seq data were retrieved from the Genotype-Tissue Expression (GTEx) project (https://gtexportal.org/) via the same platform. Additionally, microarray expression profiles and associated clinical data were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), including five independent datasets: GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081. To integrate these GEO datasets and reduce potential platform-specific and batch-related variations, we applied the ComBat function from the R package “sva” for batch effect correction. The effectiveness of batch correction was evaluated using principal component analysis (PCA), which showed that dataset-specific clustering was successfully eliminated (Fig. S1A–B). The processed GEO datasets were subsequently used as an independent validation cohort in downstream analyses.

Comparison of HNRNP family gene and protein expression

We first examined the mRNA expression levels of HNRNP family members in LUAD and normal lung tissues using RNA-seq data from the TCGA and GTEx cohorts. Expression patterns were further evaluated across five independent GEO microarray datasets. To assess protein expression, we accessed the UALCAN portal (https://ualcan.path.uab.edu/analysis-prot.html), which provides data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) (https://proteomics.cancer.gov/programs/cptac), and compared HNRNP family protein levels between LUAD and normal lung tissues.

Immune infiltration and immune escape analysis

To explore the association between HNRNP expression and immune cell infiltration, single-sample gene set enrichment analysis (ssGSEA) was performed using hallmark immune cell gene sets via the “IOBR” R package, which provides an integrated framework for immune deconvolution analysis. Spearman correlation analysis was then conducted to evaluate the relationships between HNRNP expression and immune cell infiltration levels. In addition, Immune, Stromal, and ESTIMATE scores were calculated using the ESTIMATE algorithm. Furthermore, the Tumor Immune Dysfunction and Exclusion (TIDE) database (http://tide.dfci.harvard.edu/) was used to evaluate the potential roles of HNRNPs in immune escape mechanisms, including T-cell exclusion and dysfunction.

Survival and prognostic analysis

Kaplan–Meier (K–M) survival curves and univariate Cox proportional hazards models were used to assess the prognostic significance of individual HNRNP genes. Multivariate Cox regression was conducted to identify independent prognostic factors, adjusting for clinicopathological variables such as T stage, N stage, and M stage. A prognostic nomogram incorporating HNRNPC expression and TNM stage was constructed to predict 1-year, 3-year, and 5-year overall survival (OS) probabilities. Model performance was evaluated using time-dependent receiver operating characteristic (ROC) curve analysis.

Functional enrichment analysis

Samples were stratified into high and low HNRNPC expression groups based on the median expression value. Differentially expressed genes (DEGs) were identified using the “limma” package in R. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were performed using both the “clusterProfiler” package and the DAVID database (https://david.ncifcrf.gov/). Gene Set Enrichment Analysis (GSEA) was conducted to assess the enrichment of hallmark oncogenic pathways. Gene Set Variation Analysis (GSVA) was applied to evaluate the activity of pathways related to cell cycle, DNA repair, and apoptosis. Correlation analyses were performed between HNRNPC expression and representative genes involved in these biological processes. Expression differences between high and low HNRNPC groups were validated across both TCGA and GEO cohorts.

Alternative splicing (AS) analysis

HNRNPC-mediated AS events were analyzed using LUAD splicing data from the Cancer Genome Atlas (TCGA) SpliceSeq database (http://bioinformatics.mdanderson.org/TCGASpliceSeq/), where percent spliced in (PSI) values were used to quantify seven major types of AS events, including exon skipping (ES), intron retention (RI), alternative donor (AD) sites, and alternative acceptor (AA) sites. Samples were divided into high and low HNRNPC expression groups, and differential AS events were identified accordingly. The top differential AS events were visualized using heatmaps. Functional enrichment of genes involved in significant AS events was assessed using GO and KEGG analyses. To illustrate the regulatory landscape of HNRNPC-mediated splicing, a splicing regulatory network was constructed and visualized using Cytoscape.

Results

Expression profiles of HNRNP family members in LUAD

To investigate the expression patterns of the HNRNP family in LUAD, we first analyzed transcriptomic data from TCGA and GTEx. Compared to normal lung tissues, several HNRNP genes—including HNRNPA1, HNRNPC, HNRNPF, and HNRNPU—were significantly upregulated in LUAD, whereas HNRNPH1 and HNRNPM showed reduced expression (Fig. 1A). To validate these findings, we further analyzed an independent LUAD cohort from five GEO datasets. Batch effects across merged GEO datasets were adjusted using the ComBat function from the sva R package, and the effectiveness of correction was confirmed by principal component analysis (PCA) (Fig. S1A–B). Clinical baseline characteristics of the GEO cohort are summarized in Table S1. Consistent with the TCGA-GTEx results, the majority of HNRNP genes were significantly upregulated in LUAD samples from the GEO cohort (Fig. 1B). At the protein level, CPTAC data revealed that most HNRNP proteins were significantly elevated in tumors, except for HNRNPI, which showed no significant change (Fig. 1C). Furthermore, a positive correlation between mRNA and protein abundance was observed for most detectable HNRNP members (e.g., HNRNPC), indicating overall concordance between transcriptional and translational levels (Fig. S4A).

Fig. 1.

Fig. 1

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Expression analysis of HNRNP family members in LUAD. (A) Violin plots comparing mRNA expression of HNRNP genes between normal lung tissues (GTEx) and LUAD tissues (TCGA). (B) Validation of mRNA expression patterns in an independent LUAD cohort from GEO. (C) Boxplots showing Z-score normalized protein expression levels of HNRNP family members in LUAD and matched normal tissues from the CPTAC database. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Expression patterns of HNRNP family members and their associations with clinical features

Analysis of TCGA and GEO datasets revealed that the expression of HNRNP family members is closely associated with key clinical features of LUAD. Several genes, including HNRNPF, HNRNPI, and HNRNPK, exhibited progressively increased expression from early-stage (Stage I) to advanced-stage (Stage III/IV) tumors in both TCGA and GEO cohorts (Fig. 2A–B), suggesting a potential role in LUAD progression. Metastasis and recurrence are among the leading causes of poor prognosis in lung cancer patients. In the TCGA cohort, metastatic tumors (M1) displayed higher expression levels of HNRNPA1, HNRNPA2B1, HNRNPC, HNRNPF, HNRNPG, HNRNPK, and HNRNPL compared to non-metastatic (M0) tumors (Fig. 2C). In the GEO cohort, patients with recurrence exhibited significantly higher expression of HNRNPC, HNRNPF, and HNRNPI (Fig. 2D), indicating a possible link to relapse risk. The lineage-specific transcription factor TTF1 (also known as NKX2-1), a critical marker for LUAD subtyping and a key tumor-driving factor, also showed strong association with HNRNP expression. In both TCGA and GEO datasets, high TTF1 expression was accompanied by elevated expression of several HNRNP members, especially HNRNPA2B1, HNRNPC, HNRNPK, and HNRNPU (Fig. 2E–F), suggesting that these genes may participate in TTF1-related transcriptional networks. Regarding classical driver mutations, HNRNP members such as HNRNPA2B1, HNRNPC, and HNRNPF showed increased expression in TP53-mutant samples (Fig. 2G). In contrast, KRAS mutation had a more limited effect, with significant differences observed only in HNRNPA1, HNRNPA2B1, and HNRNPU (Fig. 2H). Collectively, these findings suggest that HNRNP genes are functionally associated with LUAD progression, metastasis, recurrence, and driver gene alterations.

Fig. 2.

Fig. 2

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Association of HNRNP gene expression with clinical features in LUAD. (A) Boxplots comparing HNRNP gene expression across pathological stages (Stage I, II, III/IV) in the TCGA LUAD cohort. (B) Boxplots comparing HNRNP gene expression across pathological stages (Stage I, II, III/IV) in the GEO LUAD cohort. (C) Comparison of HNRNP expression between metastatic (M1) and non-metastatic (M0) tumors in the TCGA cohort. (D) Expression differences between non-relapse and relapse groups in the GEO cohort. (E) Comparison of HNRNP gene expression between TTF1 low and high expression groups in the TCGA dataset. (F) Comparison of HNRNP gene expression between TTF1 low and high expression groups in the GEO dataset. (G) Expression levels of HNRNP genes in TP53 wildtype vs. mutant samples in the TCGA cohort. (H) Expression levels of HNRNP genes in KRAS wildtype vs. mutant samples in the TCGA cohort. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Association between hnRNPs expression and immune infiltration in LUAD

Given the critical role of immune cells in the tumor microenvironment (TME), we systematically investigated the association between hnRNP expression and immune infiltration in LUAD. In the TCGA cohort, ssGSEA analysis revealed that most hnRNPs—particularly HNRNPA1, HNRNPA2B1, HNRNPC, HNRNPF, HNRNPI, HNRNPK, and HNRNPM—were negatively correlated with the infiltration of major immune cell types, including effector T cells, B cells, dendritic cells, macrophages, and MDSCs, whereas HNRNPH1 showed minimal correlation (Fig. 3A). These findings were validated in the GEO LUAD cohort, where HNRNPA1, HNRNPA2B1, HNRNPC, HNRNPL, HNRNPK, and HNRNPU exhibited similar negative correlations (Fig. S2A). Immune scoring analysis further demonstrated that elevated expression of HNRNP family members was associated with significantly reduced Immune, Stromal, and ESTIMATE scores in both datasets (Fig. 3B, Fig. S2B), indicating a potential role in shaping an immunosuppressive TME. To assess their involvement in immune escape, we applied the TIDE algorithm. Multiple hnRNPs, especially HNRNPA2B1, HNRNPD, HNRNPF, HNRNPI, HNRNPK, and HNRNPM, were positively correlated with TIDE, Exclusion scores, and MSI signatures, and negatively correlated with Dysfunction in the TCGA dataset (Fig. 3C). In the GEO cohort, HNRNPD, HNRNPF, and HNRNPM exhibited robust positive associations with TIDE scores (Fig. S2C), further substantiating their potential roles in mediating immune exclusion and attenuating tumor immunogenicity. For certain genes, such as HNRNPC, no significant correlation with the TIDE score was observed, despite strong positive associations with Exclusion and MSI and a negative association with Dysfunction. Such findings may result from the counterbalancing effects on TIDE’s integrated components, thereby highlighting the intricate and context-specific role of HNRNPC in modulating immune escape pathways in LUAD.

Fig. 3.

Fig. 3

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Association of hnRNPs expression with immune infiltration and immune evasion in the TCGA LUAD cohort. (A) Bubble plot illustrating the Spearman correlation between hnRNP expression and infiltration levels of 28 immune cell subsets using ssGSEA. Circle size indicates statistical significance (− log₁₀ P), and color represents correlation coefficient. (B) Violin plots comparing Immune, Stromal, and ESTIMATE scores between hnRNP high- and low-expression groups. (C) Heatmap showing correlations between hnRNP expression and TIDE-related scores, including TIDE, Dysfunction, Exclusion, and MSI expression signature. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

HNRNP family expression is associated with LUAD prognosis

To evaluate the prognostic significance of hnRNP family members in LUAD, we first performed Kaplan–Meier survival analysis using the TCGA cohort. High expression of HNRNPA1, HNRNPA2B1, HNRNPC, HNRNPD, HNRNPG, HNRNPI, HNRNPK, HNRNPL, HNRNPM, and HNRNPU was significantly associated with worse OS (Fig. 4A), among which HNRNPC (HR = 1.61, P = 0.0016) and HNRNPI (HR = 1.58, P = 0.0023) showed strong prognostic associations. Univariate Cox regression analysis further confirmed that elevated expression of HNRNPA1, HNRNPA2B1, HNRNPC, HNRNPF, HNRNPI, HNRNPK, HNRNPL, and HNRNPM correlated with unfavorable OS (Fig. 4B), with HNRNPC (HR = 1.626, P = 0.001) and HNRNPI (HR = 1.586, P = 0.012) showing particularly high hazard ratios. Multivariate Cox regression analysis identified HNRNPC (HR = 1.805, P = 0.018) as an independent prognostic factor after adjusting for confounders (Fig. 4C). To validate these findings, we analyzed the GEO cohort, where K–M survival curves and univariate Cox analysis revealed that HNRNPA2B1, HNRNPC, and HNRNPF were significantly associated with poor OS (Fig. S2A–B). Notably, HNRNPC (HR = 2.186, P = 0.014) and HNRNPF (HR = 1.401, P = 0.028) remained independent prognostic risk factors in the multivariate Cox model (Fig. S2C). Collectively, these results suggest that specific hnRNP members, especially HNRNPC, may serve as potential biomarkers for LUAD prognosis.

Fig. 4.

Fig. 4

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Prognostic significance of hnRNP family gene expression in LUAD based on the TCGA cohort. (A) Kaplan–Meier survival curves showing OS differences between high- and low-expression groups of each hnRNP gene. (B) Forest plot of univariate Cox regression analysis assessing the association between hnRNP gene expression and OS. (C) Forest plot of multivariate Cox regression analysis adjusting for clinical confounders. Hazard ratios (HR) and 95% confidence intervals (CI) are presented

Prognostic modeling based on HNRNPC expression and clinicopathological features in LUAD

To further assess the clinical utility of hnRNP members in LUAD, we constructed a prognostic model centered on HNRNPC expression and clinicopathological variables. Univariate Cox regression analysis in the TCGA cohort demonstrated that T stage (T2–T4 vs. T1), N stage (N1–N3 vs. N0), M stage (M1 vs. M0), and HNRNPC expression were significantly associated with OS, with HNRNPC emerging as a potential adverse prognostic factor (HR = 1.92, P = 0.002; Fig. 5A). Subsequent multivariate analysis identified T stage (P = 0.022), N stage (P < 0.001), and HNRNPC (HR = 1.60, P = 0.034) as independent risk factors for OS (Fig. 5B). Based on these variables, we established a nomogram incorporating HNRNPC expression and TNM stage to quantitatively predict 1-year, 3-year, and 5-year survival probabilities (Fig. 5C). The M stage was statistically significant in the univariate Cox analysis but lost significance in the multivariate model. Nevertheless, it was retained in the nomogram for two main reasons. First, M stage is a fundamental component of the TNM classification system, and its inclusion preserves the clinical completeness and interpretability of the model, allowing direct alignment with established oncological staging systems. Second, the lack of statistical significance in the multivariate analysis likely reflects the limited number of M1 cases in the TCGA cohort rather than true biological irrelevance. Therefore, its inclusion helps maintain clinical coherence and may reflect potential prognostic relevance in larger or external cohorts. Each variable was assigned a risk score, and the total score was used to estimate individualized survival probability. Time-dependent ROC curve analysis demonstrated the model’s predictive performance, with area under the curve (AUC) values of 0.698, 0.700, and 0.670 for 1-year, 3-year, and 5-year survival, respectively (Fig. 5D), indicating favorable discrimination. However, due to the lack of TNM staging information in the GEO cohort, external validation of this model remains limited.

Fig. 5.

Fig. 5

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Construction and performance of an HNRNPC-based prognostic model in LUAD. (A) Univariate Cox regression analysis of clinical variables and HNRNPC expression in the TCGA cohort. (B) Multivariate Cox regression analysis identifying independent prognostic factors. (C) Nomogram integrating T stage, N stage, M stage, and HNRNPC expression to predict 1-year, 3-year, and 5-year OS. (D) Time-dependent ROC curves evaluating model performance at 1, 3, and 5 years. AUC values indicate the model’s discriminative ability. *P < 0.05, **P < 0.01, ***P < 0.001

Functional enrichment of differentially expressed genes associated with HNRNPC in LUAD

To investigate the potential molecular mechanisms of HNRNPC in LUAD, we performed differential expression and functional enrichment analyses based on the TCGA and GEO cohorts. In the TCGA dataset, samples were stratified into high and low HNRNPC expression groups. Differential expression analysis revealed that upregulated genes in the high-expression group were significantly enriched in biological processes related to cell cycle regulation, DNA repair, and RNA polymerase II transcriptional control (Fig. 6A and C). A similar enrichment pattern was observed in the GEO cohort (Fig. 6B and D), indicating the robustness of these associations. In contrast, genes downregulated in the low HNRNPC expression group were predominantly involved in cell adhesion, immune response, and motile cilium function, including pathways associated with immune regulation and cellular migration (Fig. 6E and F). Furthermore, GSEA revealed that multiple KEGG pathways, such as cell cycle, DNA replication, proteasome, and spliceosome, were significantly activated in the HNRNPC high-expression group across both datasets (Fig. 6G and H). These pathways are closely related to tumor proliferation, protein degradation, and post-transcriptional modification. Collectively, these results suggest that HNRNPC may contribute to LUAD progression through the activation of oncogenic programs involving cell cycle regulation and RNA splicing.

Fig. 6.

Fig. 6

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Functional enrichment analysis of HNRNPC-related genes in LUAD. (A) Volcano plot showing DEGs between HNRNPC high- and low-expression groups in the TCGA LUAD cohort (|fold change| > 2, P < 0.05). (B) Volcano plot of DEGs based on HNRNPC expression in the GEO LUAD cohort (|fold change| > 1.5, P < 0.05). (C) GO biological process (BP) enrichment of upregulated genes in the TCGA cohort. (D) GO BP enrichment of upregulated genes in the GEO cohort. (E) GO BP enrichment of downregulated genes in the TCGA cohort. (F) GO BP enrichment of downregulated genes in the GEO cohort. (G) GSEA enrichment plots of the top five KEGG pathways enriched in the HNRNPC high-expression group in the TCGA cohort. (H) GSEA enrichment plots of the top five KEGG pathways enriched in the HNRNPC high-expression group in the GEO cohort

HNRNPC drives cell cycle and DNA repair programs in LUAD across cohorts

To further elucidate the functional role of HNRNPC in LUAD, we systematically explored its association with biological pathways related to cell cycle, proliferation, apoptosis, and DNA repair using both TCGA and GEO cohorts. GSVA analysis revealed that high HNRNPC expression was significantly associated with increased enrichment scores for key proliferative and cell cycle pathways, including G2M checkpoint, E2F targets, MYC targets V1, mitotic spindle, and DNA repair in both cohorts (Fig. 7A and B). Correlation analyses further demonstrated that HNRNPC expression was moderately and positively correlated with representative genes involved in cell division (e.g., CDC20, AURKB, PLK1, MKI67; Fig. 7C), DNA repair (e.g., BRCA1, BRCA2, PCNA, RAD51; Fig. 7D), and cell cycle progression (e.g., CDK1, CDK2, CCNA2, CCNB1; Fig. 7E) in the TCGA dataset. Notably, the expression of these key genes was also significantly higher in the HNRNPC high-expression group compared to the low-expression group, a finding that was consistently validated in the GEO cohort (Fig. 7F and G). Collectively, these results suggest that HNRNPC may enhance cellular proliferation and tumor progression in LUAD by promoting cell division, cell cycle progression, and DNA repair activity, underscoring its critical role in lung tumor biology. Importantly, to further strengthen the functional link, we demonstrated that HNRNPC expression was significantly and positively correlated with the expression levels of multiple key cell cycle and DNA repair genes (such as CDC20, AURKB, PLK1, BRCA1, BRCA2, PCNA, and RAD51), as shown in both TCGA and GEO cohorts. These consistent gene-level associations provide direct evidence that the activity of cell cycle and DNA repair pathways is closely related to HNRNPC expression in LUAD.

Fig. 7.

Fig. 7

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HNRNPC expression is associated with cell cycle, proliferation, apoptosis, and DNA repair pathways in LUAD. (A) Heatmap and violin plots showing GSVA enrichment scores for cell cycle, proliferation, and apoptosis pathways in HNRNPC high- and low-expression groups in the TCGA cohort. (B) Heatmap and violin plots displaying GSVA enrichment scores for the same pathways in the GEO cohort. (C) Scatter plots showing correlations between HNRNPC expression and cell division genes (CDC20, AURKB, PLK1, MKI67) in the TCGA cohort. (D) Scatter plots showing correlations between HNRNPC expression and DNA repair genes (BRCA1, BRCA2, PCNA, RAD51) in the TCGA cohort. (E) Scatter plots showing correlations between HNRNPC expression and cell cycle regulators (CDK1, CDK2, CCNA2, CCNB1) in the TCGA cohort. (F) Boxplots showing the expression levels of cell cycle, proliferation, and DNA repair genes in HNRNPC high- and low-expression groups in the TCGA cohort. (G) Boxplots validating these findings in the GEO cohort. *P < 0.05, **P < 0.01, ***P < 0.001

HNRNPC-mediated alternative splicing and its regulatory network in LUAD

AS represents a major layer of post-transcriptional regulation in cancer, and HNRNPC is a key splicing factor implicated in this process. Given that HNRNPC-associated DEGs were enriched in RNA splicing–related pathways, we systematically investigated HNRNPC-mediated AS events in LUAD using the TCGA cohort. Seven major AS event types were detected, with ES being the most prevalent (Fig. 8A), underscoring its dominance in LUAD. Importantly, AS events significantly associated with HNRNPC expression were also primarily ES events (Fig. 8B), suggesting that HNRNPC may exert its regulatory function predominantly via modulation of ES. Heatmap analysis of the top 20 differential AS events sorted by PSI revealed clear clustering according to HNRNPC expression levels (Fig. 8C), with most events showing higher PSI values in the high HNRNPC group. These findings suggest that HNRNPC acts as a positive regulator of specific AS events. Notably, events such as GEMIN2 [ES], FKBP3 [ES], TMEM33[RI], NEIL3[AT], and ZNF274[AT] displayed marked splicing pattern changes, highlighting their potential as direct regulatory targets. GO enrichment analysis of genes involved in these AS events indicated enrichment in transcription regulation, chromatin remodeling, and cell division in the high-expression group, whereas the low-expression group was enriched for mRNA splicing and processing pathways (Fig. 8D–E). KEGG analysis implicated HNRNPC-associated AS genes in key signaling pathways such as motor proteins, adherens junction, and chemokine signaling (Fig. 8F). Finally, the AS regulatory network further illustrated HNRNPC as a central node linking multiple splicing targets involved in tumor progression (Fig. 8G). These data suggest that HNRNPC may rewire splicing programs to facilitate tumor progression in LUAD. Notably, although our literature search indicates that direct experimental evidence for the oncogenic roles of specific AS-derived isoforms (e.g., those generated by ES events) remains limited, our GO and KEGG enrichment analyses of the affected genes provide indirect support for the potential functional and oncogenic significance of these HNRNPC-mediated splicing events.

Fig. 8.

Fig. 8

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HNRNPC-associated alternative splicing landscape and regulatory network in LUAD. (A) Bar plot showing the distribution of all AS event types identified in the TCGA LUAD cohort. (B) Bar plot showing the distribution of AS event types significantly associated with HNRNPC expression. (C) Heatmap of the top 20 differential AS events ranked by PSI values between HNRNPC high- and low-expression groups. (D) GO enrichment analysis of genes associated with HNRNPC-related AS events in the high-expression group. (E) GO enrichment analysis of genes associated with HNRNPC-related AS events in the low-expression group. (F) KEGG pathway enrichment analysis of genes with HNRNPC-associated AS events. (G) Regulatory network illustrating connections between HNRNPC and its major AS target genes in LUAD

Discussion

The HNRNP family comprises over 20 RNA-binding proteins and has garnered increasing attention for its critical roles in regulating RNA splicing, transcription, and translation [23]. AS, a process by which introns are removed, and exons are selectively joined, enables a single gene to produce multiple transcripts and thereby greatly increases proteomic complexity in humans. Previous studies have shown that most hnRNPs negatively regulate splicing by binding to inhibitory cis-elements, such as intronic or exonic splicing silencers, and interfering with splice site recognition or spliceosome assembly [24]. Notably, the expression of HNRNP family members is frequently elevated in various cancers, where they promote tumorigenesis and progression through pathways involving cell proliferation, migration, metabolic reprogramming, and inflammation [25–28].

In this study, we systematically characterized the expression profiles, clinical associations, immune relevance, and functional implications of the HNRNP family members in LUAD using data from TCGA, GTEx, GEO, and CPTAC. Our results demonstrated consistent upregulation of several HNRNP members at both the transcriptomic and proteomic levels, particularly HNRNPA1, HNRNPA2B1, HNRNPC, and HNRNPK, suggesting these genes may play central roles in LUAD biology. Expression trends of certain members, such as HNRNPH1 and HNRNPM, were inconsistent across datasets, possibly reflecting molecular heterogeneity among LUAD samples. Clinically, HNRNP expression levels were significantly associated with advanced tumor stage, metastasis, and recurrence. HNRNPC, HNRNPF, and HNRNPK showed progressive upregulation in advanced-stage tumors and metastatic or recurrent samples, implying a role in enhancing tumor aggressiveness via proliferation and migration. We also identified a strong correlation between HNRNP expression and TTF1 (NKX2-1), a lineage-defining transcription factor and oncogene in LUAD [29]. Although no evidence currently demonstrates a direct regulatory relationship between TTF1 and HNRNPC, this correlation suggests that HNRNPC expression may be co-regulated within TTF1-associated transcriptional networks. Further experimental studies are required to determine whether TTF1 directly influences HNRNPC expression or function. Additionally, the enrichment of HNRNPA2B1, HNRNPC, and HNRNPF expression in TP53-mutant tumors reinforces their involvement in tumorigenesis, as TP53 mutations are known to promote genomic instability and deregulated RNA processing [30]. Moreover, our study uncovered a strong inverse relationship between HNRNP expression and the tumor immune microenvironment. Tumor immune infiltration is a key determinant of prognosis and therapeutic response in LUAD, especially in the context of immunotherapy [31, 32]. Through ssGSEA, ESTIMATE, and TIDE analyses, we found that elevated expression of several HNRNPs—especially HNRNPC—was significantly associated with reduced infiltration of T cells, B cells, dendritic cells, and other immune components, along with lower immune and stromal scores and higher exclusion scores. These findings suggest that HNRNPs may contribute to the establishment of an immunosuppressive microenvironment, potentially leading to reduced immunotherapy efficacy.

Among the HNRNP family, HNRNPC consistently exhibited the most significant biological and clinical relevance. High HNRNPC expression was strongly associated with poor OS in both TCGA and GEO cohorts and remained an independent prognostic factor in multivariate Cox regression analysis. A prognostic nomogram integrating HNRNPC expression and TNM staging demonstrated favorable predictive accuracy, with AUCs exceeding 0.67 for 1-year, 3-year, and 5-year survival. Despite external validation was limited due to incomplete clinical annotations in the GEO dataset, the reproducibility of HNRNPC’s prognostic value across cohorts highlights its translational potential as a biomarker. Functionally, differential expression and enrichment analyses revealed that HNRNPC may promote tumor progression through activation of cell cycle regulation, DNA repair, and transcriptional control. GSEA and GSVA further confirmed the upregulation of hallmark oncogenic pathways, including the G2M checkpoint, E2F targets, MYC signaling, mitotic spindle assembly, and the spliceosome. Additionally, HNRNPC expression was positively correlated with representative genes involved in these pathways, such as CDK1, CDC20, BRCA1, and PCNA, underscoring its role in driving LUAD cell proliferation and genomic maintenance. Consistent with our findings, recent studies have shown that cell-cycle dysregulation and methylation-based alterations are key drivers of LUAD progression and prognosis [21, 33]. Previous studies have reported HNRNPC’s prognostic relevance in other malignancies, including hepatocellular carcinoma, breast cancer, glioblastoma, and ovarian cancer [34–37]. Recent studies have identified key oncogenic pathways in LUAD, including the GSK3β/ITCH/c-FLIP axis and EGFR signaling, underscoring the complexity of molecular networks that drive LUAD progression [38, 39]. In non-small cell lung cancer, it has been reported to interact with KHSRP and activate the IFN-α–JAK–pSTAT1 signaling pathway, contributing to tumor development. However, its precise role in LUAD remained poorly defined [40]. Our study provides key insights into its LUAD-specific functions and highlights HNRNPC as a potential therapeutic target.

Importantly, we also revealed the involvement of HNRNPC in regulating AS events in LUAD. AS events can be broadly classified into seven distinct types, including ES, RI, AD site, AA site, mutually exclusive exons, alternative promoter, and alternative terminator (AT) events [41, 42]. Each of these AS types contribute uniquely to the generation of mRNA and protein isoform diversity, ultimately influencing gene function and cellular phenotype [43, 44]. Among the seven major types of AS events, ES was most prevalent and strongly associated with HNRNPC expression. Notably, genes such as TMEM33[RI], NEIL3[AT], and ZNF274[AT] exhibited distinct splicing patterns between high and low HNRNPC expression groups, suggesting direct regulation. GO enrichment of AS-regulated genes pointed to chromatin remodeling and transcriptional regulation, indicating that HNRNPC-mediated splicing may contribute to broader epigenetic and transcriptomic reprogramming in LUAD. The AS regulatory network we constructed further positions HNRNPC as a central node linking multiple splicing targets that may cooperate to promote malignancy. Taken together, our findings support a model in which HNRNPC drives LUAD progression through three interconnected mechanisms: (1) enhancing cell proliferation and cell cycle progression; (2) remodeling the tumor microenvironment to promote immune evasion; and (3) modulating alternative splicing of cancer-related genes to generate oncogenic isoforms. These multifaceted roles position HNRNPC as a promising target for prognosis and therapeutic intervention in LUAD.

Despite these important insights, several limitations should be acknowledged. In addition, as our analyses were based on publicly available multi-omics datasets with limited sample coverage and without experimental validation, the generalizability and biological interpretation of the results may be constrained. First, as our study is based on retrospective analysis of public datasets, causal relationships cannot be definitively established. Functional experiments in vitro and in vivo are needed to validate the role of HNRNPC, particularly in splicing regulation and immune modulation. Second, although HNRNPC demonstrated strong prognostic utility, its subcellular localization in the nucleus limits its applicability as a clinically accessible biomarker. Unlike HER2 in breast cancer or CEA in colorectal and pancreatic cancers, which are either membrane-bound or secreted and thus easily detectable, HNRNPC lacks favorable properties for detection via noninvasive means in clinical practice [45, 46]. Therefore, future research should prioritize therapeutic strategies targeting HNRNPC—such as combining splicing modulators with immune checkpoint inhibitors—rather than its diagnostic application.

In conclusion, this integrative multi-omics study provides compelling evidence that the HNRNP family, and HNRNPC in particular, plays a critical role in LUAD progression, immune evasion, and patient prognosis. Our findings expand the current understanding of HNRNP-mediated tumor biology and offer a foundation for future translational studies aimed at leveraging HNRNPC as a biomarker and therapeutic target in lung adenocarcinoma.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1. (3.4MB, docx)

Acknowledgements

Not applicable.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by J.L., Z.J., and J.H.L. The first draft of the manuscript was written by J.L., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (No. 32171408).

Data availability

All data used in this study are publicly available. TCGA-LUAD data were obtained from the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/projects/TCGA-LUAD) via UCSC Xena ( [https://xenabrowser.net/datapages/](https:/xenabrowser.net/datapages) ). GTEx data were retrieved from the Genotype-Tissue Expression (GTEx) project ( [https://gtexportal.org/](https:/gtexportal.org) ). GEO datasets were accessed from the Gene Expression Omnibus (GEO) ( [https://www.ncbi.nlm.nih.gov/geo/](https:/www.ncbi.nlm.nih.gov/geo) ) under accession numbers GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081. Proteomic data were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) ( [https://proteomics.cancer.gov/programs/cptac](https:/proteomics.cancer.gov/programs/cptac) ) via the UALCAN portal ( [https://ualcan.path.uab.edu/analysis-prot.html](https:/ualcan.path.uab.edu/analysis-prot.html) ). Alternative splicing data were from the TCGA SpliceSeq database ( [http://bioinformatics.mdanderson.org/TCGASpliceSeq/](http:/bioinformatics.mdanderson.org/TCGASpliceSeq) ). Immune escape data were analyzed using the Tumor Immune Dysfunction and Exclusion (TIDE) database ( [http://tide.dfci.harvard.edu/](http:/tide.dfci.harvard.edu) ).

Declarations

Ethics approval and consent to participate

The data utilized in this study were obtained from publicly available databases, including The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and other publicly accessible resources. No new human or animal experiments were conducted; therefore, ethical approval was not required.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Ruhui Zhang, Email: ruhuizhang@zju.edu.cn.

Chunqing Cai, Email: h_zcn@126.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (3.4MB, docx)

Data Availability Statement

All data used in this study are publicly available. TCGA-LUAD data were obtained from the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/projects/TCGA-LUAD) via UCSC Xena ( [https://xenabrowser.net/datapages/](https:/xenabrowser.net/datapages) ). GTEx data were retrieved from the Genotype-Tissue Expression (GTEx) project ( [https://gtexportal.org/](https:/gtexportal.org) ). GEO datasets were accessed from the Gene Expression Omnibus (GEO) ( [https://www.ncbi.nlm.nih.gov/geo/](https:/www.ncbi.nlm.nih.gov/geo) ) under accession numbers GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081. Proteomic data were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) ( [https://proteomics.cancer.gov/programs/cptac](https:/proteomics.cancer.gov/programs/cptac) ) via the UALCAN portal ( [https://ualcan.path.uab.edu/analysis-prot.html](https:/ualcan.path.uab.edu/analysis-prot.html) ). Alternative splicing data were from the TCGA SpliceSeq database ( [http://bioinformatics.mdanderson.org/TCGASpliceSeq/](http:/bioinformatics.mdanderson.org/TCGASpliceSeq) ). Immune escape data were analyzed using the Tumor Immune Dysfunction and Exclusion (TIDE) database ( [http://tide.dfci.harvard.edu/](http:/tide.dfci.harvard.edu) ).

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