Unraveling the oncogenic and immunomodulatory roles of GINS1: a systematic pan-cancer study

Abstract

Background

GINS1 mediates DNA replication fidelity and cell cycle regulation through its integral role in the GINS complex. However, its comprehensive role across diverse cancer types remains unclear. This study aimed to systematically analyze the critical functions of GINS1 across cancers.

Methods

We utilized pan-cancer and multi-omics datasets and applied survival analysis, immune infiltration assessment, functional enrichment analysis, and genomic alteration profiling to explore.

Methods

We utilized pan-cancer and multi-omics datasets and applied survival analysis, immune infiltration assessment, functional enrichment analysis, and genomic alteration profiling to explore expression patterns, prognostic significance, immune infiltration, genomic alterations, and potential therapeutic relevance of GINS1 across multiple cancers. Ultimately, we evaluated the diagnostic performance of GINS1 in four cancer types and validated its functional role in renal cancer through in vitro assays, including colony formation, CCK-8, EdU, and Transwell, confirming that GINS1 knockdown inhibits cell proliferation and impairs PI3K/AKT pathway activity.

Results

GINS1 was significantly upregulated in 31 cancer types and correlated with poor prognosis in multiple malignancies. GINS1, as a biomarker, is concurrently linked to tumor mutational burden (TMB), microsatellite instability (MSI), and RNA m6A modification across tumor lineages. High GINS1 expression was significantly associated with distinct immune infiltration patterns, characterized by reduced NKT cell signatures and increased Th2 cell enrichment. Functional analysis revealed that GINS1 is involved in cell cycle regulation, DNA replication, and oncogenic signaling pathways (PI3K-Akt, p53, and NF-κB). Notably, survival analysis indicated that GINS1 expression affects the immunotherapy response, predicting poor outcomes in patients receiving anti-PD-1 therapy but an improved response to anti-PD-L1 inhibitors. GINS1 exhibited strong diagnostic value in KIRP, LIHC, PAAD, and SARC. In renal cancer, functional assays confirmed that GINS1 knockdown significantly suppressed cell proliferation, migration, and invasion, and attenuated PI3K/AKT signaling activity.

Conclusion

GINS1 serves as a viable prognostic indicator and target for therapeutic intervention, influencing both tumor progression and immune regulation. Targeting GINS1 may provide new therapeutic insights for cancer management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-026-04252-z.

Introduction

Cancer is a category of disease characterized by uncontrolled cellular proliferation with the potential for invasion into adjacent tissues or metastasis to distant body sites [1]. In countries with a low/medium human development index (LHDI/MHDI), cancer increasingly contributes to premature mortality among individuals [2]. Across North America, although significant progress has been made in reducing cancer-related mortality by over 4 million cases through lifestyle modifications and advancements in early detection and treatment, the incidence of certain cancers has been steadily increasing in specific populations, such as cervical cancer in women(30–44) and colorectal cancer in individuals(<55) [3]. A similar trend has been observed in China, and both the incidence and mortality rates of cervical cancer in China have been increasing annually [4]. Consequently, this necessitates the implementation of strategies to mitigate the growing burden of cancer, particularly in resource-limited settings where healthcare systems are often underdeveloped and access to effective interventions remains limited.

GINS1 serves as the molecular scaffold within the GINS complex (derived from the Japanese term “go-ichi-ni-san”), which also includes SLD5, GINS2, and GINS3 [5]. Together with the MCM proteins and CDC45, the GINS complex forms the CMG (CDC45-MCM-GINS) complex, which is essential for initiating the unwinding process during DNA replication [6, 7]. The GINS complex plays a critical role in stabilizing the replication fork structure, ensuring the efficient replication of double-stranded DNA, and coordinating the transition between the initiation and elongation phases of DNA replication [8, 9]. GINS1, associated with increased proliferative activity in tumor cells, is overexpressed in various cancers, including breast cancer [10], gastric cancer [11], and colorectal cancer [12], hepatocellular carcinoma (HCC) [13], bladder cancer [14], sarcoma [15], and gastric cancer [16], and its overexpression is closely associated with poor prognosis. Silencing GINS1 has been shown to increase the sensitivity of HCC to sorafenib, thereby reversing drug resistance [17]. In sarcoma, high expression of GINS1 is associated with an immunosuppressive tumor microenvironment, which may impair the response to immunotherapy [18]. Additionally, targeting the MALAT1-FOXP3-GINS1 signaling pathway has demonstrated significant therapeutic potential in non-small cell lung cancer (NSCLC) [19].

Although the aforementioned studies revealed the oncogenic role of GINS1 in multiple cancer types, research on certain cancer types remains relatively limited, and sample sizes are often limited. Therefore, this study aims to systematically analyze the role of GINS1 across cancers on the basis of large-scale sample data, providing more evidence to elucidate its biological functions, prognostic value, and potential therapeutic targets across different cancer types. This approach will address the current gaps in research and contribute to a more comprehensive understanding of the role of GINS1 in cancer biology.

Materials and methods

Data preprocessing and normalization

Pan-cancer transcriptomic profiles were retrieved from the UCSC Xena platform (https://xenabrowser.net/), using the TCGA Pan-Cancer (PANCAN) RNA-seq dataset that has been uniformly processed and normalized. This dataset comprises 10,535 samples across multiple cancer types, with expression values available for 60,499 genes. Expression data corresponding to GINS1 (ENSG00000101003) were extracted from the pan-cancer matrix for subsequent analyses. Prior to downstream analyses, expression values were transformed using a log₂(x + 1) scale to improve data distribution and comparability across samples. For pan-cancer analyses, cancer types represented by fewer than three samples were excluded. In addition, samples with undetectable GINS1 expression were removed. The filtered and transformed dataset was then used consistently throughout all subsequent analyses.

GINS1 expression profile and subcellular localization

The expression of GINS1 in normal/tumor tissues was obtained from the Human Protein Atlas (HPA, https://www.proteinatlas.org/) by querying the Tissue Atlas, which provides RNA-seq-based expression levels across human tissues. The subcellular localization of GINS1 in the A-431, MCF-7, and U2OS cell lines was analyzed via HPA via immunofluorescence (IF) imaging and high-resolution confocal microscopy. To further evaluate differential GINS1 expression between tumor and normal tissues across cancer types, transcriptomic data were analyzed using datasets retrieved from the UCSC Xena platform. Tumor and normal samples were classified according to TCGA tumor annotations, and differential expression analysis was performed using the limma package (version 3.58.1). The corresponding TCGA cancer type abbreviations are provided in Table S1.

Associations between GINS1 expression and clinicopathological characteristics

Clinicopathological information, including T (tumor), N (node), and M (metastasis) classifications as well as AJCC pathological stage (stages I-IV), was obtained from the clinical annotation files available in the UCSC Xena database. For each cancer type, samples were grouped according to the corresponding clinicopathological categories. Differences in GINS1 expression across clinicopathological groups were evaluated within individual cancer types using the limma package in R. Statistical significance was defined as a two-sided P value < 0.05.

GINS1 expression was analyzed for its prognostic significance

Clinical and survival data (OS, DSS, PFI) were processed from the GDC Data Commons (https://portal.gdc.cancer.gov). The optimal GINS1 expression cutoff was determined via the survminer package in the R tool [20]. Expression-based stratification (upper vs. lower tertile of GINS1 mRNA levels) revealed significant survival disparities, as validated by Kaplan-Meier analysis with log-rank P < 0.05. In addition, uni- and multivariate Cox models incorporating GINS1 expression and clinical variables were built to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) via the Xiantao Academic online analysis tool (https://www.xiantaozi.com/).

Correlations between GINS1 expression and immune cell infiltration scores

Immune cell infiltration levels were estimated using the TIMER, CIBERSORT, and xCELL algorithms implemented in the IOBR package in R [21]. Gene expression data, processed as log₂(TPM + 1), were derived from TCGA tumor samples to ensure consistency in immune deconvolution analyses. TIMER was used to estimate the abundance of major immune cell populations based on cancer-specific expression profiles [22], CIBERSORT was applied to infer the relative proportions of 22 immune cell types [23], and xCELL was employed to calculate enrichment scores for 64 immune and stromal cell populations [24]. For each cancer type, Pearson correlation analysis was performed to assess the associations between GINS1 expression levels and immune cell infiltration scores. Statistical significance was defined as a two-sided P value < 0.05.

GINS1-related genes and enrichment analysis

Protein-protein interaction (PPI) networks associated with GINS1 were constructed using the STRING database (https://string-db.org/) to identify potential interacting partners involved in DNA replication and cell cycle regulation. Genes coexpressed with GINS1 were identified using the GEPIA2 platform (http://gepia2.cancer-pku.cn/) based on TCGA transcriptomic data. The top 100 genes showing the strongest positive correlation with GINS1, ranked by Pearson correlation coefficient, were selected for subsequent analyses (Table S2). Functional enrichment analyses, including Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed using the Sangerbox 3.0 platform [25]. To account for multiple comparisons, enrichment results were adjusted using the Benjamini–Hochberg method, and pathways with a false discovery rate (FDR) < 0.05 were considered statistically significant. In addition, GINS1-centered coexpression networks were further explored using the LinkedOmics database (https://www.linkedomics.org/). Analyses were performed within individual cancer cohorts, with a particular focus on prostate cancer and renal cell carcinoma, to evaluate the consistency of GINS1-associated transcriptional programs in tumor types that were subsequently investigated in greater depth.

Association of GINS1 alterations with cancer genomic diversity

The genomic alteration landscape of GINS1 across cancer types was analyzed using the cBioPortal platform (https://www.cbioportal.org/), and alteration frequency plots were generated to summarize mutation and copy number alteration patterns. Somatic mutation data were obtained from the GDC portal and processed using MuTect2 [26]. Tumor mutation burden (TMB) was calculated for each sample using the maftools package (version 2.8.05) in R, defined as the total number of somatic mutations per megabase, and analyses were performed within individual cancer types. Microsatellite instability (MSI) scores were retrieved from previously published studies and integrated at the cancer-type level [27]. Pearson correlation analysis was conducted to assess the associations between GINS1 expression and TMB or MSI, respectively, with analyses stratified by cancer type. In addition, a curated set of 44 RNA modification-related genes, including m1A (n = 10), m5C (n = 13), and m6A (n = 21), was obtained from the UCSC Xena database. Correlation analyses between GINS1 expression and RNA modification-related gene expression were performed using Pearson correlation coefficients. For analyses involving multiple comparisons, P values were adjusted using the Benjamini-Hochberg method, and an FDR < 0.05 was considered statistically significant.

Relationship between GINS1 and ESTIMATE score

The ESTIMATE algorithm was applied to infer stromal and immune components of the tumor microenvironment using the ESTIMATE R package (version 1.0.13) [28]. Analyses were performed on TCGA tumor samples, with gene expression data obtained from the UCSC Xena platform. ESTIMATE-derived stromal and immune scores were merged with corresponding GINS1 expression levels. For each cancer type, Pearson correlation analysis was conducted to assess the associations between GINS1 expression and ESTIMATE scores.

Relationship between GINS1 and immune checkpoint blockade (ICB)

The association between GINS1 expression and ICB treatment response was analyzed via KMplot (https://kmplot.com/analysis/). The ICB-treated cohorts were selected, and patients were stratified based on GINS1 expression levels (median cutoff).

Diagnostic performance of GINS1 in KIRC, LIHC, PAAD, and SARC

The FPKM expression data of GINS1 (ENSG00000101003) in KIRC, LIHC, PAAD, and SARC were acquired via the GDC Data Portal. The diagnostic efficacy of GINS1 in distinguishing tumors from normal tissues was assessed via receiver operating characteristic (ROC) curve analysis with the pROC package (version 1.18.5) in R. The area under the curve (AUC) was calculated for each cancer type, and ROC curves were visualized via the ggplot2 package (version 3.5.0) in R. Higher AUC values indicate better diagnostic performance.

GINS1-PI3K/AKT pathway association analysis

RNA-seq data and corresponding clinical information for renal cell carcinoma (RCC) were obtained from TCGA database. Specifically, STAR-counts data were downloaded and subsequently normalized to transcripts per million (TPM) for downstream expression analysis. The PI3K/AKT pathway activity was assessed using single-sample gene set enrichment analysis (ssGSEA), based on predefined hallmark gene sets. The correlation between GINS1 expression and pathway enrichment scores was calculated using Pearson correlation analysis.

Clinical samples collection

Formalin-fixed, paraffin-embedded (FFPE) tissue specimens were collected from 31 treatment-naïve patients with RCC who underwent surgical resection at Yunnan Cancer Hospital between January 2024 and February 2025. For each patient, paired tumor tissues and adjacent non-tumorous tissues were obtained to enable comparative analysis of GINS1 expression. All procedures involving human specimens were approved by the Ethics Committee of Yunnan Cancer Hospital, and written informed consent was obtained from all participants.

Cell culture

RCC cell lines 769-P (Cat. No. CL-0009) and 786-O (Cat. No. CL-0010) were obtained from Procell (Wuhan, China). Cells were cultured in RPMI-1640 medium (Cat. No. PM150110, Procell) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin‒streptomycin (P/S), and maintained at 37 °C in a humidified incubator with 5% CO₂. The culture medium was refreshed every 3 days.

Total RNA isolation and reverse transcription

Total RNA was extracted from renal cancer cell lines using TRIzol reagent (Cat. No. R0016, Beyotime, China), and from FFPE tissue specimens using the RNAprep Pure FFPE Kit (Cat. No. DP439, TIANGEN Biotech, Beijing), following the respective manufacturers’ instructions. Subsequently, 1 µg of total RNA was reverse transcribed into cDNA using a reverse transcription kit (Cat. No. RR047A, Takara, Dalian, China) following the manufacturer’s protocol. The resulting cDNA was stored at − 20 °C until further use.

Cell transfection

The two siRNA sequences (5′-CAAGTTCTGGAGGAGATGAA-3′ and 5′-ACAAGTTCTGGAGGAGAT-3′) were obtained from Genechem (Shanghai, China) and mixed with Lipofectamine 2000 (Cat. No. 11668027, Thermo Fisher) according to the manufacturer’s recommended ratio to prepare the transfection complexes. The complexes were added to the culture media of 769-P and 786-O cells and incubated at 37 °C with 5% CO₂ for 24 h.

RT-qPCR

RT‒qPCR primers for GINS1 and reference genes (β-actin) were designed and acquired from Genechem (Shanghai, China) according to the validated sequences [14]. The reaction mixture contained SYBR Green Master Mix (Cat. No. RR820A, Takara), 0.5 µL each of 10 µM forward/reverse primers, and 2 µL of cDNA template. Amplification was performed with the following program: 95 °C for 10 min; 40 cycles of 95 °C for 15 s, 55 °C for 30 s, and 72 °C for 30 s. Relative gene expression was calculated via the 2^−ΔΔCt method [29], with three technical replicates per sample.

Western blotting analysis

Cells (786-O and 769-P) at 90% confluence were lysed via chilled RIPA extraction buffer (Cat. No. R0010; Solarbio, Beijing, China). The lysates were centrifuged, and the protein concentration was determined via a BCA assay (Cat. No. P0010, Beyotime, China). Protein (30 µg) was separated via 10% SDS-PAGE and transferred to PVDF membranes (Cat. No. 03010040001, Millipore). The membranes were blocked with 5% nonfat milk for 1 h at RT, followed by incubation with GINS1 antibodies (Cat. No. ab181112; Abcam, Cambridge, UK) and β-actin (Cat. AF5003; Beyotime, China) at 4 °C for 24 h. The membranes were incubated with an HRP-conjugated secondary antibody for 1 h at RT. The protein bands were visualized via an Odyssey Infrared Imager system (LI-COR, NE, USA) and quantified via ImageJ. The target protein expression was normalized to that of β-actin, with three independent biological replicates per group.

Transwell Assay

Cell migration and invasion were evaluated using Transwell inserts (Cat. No. 3422; Corning). For invasion assays, membranes were pre-coated with Matrigel (Cat. No. 354248; Corning). After incubation, non-migrated cells were removed, and the cells on the lower surface were fixed and stained. Five random fields per insert were imaged under a microscope, and cell numbers were quantified using ImageJ software.

Cell Counting Kit-8 (CCK-8) Assay

Cells (5000/well) were seeded in 96-well plates at the indicated densities and treated for 0, 24, 48, and 72 h. At each time point, CCK-8 reagent (Cat. No. C0038, Beyotime, China) was added and incubated for 2 h. Absorbance was measured at 450 nm using a microplate reader. Results were normalized to the absorbance of control wells.

EdU Assay

Cells were incubated with 10 µM EdU (Cat. No. C0075S, Beyotime, China) for 2 h, fixed with 4% paraformaldehyde, and permeabilized with 0.3% Triton X-100. EdU-labeled nuclei were visualized via click chemistry using a fluorescent azide dye, and counterstained with Hoechst 33,342. Images were captured under a fluorescence microscope, and EdU-positive cells were quantified in randomly selected fields.

Colony formation assay

After transfection, 769-P and 786-O cells were seeded at low density in 6-well plates and cultured for 7 days. Visible colonies were fixed with 4% PFA and stained with 0.1% crystal violet. Colony numbers were quantified to evaluate the siRNA-mediated suppression of the cellular proliferative capacity.

Statistical analysis

Statistical analyses were conducted with GraphPad Prism 8.0.1 (GraphPad Software, San Diego, CA) and the R 3.6.4 environment. Continuous variables were expressed as mean ± SD. Comparative analyses between tumor and adjacent normal tissues for GINS1 expression levels were executed through unpaired Wilcoxon rank sum test. To evaluate the associations of GINS1 expression patterns with clinicopathological staging, an unpaired t test was used for pairwise comparisons. Intergroup differences were assessed by one-way ANOVA (F test) for multiple comparisons. Intervariable correlations involving GINS1 were quantified through the Pearson correlation coefficient. Time-to-event analyses utilized Kaplan-Meier estimators with log-rank tests (HRs with 95% CIs). In vitro data were analyzed using GraphPad Prism. Two-group comparisons used unpaired two-tailed t-tests. Statistical significance was defined as two-tailed p < 0.05 throughout.

Results

Expression profiles and dynamic subcellular localization of GINS1

According to the HPA, GINS1 was relatively highly expressed in the testis and lymphoid tissue, whereas its expression was lowest in the choroid plexus and tongue (Fig. 1A). Furthermore, GINS1 is significantly overexpressed in GBM, GBMLGG, LGG, UCEC, BRCA, CESC, LUAD, ESCA, STES, KIRP, KIPAN, COAD, COADREA, PRAD, STAD, HNSC, KIRC, LUSC, LIHC, WT, SKCM, BLCA, THCA, READ, OV, PAAD, UCS, ALL, LAML, ACC, and CHOL tumor tissues compared with normal tissues. Conversely, its expression is notably downregulated in TGCT and KICH tumor tissues relative to their normal counterparts (Fig. 1B, Table S3). Through staining results, the distribution of GINS1 and its interactions with the nucleus, microtubules, and endoplasmic reticulum (ER) can be observed in different cancer cell lines (Fig. 1C). Especially, GINS1 showed strong fluorescence signals in the nucleus, suggesting its potential involvement in nuclear processes.

Fig. 1.

Analysis of dysregulation and subcellular distribution of GINS1. (A) The Normalized transcript per million (nTPM) values of GINS1 in different human organs. (B) Violin plots depicting GINS1 transcript levels in tumors (red) versus adjacent normal tissues (blue). (C) Distribution and colocalization of GINS1 (green), the nucleus (blue), microtubules (red), and the ER (yellow) in cancer cell lines. **** p < 0.001

Correlation between GINS1 expression and tumor stage in pan-cancer analysis

We subsequently analyzed GINS1 expression across different cancers in pan-cancer cohort. The results demonstrated that GINS1 exhibited significant variation across different TNM stages and overall stage classifications in multiple cancer types. Higher GINS1 expression in ACC and KIPAN was associated with advanced T, N, and M stages, indicating a potential role in tumor progression and metastatic potential (Fig. 2A-C, Table S4-6). In BRCA, KIRP, KIPAN, KIRC, LUSC, LIHC, ACC, and KICH, GINS1 expression was significantly greater in late-stage tumors than in early-stage tumors (Fig. 2D, Table S7).

Fig. 2.

Stage-dependent differential expression of GINS1 in various cancer types. (A–C) Correlation between GINS1 expression and TNM classification (T, N, and M stages). (D) Comparison of GINS1 expression levels between early- and late-stage tumors based on overall clinical staging. *p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.001

Clinical significance of GINS1 expression as a prognostic biomarker for cancer survival

We revealed the prognostic significance of GINS1 expression across multiple cancer types on the basis of overall survival (OS), disease-specific survival (DSS), and the progression-free interval (PFI). Elevated GINS1 expression demonstrated significant prognostic value and was correlated with reduced OS in ACC, KIRP, LGG, LIHC, MESO, PAAD, and SARC (Fig. 3A). Similarly, elevated GINS1 expression correlated with shorter DSS in the same cancer types (Fig. 3B). Furthermore, increased GINS1 expression was linked to a reduced PFI in LGG, LIHC, MESO, PAAD, PRAD, and SARC (Fig. 3C). In comprehensive multivariate models incorporating established clinical variables, GINS1 expression maintained independent prognostic significance, specifically for overall OS, DSS, and PFI in MESO (Table S8-10); for OS, DSS, and PFI in LIHC (Table S11-13); for OS and PFI in PAAD (Table S14-15); and for OS in ACC (Table S16).

Fig. 3.

Survival outcomes stratified by GINS1 expression were evaluated via Kaplan‒Meier curves across multiple cancer types. (A) OS. (B) DSS. (C) PFI

Relationship between GINS1 and immune cell infiltration

To explore the correlation between GINS1 expression and immune cell infiltration across multiple cancer types, as analyzed using the Timer, CIBERSORT, and xCELL methods. GINS1 expression showed strong positive correlations with B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells in PRAD, KIRC, THCA, KIPAN, and LIHC. In contrast, negative correlations were observed in LUSC and STAD (Fig. 4A). The CIBERSORT results revealed significant positive correlations between GINS1 and macrophages in THCA, PRAD, and LIHC, suggesting its potential role in immune suppression (Fig. 4B). Interestingly, xCELL analysis indicated that GINS1 expression was significantly positively associated with TH2 cells but negatively associated with NKT cells in almost all solid tumors and hematologic malignancies (Fig. 4C). Hence, GINS1 expression was negatively correlated with NKT cell infiltration and positively correlated with Th2 cell infiltration across multiple cancer types, suggesting an association with immunosuppressive immune infiltration patterns.

Fig. 4.

Correlation analysis between GINS1 expression and immune cell infiltration across multiple cancers via TIMER (A), CIBERSORT (B), and xCELL (C) algorithms

GINS1-related proteins and functional analysis

The PPI network of GINS1 was generated via the STRING database. The network revealed strong interactions between GINS1 and components of the GINS complex (GINS2, GINS3, and GINS4), as well as the minichromosome maintenance (MCM) complex (MCM2-7) and CDC45 (Fig. 5A). GO enrichment analysis revealed that GINS1 is crucial for cell cycle progression, mitotic regulation, and chromosomal dynamics (Fig. 5B). In addition, KEGG enrichment analysis further confirmed that GINS1 participates in cell cycle regulation, DNA replication, and genome stability and may influence tumor suppression and DNA damage repair (Fig. 5C).

Fig. 5.

PPI network and functional enrichment analysis of GINS1-associated proteins. (A) STRING database-based PPI network of GINS1 and DNA replication-related proteins. GO (B) and KEGG (C) functional analyses

GINS1 mutation landscape and RNA modification correlations across pan-cancer datasets

GINS1 demonstrated a high frequency of alterations, primarily gene amplification and mutation, in UCS, COAD, and UCEC (Fig. 6A). Our analysis revealed that GINS1 is positively correlated with TMB in GBMLGG, LUAD, KIPAN, STAD, PRAD, KIRC, TGCT, PCPG, and KICH (Fig. 6B). Moreover, GINS1 exhibited positive correlations with MSI in SARC, STAD, KIRC, and LIHC but negative correlations with MSI in GBMLGG and DLBC (Fig. 6C), suggesting its differential roles in genomic instability across cancer types. In addition, GINS1 showed significant positive correlations with multiple RNA modification genes, especially those related to m6A, while exhibiting moderate correlations with m5C and m1A modification genes across various cancer types (Fig. 6D).

Fig. 6.

Diverse genomic feature analysis of GINS1. (A) Genomic alteration landscape of GINS1. Correlations between GINS1 expression and TMB (B), MSI (C), and RNA modification (D)

GINS1 was studied for its association with the ESTIMATE score

Our analysis via the ESTIMATE algorithm revealed distinct patterns of GINS1 correlation with stromal and immune cell infiltration in the tumor microenvironment (TME). Specifically, GINS1 was positively correlated with GBMLGG, KIRC, and KIPAN but negatively correlated with STES, STAD, LUSC, SARC, GBM, BRCA, TGCT, NB, OV, CESC, LUAD, HNSC, UCEC, SKCM, KIRP, SKCM-M, SKCM-P, ESCA, WT, CHOL, ACC, and LIHC, underscoring its variable role in TME composition across different cancer types (Fig. 7).

Fig. 7.

Correlation analysis of GINS1 expression with ESTIMATE scores

Kaplan–Meier survival analysis of GINS1 expression in patients receiving ICB therapy

We next analyzed the correlation between GINS1 expression and the prognosis of ICB therapy. In all anti-PD-1-treated cancer patients, including those treated with Nivolumab and Pembrolizumab, high GINS1 expression was associated with significantly reduced survival rates (Fig. 8A-C). Conversely, in patients receiving anti-PD-L1 (Atezolizumab) or anti-CTLA-4 (Ipilimumab) therapies, high GINS1 expression showed a trend toward improved survival (Fig. 8D-F). These results indicate that GINS1 expression is associated with differential survival patterns across distinct ICB-treated cohorts.

Fig. 8.

Correlation between GINS1 expression and survival in patients receiving ICB therapy. (A) All anti-PD-1 therapies. (B) Nivolumab. (C) Pembrolizumab. (D) All anti-PD-L1 therapies. (E) Atezolizumab. (F) Ipilimumab

GINS1 is significantly upregulated in prostate cancer and is closely linked to oncogenic pathways

Immunohistochemistry (IHC) analysis revealed that GINS1 expression was low in normal glandular cells but significantly elevated in tumor cells (Fig. 9A-B). The genes positively correlated with GINS1 were involved primarily in the cell cycle, DNA replication, and mitosis, whereas the genes negatively correlated with GINS1 were associated with tumor suppression and metabolic regulation (Fig. 9C-D). GO analysis revealed significant enrichment of GINS1-related genes related to cell cycle regulation, mitosis cell cycle processes, chromosome organization, and DNA metabolic processes (Fig. 9E). KEGG pathway analysis further revealed enrichment in the cell cycle, DNA damage repair, and p53 signaling pathways (Fig. 9F).

Fig. 9.

GINS1 expression and function were analyzed in prostate cancer. GINS1 expression is low in normal prostate tissue (A) but significantly upregulated in prostate cancer tissue (B). LinkedOmics-based correlation analysis identified genes positively (C) and negatively (D) correlated with GINS1. GO (E) and KEGG (F) enrichment analyses revealed GINS1-associated genes

High expression levels in renal cancer and related analysis

We also analyzed the expression level and functional enrichment of these genes in renal cancer. IHC analysis revealed that GINS1 expression was moderate in glomerular cells, high in renal tubular cells, and significantly elevated in tumor cells (Fig. 10A-B). LinkedOmics database analysis revealed that genes positively correlated with GINS1 were enriched in the cell cycle, mitosis, and DNA replication, suggesting its role in tumor cell proliferation (Fig. 10C). Conversely, negatively correlated genes were associated with tumor suppression and the stress response, indicating potential suppression of antitumor mechanisms (Fig. 10D). GO analysis highlighted GINS1-related genes involved in the cell cycle, mitosis, and DNA replication (Fig. 10E). KEGG analysis further revealed enrichment of the PI3K-Akt, MAPK, NF-kappa B, and p53 signaling pathways, emphasizing the involvement of GINS1 in renal cancer progression and treatment sensitivity (Fig. 10F).

Fig. 10.

Expression level, correlation, and functional enrichment in renal cancer. GINS1 expression in normal kidney tissue (A) and renal cancer tissue (B). Genes that are positively (C) and negatively (D) correlated with GISN1 in renal cancer. GO (E) and KEGG (F) enrichment analyses of GINS1-associated genes in renal cancer

Diagnostic efficacy assessment and in vitro functional validation

Given the link between elevated GINS1 expression and unfavorable prognosis in several cancers, we evaluated its diagnostic performance in KIRP, LIHC, PAAD, and SARC using ROC curves. The AUC values for GINS1 were 0.693 (0.606–0.781) in KIRP (Fig. 11A), 0.951 (0.925–0.977) in LIHC (Fig. 11B), 0.673 (0.486–0.860) in PAAD (Fig. 11C), and 0.962 (0.931–0.993) in SARC (Fig. 11D), highlighting its strong diagnostic potential (AUC > 0.6) in these malignancies. GINS1 expression was significantly higher in FFPE tumor tissues compared to matched adjacent normal tissues, as confirmed by RT-qPCR analysis (Fig. 11E). Given that GINS1 is upregulated in renal cancer and implicated in multiple tumorigenesis-related signaling pathways, we conducted in vitro experiments to validate the impact of GINS1 knockdown on the proliferation of the renal cancer cell lines 769-P and 786-O. Specifically, the transfection of si-GINS1-1 and si-GINS1-2 into both cell lines resulted in a significant reduction in both the transcriptional (Fig. 11F-G) and protein levels(Fig. 11H) of GINS1. Consequently, the proliferative capacity of both cell lines was markedly suppressed (Fig. 11I). CCK-8 assays showed that GINS1 knockdown significantly inhibited proliferation of RCC cells, with the most notable reduction observed at 96 h compared to the si-NC group (Fig. 11J). EdU assays demonstrated that GINS1 knockdown markedly suppressed DNA synthesis in RCC cells. The proportion of EdU-positive cells was significantly reduced in the si-GINS1 groups compared to the si-NC group (Fig. 11K). Transwell assays revealed that GINS1 knockdown significantly inhibited the migratory and invasive capacities of RCC cells. Both si-GINS1-1 and si-GINS1-2 groups showed markedly fewer migrated and invaded cells compared to the si-NC group (Fig. 11L). Besides, Western blot analysis showed that GINS1 knockdown reduced the levels of phosphorylated PI3K and AKT in both 769-P and 786-O cells, while total PI3K and AKT remained unchanged. Consistently, ssGSEA analysis revealed a positive correlation between GINS1 expression and PI3K/AKT pathway activity (Fig. 11M).

Fig. 11.

Diagnostic efficacy of GINS1 in four types of cancers and in vitro experimental validation. Diagnostic performance for KIRP (A), LIHC (B), PAAD (C), and SARC (D). (E) RT-qPCR analysis of GINS1 expression in FFPE tumor tissues and matched adjacent normal tissues from 31 RCC patients. (F and G) The relative mRNA expression levels were quantified via the 2^−ΔΔCT method in both the 769-P and 786-O cell lines, with normalization to β-actin, following the downregulation of GINS1. (H) Protein expression levels were quantified via densitometric analysis (GINS1/β-actin ratio). Downregulation of GINS1 markedly impaired the proliferative, migratory, and invasive abilities of 769-P and 786-O cells, as confirmed by colony formation(I), CCK-8 (J), EdU (K), and Transwell assays(L). (M) GINS1 knockdown suppressed PI3K/AKT pathway activity in RCC cells. Western blot analysis of p-PI3K, PI3K, p-AKT, and AKT in 769-P and 786-O cells (top). ssGSEA showing a positive correlation between GINS1 expression and PI3K/AKT pathway activity (bottom left), with quantification of p-PI3K/PI3K and p-AKT/AKT levels (bottom right). * p < 0.05, ** p < 0.01, ***p < 0.001, **** p < 0.0001

Discussion

In this study, we utilized a large-scale pan-cancer dataset from the UCSC database and employed various bioinformatics approaches to systematically analyze the expression patterns, prognostic significance, immune infiltration, genomic diversity, regulatory pathways, and potential therapeutic relevance of GINS1 across 34 cancer types.

Our research suggested that GINS1 likely plays a significant role in the reproductive and immune systems, but its function appears to be limited to certain neural and metabolic-related tissues. Expression analysis revealed that GINS1 is highly expressed in 31 types of tumors, suggesting that it may function as a crucial pan-cancer oncogenic factor, a potential biomarker for poor prognosis, and a regulatory factor in immunotherapy. The immunofluorescence results subsequently demonstrated that GINS1 exhibited strong fluorescent signals in the nucleus, indicating its potential involvement in nuclear processes. Additionally, the partial colocalization of GINS1 with microtubules and the endoplasmic reticulum suggests its potential roles in cytoskeletal organization and endoplasmic reticulum-related functions. This result also demonstrated that GINS1 cooperates with other subunits in the nucleus to maintain helicase activity, ensuring the continuous unwinding and replication of the DNA strand [30, 31]. In fact, the proliferation of tumor cells is often accompanied by abnormally active DNA replication, and advanced-stage tumors exhibit increased levels of genomic instability [32]. The results indicated that GINS1 exhibited an increasing trend with tumor progression across most cancer types, suggesting that GINS1 might serve as a potential biomarker for tumor staging and play a crucial role in cancer invasiveness and metastatic potential. Here, we focused on investigating the correlation between GINS1 expression and tumor survival and prognosis. High expression of GINS1 was associated with worse OS and DSS in seven types of cancers, including ACC, KIRP, LGG, LIHC, MESO, PAAD, and SARC. Additionally, elevated GINS1 expression was linked to a lower PFI in six cancer types, namely, LGG, LIHC, MESO, PAAD, PRAD, and SARC. These findings align with those of previous studies [15, 33–35], suggesting that GINS1 may serve as a potential prognostic biomarker, with its high expression levels potentially predicting poorer survival outcomes. Next, we applied Cox regression analysis, combining GINS1 expression levels with the clinical parameters of cancer patients. These findings demonstrated that GINS1 acts as an independent prognostic indicator in MESO, LIHC, ACC, and PAAD, providing meaningful information for therapeutic strategies for these cancers.

In tumorigenesis, the TME is of paramount importance, with immune cell infiltration being a key element. Immune cells modulate tumor cell growth, proliferation, and survival by releasing cytokines, chemokines, and other signaling molecules, thereby participating in the process of tumor formation [36, 37]. During tumor growth, macrophages can be polarized toward an M2-like phenotype through Th2 cell activity, thereby driving tumor progression [38]. In contrast, NKT cells increase the host’s antitumor capabilities [39]. Although our pan-cancer analyses revealed consistent associations between GINS1 expression and immune infiltration patterns, particularly reduced NKT cell signatures and increased Th2-related immune populations, these findings are primarily correlative and should be interpreted with caution. In this context, elevated GINS1 expression may reflect a highly proliferative tumor state associated with immunosuppressive microenvironmental features rather than a direct immunoregulatory function. Given that immune infiltration in this study was inferred from bulk transcriptomic data using computational approaches, future mechanistic and single-cell studies will be required to establish causal relationships between GINS1 expression and immune regulation. Subsequently, the PPI network and pathway enrichment analysis of GINS1-associated proteins in this study indicate that GINS1 is a crucial component of DNA replication. Its dysregulation may lead to genomic instability and potentially impact tumor suppression and DNA damage repair mechanisms. Notably, tumorigenesis arises from genetic mutations and genomic instability. High TMB enhances immunotherapy efficacy by generating neoantigens, whereas MSI similarly indicates sensitivity to immune checkpoint inhibitors [40, 41]. RNA modifications such as m6A regulate RNA metabolism, influencing tumor progression and therapy resistance [42]. The interplay between genomic instability and the immune microenvironment constitutes a core mechanism of tumor evolution, providing biomarkers and therapeutic targets for precision oncology. In our research, the widespread occurrence of GINS1 amplification in multiple cancers, along with its positive associations with TMB, MSI, and m6A RNA modification, indicates that GINS1 amplification could induce genomic instability and aggravate mutation accumulation and DNA repair deficiencies in tumor cells. Some studies suggest that tumors with high ESTIMATE scores often exhibit greater invasiveness and metastatic potential, which may be associated with interactions between tumor cells and stromal cells [43]. On the basis of our findings, the coexistence of a high ESTIMATE score and high GINS1 expression may suggest that the tumor is immunogenic but harbors immune escape mechanisms, necessitating the combination of immune checkpoint inhibitors or other immunomodulatory therapies. Conversely, in tumors with a negative correlation, a low ESTIMATE score (high tumor purity) and high GINS1 expression may indicate high genomic instability, potentially rendering immunotherapy ineffective and requiring reliance on chemotherapy or targeted therapies.

Immune checkpoint inhibitors (ICIs) are a class of therapeutic agents designed to combat cancer by enhancing the body’s immune system. They function by blocking the activity of immune checkpoint proteins (e.g., PD-1/PD-L1), thereby restoring or augmenting the ability of T cells to attack cancer cells and suppress tumor growth [44, 45]. Here, the observed differences in survival associations between anti-PD-1- and anti-PD-L1-treated cohorts should be interpreted with caution, as immunotherapy datasets are inherently heterogeneous with respect to tumor types, treatment regimens, and sample sizes. Rather than indicating a direct differential sensitivity, these findings suggest that GINS1-associated tumor-intrinsic states may interact with distinct immune checkpoint contexts in a cohort-dependent manner. Therefore, the immunotherapy-related results in this study should be considered exploratory and hypothesis-generating. Besides, evidence suggests that GINS1 enhances tumor cell metastasis and proliferation by modulating the β-catenin and AKT/mTOR pathways [14, 46]. To further investigate its role, we studied its expression and related pathways in prostate cancer and renal cancer. Elevated expression of GINS1 in prostate cancer and renal cancer tissues, along with its involvement in the cell cycle, tumor proliferation, and DNA repair pathways, underscores its potential role in modulating oncogenic signaling and tumor development. Finally, we revealed that GINS1 has high diagnostic value for KIRP, LIHC, PAAD, and SARC. Consistent with its upregulation in renal cancer tissues observed in FFPE samples, we further investigated the functional role of GINS1 by silencing its expression in renal cancer cell lines. The results demonstrated that silencing GINS1 significantly inhibited cell proliferation, migration, and invasion, indicating its oncogenic role. Moreover, western blot analysis revealed decreased phosphorylation of PI3K and AKT following GINS1 knockdown, suggesting that GINS1 may promote RCC progression at least in part by activating the PI3K/AKT signaling pathway. These findings provide mechanistic insight into the tumor-promoting function of GINS1 and highlight it as a potential therapeutic target in RCC.

Beyond its effects on proliferation, the inhibitory effects of GINS1 knockdown on cell migration and invasion prompted us to further consider the potential mechanisms linking GINS1 to metastatic progression. Our functional assays demonstrated that GINS1 knockdown markedly suppressed the migratory and invasive capacities of renal cancer cells, indicating a potential involvement of GINS1 in metastatic progression. Tumor cell migration and invasion are complex processes that require coordinated cytoskeletal remodeling, extracellular matrix (ECM) degradation, and dynamic interactions with the tumor microenvironment. Matrix metalloproteinases, particularly MMP1 and MMP2, are well-established mediators of ECM remodeling and have been shown to facilitate tumor invasion and metastatic dissemination [47–50]. Emerging evidence suggests that oncogenic signaling pathways associated with cell cycle dysregulation and replication stress, including PI3K/AKT and TGF-β–related programs, can indirectly regulate MMP expression, epithelial–mesenchymal transition (EMT), and the formation of metastasis-associated cellular structures such as invadopodia [51, 52]. Given that GINS1 knockdown attenuated PI3K/AKT pathway activity in our study, it is plausible that GINS1-associated proliferative programs may contribute to metastatic phenotypes by engaging downstream signaling networks that converge on MMP-mediated ECM remodeling and TGF-β-driven EMT processes. Furthermore, metastatic progression is increasingly recognized as a microenvironment-dependent process, in which tumor-intrinsic oncogenic states cooperate with stromal and immune components to create a permissive metastatic niche [53, 54]. In this context, the association between high GINS1 expression, enhanced migratory and invasive behavior, and immunosuppressive microenvironmental features observed in our pan-cancer analyses suggests that GINS1 may be linked to metastatic competence through coordinated regulation of tumor cell–intrinsic and microenvironmental programs. These hypotheses warrant further investigation in future mechanistic studies.

Despite the reliability of our pan-cancer analysis, several limitations should be acknowledged. First, although we leveraged large-scale transcriptomic and clinical datasets, the immune-related findings in this study are derived from computational inference based on bulk transcriptomic data and therefore do not establish direct causal relationships. Further molecular and cellular experiments are required to elucidate the precise mechanisms underlying GINS1-mediated oncogenesis and its association with immune regulation. Second, our study primarily focused on mRNA expression, and future research should explore post-translational modifications and protein-level regulation of GINS1. Additionally, prospective clinical studies are needed to assess GINS1-targeted therapeutic strategies in diverse cancer cohorts.

Conclusions

Our study provides a comprehensive overview of GINS1 in pan-cancer, demonstrating its oncogenic role, prognostic value, and potential as an immunotherapy biomarker. These findings offer new insights into the functional importance of GINS1 in tumor progression and suggest that targeting GINS1 may hold therapeutic promise in multiple malignancies.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Xi Zhang: Conceptualization, Methodology, Investigation, Formal analysis, Writing – Original, Draft, Funding acquisition. Wanyang Lei and Yuqing Pan: Investigation, Data curation, Writing – Original, Draft. Liang Zhong: Supervision, Data curation, Visualization. Li Zhai: Data curation. Haitao Li: Methodology. Beizhong Liu: Supervision, Funding acquisition, Writing – Review & Editing, Funding acquisition.

Funding

This work was supported by the Chongqing Science and Technology Bureau’s Key Technology Innovation Special of Key Industries (No. csct2022ycjhbgzxm0034), the Joint Project of Yunnan Provincial Department of Science and Technology and Kunming Medical University (No. 202101AY070001-165), and the Scientific Research Fund of Education Department of Yunnan Province (No. 2024J0330, 2025J0247).

Data availability

The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the Ethics Committee of Yunnan Cancer Hospital, and all procedures involving human participants complied with the Declaration of Helsinki. Informed written consent was obtained from all participants prior to their enrollment in the study. Written informed consent was obtained from participants to participate in the study.

Competing interests

The authors declare no competing interests.