Abstract
This study was initiated to explore the expression variation, clinical significance, and biological importance of the GINS complex subunit 4 (GINS4) in different human cancers as a shared biomarker via pan-cancer analysis through different platforms including UALCAN, Kaplan Meier (KM) plotter, TNMplot, GENT2, GEPIA, DriverDBv3, Human Protein Atlas (HPA), MEXPRESS, cBioportal, STRING, DAVID, MuTarge, Enrichr, TIMER, and CTD. Our findings have verified the up-regulation of GINS4 in 24 major subtypes of human cancers, and its overexpression was found to be substantially associated with poor overall survival (OS), relapse-free survival (RFs), and metastasis in ESCA, KIRC, LIHC, LUAD, and UCEC. This suggested that GINS4 plays a significant role in the development and progression of these five cancers. Furthermore, we noticed that GINS4 is also overexpressed in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics. Enrichment analysis revealed the involvement of GINS4 associated genes in a variety of diverse GO and KEGG terms. We also explored few significant correlations between GINS4 expression and promoter methylation, genetic alterations, CNVs, other mutant genes, tumor purity, and immune cells infiltration. In conclusion, our results elucidated that GINS4 can serve as a shared diagnostic, prognostic biomarker, and a potential therapeutic target in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics.
Keywords: GINS4, cancer, expression variations, biomarker, tumor purity
Introduction
Cancer is one of the major health threats worldwide and is triggered by several factors, including viral infections, previous history of cancer development, excessive alcohol intake, lack of physical activity, autoimmune, and metabolic disorders [1,2]. According to recent reports, the overall global burden of cancer has risen to 19.3 million new cases, and 10 million deaths in 2020 [3], relative to 18.1 million and 9.6 million, respectively, in 2018 [4]. Despite the rapid and precise interventions in cancer detection approaches developed during the last decade, the prognosis of cancer patients is poor due to distant metastasis occurrence and recurrence [5,6]. In addition, maximum cancer cases are initially detected at advanced stages owing to the lack of reliable and sensitive diagnostic biomarkers, with a 5-year survival rate of less than 20% in many cancer subtypes [7,8]. Therefore, a detailed understanding of the molecular processes governing cancer progression is needed to explore the novel diagnostic and prognostic biomarkers for cancer detection and the development of more effective therapeutic strategies.
The GINS complex is consist of four different subunits, including Sld5, Psf1, Psf2, and Psf3, which are also known as GINS4, GINS1, GINS2, and GINS3. In eukaryotes, the GINS complex binds to Cdc45 and Mcm2-7 to form the replicative helicase CMG complex, which unties double-stranded DNA before moving the replication fork in the replication process [9]. According to previous studies, during the replication process, the GINS complex mainly enhances the enzymatic activity of the minichromosome maintenance (MCM) complex by binding to it, which further helps to recruit the other essential factors involved in the formation of replisome progression complex that leads to the initiation and elongation of replication [10,11]. Furthermore, newly emerging evidence has also reported that GINS may act as a key factor for regulating eukaryotic DNA polymerases such as DNA polymerase (Pol) ε [12] and the DNA Pol α-primase complex [13]. In the GINS complex, GINS4 or sld5 is the most important component that is required for the GINS complex assembly and to initiate and elongate the replication process in eukaryotes [14]. In addition, GINS4 also plays a key role in regulating embryogenesis in mice and cell cycle regulation and maintenance of genomic integrity in Drosophila [15,16]. Previous reports have revealed the GINS4 up-regulation in different human cancers, including breast cancer (BRCA) [17], adrenal cortex adenocarcinoma (ACC) [18], colorectal cancer (CRC) [19], non-small cell lung cancer (NSCLC) [20], bladder cancer [21], pancreatic cancer [22], and gastric cancer [23]. Additionally, it was also observed that elevated GINS4 expression is significantly associated with the lower overall survival (OS) duration of gastric cancer, CRC, NSCLC, and pancreatic cancer patients [19,20,23]. Altogether, GINS4 has a vital contribution to the progression of cancers, and we speculate that it can probably be utilized as an important target for cancer detection and treatment potentially. Moreover, no previous studies about the GINS4 based on pan-cancer analysis.
Therefore, in this study, we attempted to systematically analyze and validate the GINS4 expression across multiple human cancer subtypes using various online available databases and bioinformatics tools. In addition, we analyzed the correlation among GINS4 expression and various other parameters in distinct cancer subtypes, including OS duration, RFS duration, genetic mutations, copy number variations (CNVs), promoter methylation level, tumor purity, and immune cells infiltration. Then, we also identified the GINS4-associated miRNAs, TFs, genes, and performed their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and finally developed a gene-drug interaction network.
Materials and methods
UALCAN
UALCAN (http://ualcan.path.uab.edu/) is an online database that is created to analyze TCGA multi-omics cancer-related data [24]. With the help of UALCAN, we analyzed the transcription expression level of GINS4 in distinct human cancer subtypes through pan-cancer analysis using default settings. The transcription expression level of GINS4 was measured in terms of transcript per million (TPM) reads, and a student t-test was used for statistics purpose. A P-value <0.05 represents the significant scores.
Kaplan-Meier plotter
Kaplan-Meier Plotter tool (https://kmplot.com/analysis/) is developed to check the impact of the gene(s) of interest on the survival duration of patients suffering from distinct types of cancer [25]. In our study, we utilized Kaplan-Meier Plotter tool with default settings to find the association between the GINS4 expression and distinct cancer types related OS and RFS survival rates. For this purpose, the cancer specimens were categorized into two categories based on their median expression level (high expression level v/s low expression level), and a P-value <0.05 was used to represent the significant scores.
TNMplot database
TNMplot (https://www.tnmplot.com/) [26] was used in this study to analyze GINS4 expression in normal and metastatic tissues of different cancers. For statistics purpose, a student’s t-test was employed in this database, and a P-value <0.05 was used to represent the significant scores.
GENT2, GEPIA, DriverDBv3, and HPA databases
GENT2 (http://gent2.appex.kr/), GEPIA (http://gepia.cancer-pku.cn/) DriverDBv3 (http://driverdb.tms.cmu.edu.tw/), and Human Protein Atlas (HPA) (https://www.proteinatlas.org/) database offer a reliable multi-omics analysis of the cancer-related TCGA data [27-29]. In this study, to validate the transcription and translation expression levels of GINS4 in distinct cancer subtypes, we employed these databases to analyze the GINS4 differential expression patterns in new independent cancer cohorts with default settings. In GENT2, GEPIA, and DriverDBv3 databases, the transcription expression level was measured in terms of transcript per million (TPM) reads, and a student t-test was used for statistics purpose. While in HPA, the protein expression level was graded as not detected, low, medium, and high, based on the intensity of staining and fraction of the stained cells. A P-value <0.05 represents the significant scores.
MEXPRESS
MEXPRESS (https://mexpress.be/) is developed to visualize the TCGA expression data and identify correlations between promoter methylation and expression level [30]. In this study, the correlation between GINS4 transcription expression and promoter methylation levels in distinct cancer subtypes were computed via this tool using Pearson correlation analysis. A P-value <0.05 represents the significant scores.
The cBioportal database
cBioPortal (http://www.cbioportal.org/) is a user-friendly application that offers data on genetic mutations, copy number variations (CNVs), and transcription expression from samples of various cancer subtypes [31]. In this study, we chose TCGA PanCancer Atlas datasets to investigate GINS4-associated genetic mutations and mutational hotspots in different human cancers using default settings.
PPI network construction, visualization, functional, and pathway analysis
In the current study, STRING (Search Tool for the Retrieval of Interacting Genes) biological tool [32] was used to obtain the protein-protein interaction (PPI) network of GINS4-associated genes with a confidence score of ≥ 0.7. Later, functional, and pathway analysis of GINS4 enriched genes was performed via DAVID (v6.8, http://david.ncifcrf.gov/summary.jsp) [33] and a P-value was used <0.05 to represents the significant scores.
Correlation between GINS4 and its associated genes across different cancers
The GEPIA (http://gepia.cancer-pku.cn/) was conducted in this study to evaluate pairwise gene correlations between GINS4 and its other associated genes using the “Correlation Analysis” module. A P-value <0.05 represents the significant scores.
Enrichr database analysis
Enrichr (https://maayanlab.cloud/Enrichr/) [34] was used in this study with default settings to identify GINS4 targeted miRNAs and TFs from TRRUST 2019 and miRTarBase 2017 sources. The top 10 significantly (P<0.05) enriched items were displayed using Enrichr.
MuTarget analysis
The MuTarget (https://www.mutarget.com/result) is an online platform that associates gene expression alterations with mutational status in human cancers. Via this platform, mutant genes altering the expression of a gene of interest could be identified [35]. In our study, we used this platform to identify the mutant genes responsible for the expression alteration in the GINS4 gene in different cancers with default thresh-holds of P<0.05 and FC >1.4.
Tumor purity, immune cells infiltration, and GINS4 expression in cancer patients of distinct subtypes
The TIMER database (https://cistrome.shinyapps.io/timer/) offers helpful services to analyze the association between gene expression, tumor purity, and the infiltration level of different immune cells [36]. In this study, GINS4 was queued in the ‘Gene module’ tool of TIMER to find the Spearman correlation between tumor purity, immune cells infiltration such as B cells, macrophages, neutrophils, CD4+ T cells, and CD8+ T cells, and GINS4 expression in distinct cancer subtypes using default settings. A P-value <0.05 represents the significant scores.
GINS4 gene-drug interaction network analysis
The GINS4 gene-drug interaction network was built via Cytoscape 3.8.0 based on the data obtained from the Comparative Toxicogenomics Database (CTD) with default settings [37]. By queuing the CTD database, different potential compounds that are capable to regulate GINS4 expression were identified through this network.
Results
GINS4 expression in pan-cancer
In this study, we used UALCAN to analyze the GINS4 transcription expression across 24 major human cancers relative to controls. Our results showed that GINS4 expression was elevated significantly (P<0.05) in all 24 cancer subtypes, especially in Liver hepatocellular carcinoma (LIHC), Cholangiocarcinoma (CHOL), Kidney renal papillary cell carcinoma (KIRP), Esophageal carcinoma (ESCA), Colon adenocarcinoma (COAD), Cervical squamous cell carcinoma (CESC), Breast invasive carcinoma (BRCA), and Stomach adenocarcinoma (STAD) (Figure 1).
Figure 1.
Differential transcription expression analysis of GINS4 gene in cancerous and normal tissues via pan-cancer cancer analysis using UALCAN. (A) Pan-cancer expression analysis results of GINS4 across cancerous samples paired with normal controls, and (B) Pan-cancer expression analysis results of GINS4 in only cancer samples. Blue color represents the normal samples while red color indicates the cancer samples. *P<0.05.
Correlation analysis of GINS4 expression with OS, RFS, and metastasis
We used the KM plotter tool to analyze the association between GINS4 expression and OS or RFS in 24 human cancer subtypes. The obtained KM curves highlighted that elevated expression of GINS4 was significantly (P<0.05) linked to the reduced OS and RFS duration in five subtypes of cancer including ESCA (HR =2.8, 95% CI: 1.35-4.93, P=0.0029, HR =3.46, 95% CI: 0.49-24.72, P=0.019 ), KIRC (HR =1.51, 95% CI: 1.11-2.05, P=0.008, HR =2.15, 95% CI: 0.78-6.78, P=0.018), LIHC (HR =1.79, 95% CI: 1.24-2.59, P=0.0017, HR =1.55, 95% CI: 1.12-2.16, P=0.0084), LUAD (HR =1.59, 95% CI: 1.18-2.12, P=0.0018, HR =2.01, 95% CI: 1.29-3.14, P=0.0017), and UCEC (HR =2.13, 95% CI: 1.39-3.27, P=0.00038, HR =1.7, 95% CI: 1.01-2.87, P=0.044) UCEC (Figure 2A, 2B). Furthermore, GINS4 notable overexpression was also found in the metastatic samples of ESCA, KIRC, LIHC, LUAD, and UCEC relative to primary tumor samples and normal controls (Figure 2C). Altogether, our data suggested that GINS4 might have a significant contribution to the development and progression of ESCA, KIRC, LIHC, LUAD, and UCEC, thus the next part of this study will primarily focus on the unique role of GINS4 in those five types of human cancers.
Figure 2.
High expression level of GINS4 expression is an adverse prognostic factor in ESCA, KIRC, LIHC, LUAD, and UCEC. (A) Survival analysis revealed that higher GINS4 expressions reduced OS duration in ESCA, KIRC, LIHC, LUAD, and UCEC, (B) Survival analysis revealed that higher GINS4 expressions reduced RFS duration in ESCA, KIRC, LIHC, LUAD, and UCEC, and (C) A correlation analysis of GINS4 with metastasis in ESCA, KIRC, LIHC, LUAD, and UCEC tissues. A P-value <0.05 was considered as significant.
GINS4 expression in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological features
Generally, gene expression is often varied clinicopathological features-wise. We analyzed the relationship between GINS4 expression and different clinicopathological features of ESCA, KIRC, LIHC, LUAD, and UCEC using the UALCAN database. Our results demonstrated that GINS4 expression level was closely correlated with the clinicopathological features of ESCA, KIRC, LIHC, LUAD, and UCEC including cancer stages, races, genders, and ages (Table 1). The clinicopathological features of the ESCA, KIRC, LIHC, LUAD, and UCEC cohorts are provided in Supplementary Tables 1, 2 and 3.
Table 1.
Clinicopathalogical features-specific expression pattern of GINS4 in ESCA, KIRC, LIHC, LUAD, and patients
| GINS4 expression across ESCA patients with distinct clinicopathological features | |||
| Different cancer stages-based GINS4 expression pattern relative to normal (n=11) control samples | Stage 1 (n=13) | ↑ (up-regulation) | P-value <0.05 |
| Stage 2 (n=78) | ↑ (up-regulation) | ||
| Stage 3 (n=55) | ↑ (up-regulation) | ||
| Stage 4 (n=9) | ↑ (up-regulation) | ||
| Different patient’s races-based GINS4 expression pattern relative to normal (n=11) control samples | Caucasian (n=113) | ↑ (up-regulation) | P-value <0.05 |
| African-American (n=5) | ↑ (up-regulation) | ||
| Asian (n=46) | ↑ (up-regulation) | ||
| Different patient’s gender-based GINS4 expression pattern relative to normal (n=11) control samples | Male (n=157) | ↑ (up-regulation) | P-value <0.05 |
| Female (n=26) | ↑ (up-regulation) | ||
| Different patient’s ages-based GINS4 expression pattern relative to normal (n=11) control samples | 21-40 Yrs (n=3) | ↑ (up-regulation) | P-value <0.05 |
| 41-60 Yrs (n=89) | ↑ (up-regulation) | ||
| 61-80 Yrs (n=76) | ↑ (up-regulation) | ||
| 81-100 Yrs (n=15) | ↑ (up-regulation) | ||
| GINS4 expression across KIRC patients with distinct clinicopathological features | |||
| Different cancer stages-based GINS4 expression pattern relative to normal (n=72) control samples | Stage 1 (n=267) | ↑ (up-regulation) | P-value <0.05 |
| Stage 2 (n=57) | ↑ (up-regulation) | ||
| Stage 3 (n=123) | ↑ (up-regulation) | ||
| Stage 4 (n=84) | ↑ (up-regulation) | ||
| Different patient’s races-based GINS4 expression pattern relative to normal (n=72) control samples | Caucasian (n=462) | ↑ (up-regulation) | P-value <0.05 |
| African-American (n=56) | ↑ (up-regulation) | ||
| Asian (n=8) | ↑ (up-regulation) | ||
| Different patient’s gender-based GINS4 expression pattern relative to normal (n=72) control samples | Male (n=345) | ↑ (up-regulation) | P-value <0.05 |
| Female (n=185) | ↑ (up-regulation) | ||
| Different patient’s ages-based GINS4 expression pattern relative to normal (n=72) control samples | 21-40 Yrs (n=26) | ↑ (up-regulation) | P-value <0.05 |
| 41-60 Yrs (n=238) | ↑ (up-regulation) | ||
| 61-80 Yrs (n=246) | ↑ (up-regulation) | ||
| 81-100 Yrs (n=23) | ↑ (up-regulation) | ||
| GINS4 expression across LIHC patients with distinct clinicopathological features | |||
| Different cancer stages-based GINS4 expression pattern relative to normal (n=50) control samples | Stage 1 (n=168) | ↑ (up-regulation) | P-value <0.05 |
| Stage 2 (n=84) | ↑ (up-regulation) | ||
| Stage 3 (n=82) | ↑ (up-regulation) | ||
| Stage 4 (n=6) | ↑ (up-regulation) | ||
| Different patient’s races-based GINS4 expression pattern relative to normal (n=50) control samples | Caucasian (n=177) | ↑ (up-regulation) | P-value <0.05 |
| African-American (n=17) | ↑ (up-regulation) | ||
| Asian (n=157) | ↑ (up-regulation) | ||
| Different patient’s gender-based GINS4 expression pattern relative to normal (n=50) control samples | Male (n=245) | ↑ (up-regulation) | P-value <0.05 |
| Female (n=117) | ↑ (up-regulation) | ||
| Different patient’s ages-based GINS4 expression pattern relative to normal (n=50) control samples | 21-40 Yrs (n=27) | ↑ (up-regulation) | P-value <0.05 |
| 41-60 Yrs (n=140) | ↑ (up-regulation) | ||
| 61-80 Yrs (n=181) | ↑ (up-regulation) | ||
| 81-100 Yrs (n=10) | ↑ (up-regulation) | ||
| GINS4 expression across LUAD patients with distinct clinicopathological features | |||
| Different cancer stages-based GINS4 expression pattern relative to normal (n=59) control samples | Stage 1 (n=277) | ↑ (up-regulation) | P-value <0.05 |
| Stage 2 (n=125) | ↑ (up-regulation) | ||
| Stage 3 (n=85) | ↑ (up-regulation) | ||
| Stage 4 (n=28) | ↑ (up-regulation) | ||
| Different patient’s races-based GINS4 expression pattern relative to normal (n=59) control samples | Caucasian (n=387) | ↑ (up-regulation) | P-value <0.05 |
| African-American (n=51) | ↑ (up-regulation) | ||
| Asian (n=08) | ↑ (up-regulation) | ||
| Different patient’s gender-based GINS4 expression pattern relative to normal (n=59) control samples | Male (n=238) | ↑ (up-regulation) | P-value <0.05 |
| Female (n=276) | ↑ (up-regulation) | ||
| Different patient’s ages-based GINS4 expression pattern relative to normal (n=59) control samples | 21-40 Yrs (n=12) | ↑ (up-regulation) | P-value <0.05 |
| 41-60 Yrs (n=90) | ↑ (up-regulation) | ||
| 61-80 Yrs (n=149) | ↑ (up-regulation) | ||
| 81-100 Yrs (n=32) | ↑ (up-regulation) | ||
| GINS4 expression across UCEC patients with distinct clinicopathological features | |||
| Different cancer stages-based GINS4 expression pattern relative to normal (n=35) control samples | Stage 1 (n=341) | ↑ (up-regulation) | P-value <0.05 |
| Stage 2 (n=52) | ↑ (up-regulation) | ||
| Stage 3 (n=124) | ↑ (up-regulation) | ||
| Stage 4 (n=29) | ↑ (up-regulation) | ||
| Different patient’s races-based GINS4 expression pattern relative to normal (n=35) control samples | Caucasian (n=374) | ↑ (up-regulation) | P-value <0.05 |
| African-American (n=107) | ↑ (up-regulation) | ||
| Asian (n=20) | ↑ (up-regulation) | ||
| Different patient’s gender-based GINS4 expression pattern relative to normal (n=35) control samples | Male (n=268) | ↑ (up-regulation) | P-value <0.05 |
| Female (n=147) | ↑ (up-regulation) | ||
| Different patient’s ages-based GINS4 expression pattern relative to normal (n=35) control samples | 21-40 Yrs (n=18) | ↑ (up-regulation) | P-value <0.05 |
| 41-60 Yrs (n=189) | ↑ (up-regulation) | ||
| 61-80 Yrs (n=292) | ↑ (up-regulation) | ||
| 81-100 Yrs (n=45) | ↑ (up-regulation) | ||
GINS4 expression validation in new cohorts
Based on GENT2, GEPIA, DriverDBv3, and HPA databases, we further validated GINS4 expression at both transcriptional and translational levels using independent cohorts of ESCA, KIRC, LIHC, LUAD, and UCEC. As per expectations, our results were in agreement with the results of UALCAN, indicating the robustness of the evidence. The expression analysis via GENT2, GEPIA, and DriverDBv3 revealed the significant (P<0.05) higher expression of GINS4 in ESCA, KIRC, LIHC, LUAD, and UCEC patients relative to normal controls at the transcriptional level (Figure 3A-C), moreover, the expression analysis of GINS4 via HPA also revealed that normal esophageal, kidney, liver, lung, and endometrial tissues had low GINS4 IHC staining, while cancer tissues had medium or high staining (Figure 3D). Taken together, our results have validated that GINS4 is overexpressed at both transcriptional and translational levels in ESCA, KIRC, LIHC, LUAD, and UCEC as compared to the normal controls.
Figure 3.
Transcription and translational level expression validation of GINS4 in new independent cohorts of ESCA, KIRC, LIHC, LUAD, and UCEC via GENT2, GEPIA, DriverDBv3 and HPA databases. (A) Transcription level expression validation of GINS4 via GENT2, (B) Transcription level expression validation of GINS4 via GEPIA, (C) Transcription level expression validation of GINS4 via DriverDBv3, and (D) Translation level expression validation of GINS4 via HPA. A P-value of <0.05 was selected as cutoff criterion.
GINS4 promoter methylation negatively correlated its expression
Hypermethylation of the gene promoter region regulates transcriptional silencing. On the other hand, hypomethylation can result in the enhanced gene expression. A variety of cancers has been linked to the promoter-specific methylation levels and accompanied gene dysregulation [38]. In this study, we have chosen GINS4 methylation sites from the MEXPRESS database. This is one of the most reliable databases built to analyze the association between gene expression and methylation levels at CpG islands. As shown in Figures 4 and 5, we observed that the promoter methylation values obtained from the different CpG dinucleotides in BLCA, HNSC, KIRP, LUAD, and UCEC were significant (P<0.05) negatively correlated with GINS4 expression levels.
Figure 4.
A MRXPRESS based correlation analysis between GINS4 expression and its promoter methylation in ESCA, KIRC, and LIHC. (A) In ESCA, (B) In KIRC, and (C) In LIHC. A negative sign indicates the negative correlation between GINS4 expression and its promoter methylation using a specific probe at a specific CpG island. A P-value of <0.05 was selected as cutoff criterion.
Figure 5.
A MRXPRESS based correlation analysis between GINS4 expression and its promoter methylation in LUAD and UCEC. (A) In LUAD, and (B) In UCEC. A negative sign indicates the negative correlation between GINS4 expression and its promoter methylation using a specific probe at a specific CpG island. A P-value of <0.05 was selected as cutoff criterion.
Genetic alterations of GINS4
For inquiring about GINS4-associated genetic alterations and CNVs we used the cBioportal database. In this analysis, PanCancer Atlas ESCA, KIRC, LIHC, LUAD, and UCEC datasets were queued and genetic alterations and CNVs were observed in only 7%, 1.1%, 6%, 6%, and 6% cases of ESCA, KIRC, LIHC, LUAD, and UCEC, respectively (Figure 6A). Deep amplifications abnormality was most common in these cancers followed by deep deletions (Figure 6A). Taken together, it is speculated that GINS4 harbors genetic alteration in small numbers of ESCA, KIRC, LIHC, LUAD, and UCEC samples.
Figure 6.
GINS4 genetic alterations, CNVs, and mutational hotspot status in ESCA, KIRC, LIHC, LUAD, and UCEC. (A) Genetic alterations and CNVs status of GINS4 in ESCA, KIRC, LIHC, LUAD, and UCEC and (B) Mutational hotspot analysis of GINS4 in ESCA, KIRC, LIHC, LUAD, and UCEC.
Mutational hotspot analysis of GINS4
To further identify the mutational hotspots of GINS4 in ESCA, KIRC, LIHC, LUAD, and UCEC cancer subtypes, we analyzed PanCancer Atlas ESCA, KIRC, LIHC, LUAD, and UCEC datasets using cBioportal. In ESCA, LIHC, and LUAD, the mutational hotspots of the most frequently observed mutations, including one nonsense mutation (Q567*) in ESCA, one missense mutation (D6G) in LIHC, and other one missense mutation (A145S) in LUAD lie outside the Sld5 domain of the GINS4, which plays an important role in the initiation of the replication process. On the other hand, in UCEC, the GINS4 mutational hotspots of the most frequently observed missense mutation (P119H) lies within the Sld5 domain (Figure 6B). Moreover, no GINS4 mutation was identified in case of KIRC (Figure 5B). Taken together, we observed different GINS4 mutational hotspots in ESCA, LIHC, LUAD, and UCEC which overall suggested a high level of complexity regarding GINS4 mutations.
A PPI network and enrichment analysis of GINS4
We further conducted STRING and Cytoscape analysis to identify the GINS4 enriched genes. Functional interaction network analysis showed that GINS4 physically interacts with 23 different other genes (Figure 7). We next performed the GO and KEGG analysis of GINS4 associated genes via DAVID tool to determine the GINS4 associated genes functions and pathways. Results revealed the enrichment of GINS4-associated genes in biological processes (BP), molecular function (MF), cell composition (CC), and KEGG pathways. GINS4-associated genes were significantly (P<0.05) enriched in DNA replication BP, DNA helicase activity MF, MCM complex CC, and different KEGG terms, including DNA replication, Cell cycle, Glucagon signaling pathway, Biosynthesis of antibiotics, and Cysteine and methionine metabolism (Figure 7; Supplementary Table 4).
Figure 7.
A PPI network, GO and KEGG analysis of the GINS4 enriched genes. (A) A PPI network of GINS4 enriched genes, (B) BP functional classification terms of the GINS4 enriched genes, (C) MF functional classification terms of the GINS4 enriched genes, and (D) CC functional classification terms of the GINS4 enriched genes, and (E) KEGG classification terms of the GINS4 enriched genes. A P-value <0.05 was considered as significant.
Correlation analysis between GINS4 and the expression of its other associated genes
Via GEPIA, we further analyzed the correlations among GINS4 and its other physically associated 23 genes expression across ESCA, KIRC, LIHC, LUAD, and UCEC samples. In view of our results, GINS4 expression was found to be positively correlated with the expressions of all of its associated genes including RECQL4, SSRP1, TIPIN, NCDN, LDHB, GINS3, DONSON, KIF16B, GINS2, MCM2, MCM3, PPIL3, PAICS, MCM5, AHCYL1, ADHA1, MCM7, GINS1, WDHD1, SIK1, DUSP13, CD2BP2, and POLA1 (Figure 8).
Figure 8.
A GEPIA-based correlation analysis among GINS4 and its other associated genes expression across ESCA, KIRC, LIHC, LUAD, and UCEC samples. A P-value <0.05 was considered to indicate a statistically significant result.
Identification of miRNAs and TFs that potentially regulate GINS4 expression
Through enrichr, we predicted ten highly significant miRNAs (hsa-miR-193b-3p, hsa-miR-215-5p, hsa-miR-192-5p, hsa-miR-3613-3p, hsa-miR-372-5p, hsa-miR-373-5p, hsa-miR-371b-5p hsa-miR-616-5p, hsa-miR-371a-5p, and hsa-miR-6849-5p), and ten TFs (E2F4, RBL2, MEN1, E2F1, E2F3, BRCA1, E2F4, OTX2, FOXM1, and FOXO3) which could potentially regulate GINS4 expression (Figure 9). Ultimately, all these clues indicate that GINS4 expression can be regulated through different miRNAs and TFs.
Figure 9.
Identification of GINS4 targeted miRNAs and TFS via Enrichr database. (A) GINS4 targeted miRNAs, and (B) GINS4 targeted TFS. A P-value <0.05 was considered as significant.
Correlations among GINS4 expression and crucial mutant genes
To correlate GINS4 expression with different other mutant genes, we used MuTarget to select top the 3 mutant genes associated with GINS4 in ESCA, KIRC, LIHC, LUAD, and UCEC, respectively, with default settings. The selected top 3 mutant genes which are positively correlated with the expression GINS4 are TP53, NELL2, and RUNX1 in ESCA, SLC22A4, PTPRZ1, and VARS2 in KIRC, TP53, CSMD3, and CDH10 in LIHC, TP53, KIF19, and RB1 in LUAD, and TP53, TCOF1, and ZNF780A in UCEC (Figure 10). Collectively, this information revealed that GINS4 strongly correlates with different other mutant genes in ESCA, KIRC, LIHC, LUAD, and UCEC. This new information may also enhance the knowledge of cancer development in those cancer subtypes.
Figure 10.
Positively correlated mutant genes with GINS4 in CESC, ESCA, HNSC, and KIRC from MuTarget. (A) Top 3 correlated genes with GINS4 in ESCA, (B) Top 3 correlated genes with GINS4 in KIRC, (C) Top 3 correlated genes with GINS4 in LIHC, (D) Top 3 correlated genes with GINS4 in LUAD, and (E) Top 3 correlated genes with GINS4 in UCEC. A P-value <0.05 was consider as significant.
Tumor purity and immune cells infiltration analysis of GINS4
Considering the involvement of GINS4 in the regulation of different pathways, including cell cycle and DNA replication, it was hypothesized that GINS4 expression level variations may contribute to alterations in the immune cells infiltration and may also associate with tumor purity. Therefore, we used the TIMER algorithm to evaluate the correlation among tumor purity, immune cells infiltrations including B cells, macrophages, neutrophils, CD4+ T cells, and CD8+ T cells level and GINS4 expression in ESCA, KIRC, LIHC, LUAD, and UCEC. As per the tumor purity analysis, we observed a negative correlation between GINS4 expression and tumor purity in KIRC (Rho =-0.098, P-value =3.56e-02) and LUAD (Rho =-0.002, P-value =961e-01) while positive correlation in ESCA (Rho =0.254, P-value =5.60e-04), LIHC (Rho =0.122, P-value =2.38e-02), and UCEC (Rho =0.054, P-value =3.53e-01) (Figure 10). Moreover, we also observed a different correlations between GINS4 expression and immune cells infiltration in those cancers, like in case of B cells, our results revealed a negative correlation between B cells infiltration and GINS4 expression in KIRC (Rho =-0.073, P-value =1.16e-01), LUAD (Rho =-0.188, P-value =2.78e-05), and UCEC (Rho =-0.175, P-value = 1.03e-01) while a positive correlation in LIHC (Rho =0.359, P-value =6.34e-12), and ESCA (Rho =0.047, P-value =5.33e-01) (Figure 11). In case of macrophages, a positive correlation was revealed between macrophages infiltration and GINS4 expression in ESCA (Rho =0.009, P-value =9.06e-01), KIRC (Rho =0.252, P-value =4.17e-08), LIHC (Rho =0.35, P-value =2.14e-11), and LUAD (Rho =0.162, P-value =2.97e-04) while a negative correlation in UCEC (Rho =-0.28, P-value =8.26e-03). In case of neutrophils, a positive correlation was observed between neutrophils infiltration and GINS4 expression in ESCA (Rho =0.08, P-value =2.87e-01), KIRC (Rho =0.364, P-value =7.40e-16), LIHC (Rho =0.193, P-value =3.01e-04), LUAD (Rho =0.217, P-value =1.13e-06), and UCEC (Rho =0.14, P-value =1.93e-01). In case of CD4+ T cells, a positive correlation was seen between CD4+ T cells infiltration and GINS4 expression in ESCA (Rho =0.044, P-value =5.56e-01), KIRC (Rho =0.212, P-value =4.26e-06), LIHC (Rho =0.161, P-value =2.64e-03), and UCEC (Rho =0.01, P-value =9.26e-01) while a negative correlation in LUAD (Rho =-0.124, P-value =5.77e-03). Finally, in case of CD8+ T cell, a positive correlation was seen between CD8+ T cells infiltration and GINS4 expression in ESCA (Rho =0.006, P-value =9.36e-01), LIHC (Rho =0.163, P-value =2.41e-03), and LUAD (Rho =0.124, P-value =5.76e-03) while a negative correlation in KIRC (Rho =-0.115, P-value =7.54e-01) and UCEC (Rho =0.31, P-value =3.26e-03) (Figure 11).
Figure 11.
GINS4 correlation with tumor purity and immune cells infiltration in ESCA, KIRC, LIHC, LUAD, and UCEC. (A) GINS4 correlation with tumor purity and immune cells infiltration in ESCA, (B) GINS4 correlation with tumor purity and immune cells infiltration in KIRC, (C) GINS4 correlation with tumor purity and immune cells infiltration in LIHC, (D) GINS4 correlation with tumor purity and immune cells infiltration in LUAD, and (E) GINS4 correlation with tumor purity and immune cells infiltration in UCEC. A P-value (<0.05) was considered as statistical significant.
Gene-drug interaction network analysis of the GINS4
To identify different available potential compounds targeting GINS4, a gene-drug interaction network was carried out using the Comparative Toxicogenomics Database (CTD) and Cytoscape. As highlighted in Figure 12, a total of 18 compounds were identified that could impact GINS4 expression. For example, aflatoxin B1 and dorsomorphin could elevate the expression level of GINS4 while cyclosporine and bisphenol A could reduce GINS4 expression level (Figure 12).
Figure 12.
Gene-drug interaction network of the GINS4 and chemotherapeutic drugs. Red arrows: drugs that increase GINS4 expression; green arrows: drugs that decrease GINS4 expression. The numbers of arrows in this network represent the supported numbers of literatures by previous reports.
Discussion
Cancer is characterized by poor clinical outcomes and a higher rate of mortality [39]. Therefore, cancer patients often have the worst prognosis and thus, it is urgent to disclose the potentially shared ideal molecular biomarker for different cancers together that could help to enhance the diagnosis and treatment efficacy of these cancers as a shared target.
GINS complex was initially discovered by Boskovic et al. [40]. Recent data suggested that one of the main GINS complex subunits, the GINS4, is overexpressed in a few cancer subtypes including breast cancer (BRCA) [17], adrenal cortex adenocarcinoma (ACC) [18], colorectal cancer (CRC) [19], non-small cell lung cancer (NSCLC) [20], bladder cancer [21], pancreatic cancer [22], and gastric cancer [23]. Nevertheless, the GINS4 effect on different other cancer subtypes is relatively unknown. Via detailed pan-cancer analysis, we analyzed the feasibility of utilizing GINS4 as an ideal diagnostic, prognostic biomarker, and therapeutic target for several cancer subtypes.
In this study, our results revealed that the levels of GINS4 expression in all the 24 major cancers tissue, including LIHC, CHOL, KIRP, ESCA, COAD, CESC, BRCA, and STAD was significantly (P<0.05) elevated relative to normal tissues. We further revealed that the up-regulation of GINS4 is generally associated with the reduced OS, RFS durations and advanced metastasis of ESCA, KIRC, LIHC, LUAD, and UCEC patients. Taken together, these findings suggested that GINS4 may play an important role in the initiation, development, and progression of ESCA, KIRC, LIHC, LUAD, and UCEC, therefore, the current investigation focuses on these five cancer subtypes. Following OS, RFS, and metastasis analyses, we further explored the correlation between GINS4 overexpression and different clinicopathological features of ESCA, KIRC, LIHC, LUAD, and UCEC. In view of the results of this analysis, we have also observed a notable overexpression of GINS4 in different clinicopathological features of ESCA, KIRC, LIHC, LUAD, and UCEC including different cancer stages, patient’s races, patient’s genders, and patients ages as compared to the normal controls.
The GINS4 expression can be influenced by different factors such as promoter methylation, genetic alteration, and CNVs [41]. Therefore, in our study, we utilized MEXPRESS and cBioPortal online resources to analyze the correlation between GNS4 expression and its promoter methylation and genetic alterations in ESCA, KIRC, LIHC, LUAD, and UCEC samples. Our results revealed a significant negative correlation between GINS4 expression and its promoter methylation levels in ESCA, KIRC, LIHC, LUAD, and UCEC patients. We further revealed low percentages (7%, 1.1%, 6%, 6%, and 6%) of the GINS4 genetic alterations and CNVs in ESCA, KIRC, LIHC, LUAD, and UCEC, respectively. Additionally, it was also observed that mutations in GINS4 could change amino acids at different sites of the encoded protein. Taken together these results, we speculated that promoter hypermethylation may have a solid impact on the expression regulation of GINS4 while genetic alterations and CNVs may have very little or possibly no impact on the expression regulation of GINS4 in ESCA, KIRC, LIHC, LUAD, and UCEC.
Although, a growing number of studies have discovered numerous expression-based biomarkers in ESCA, KIRC, LIHC, LUAD, and UCEC including different genes, such as EGFR, VEGF, ER, E-cadherin, α-catenin, and β-catenin, p53, MAP3K3, and ASPM, in ESCA [42,43], MYC, VHL, PBRM1, BAP1, PTGS2, ALB, TOP2A, CDK1, AKT1, VEGFA, CASR, MMP9, PTPRC, and EGFR in KIRC [44,45], FOS, EPHA2, IGFBP3, ID1, DUSP6, MT1G, SNRPD2, MT1H, FGA, SOCS2, LMNB1, ITIH2, KNG1, EGR1, PRR11, FGG, APOA1, AHSG, F2, FOS, DUSP1, APOA2, APOB, and PROC, in LIHC [46,47], CDH1, PECAM1, SPP1, IL6, THBS1, SNCA, HGF, CAV1, DLC1, and CDH5 in LUAD [48,49], RNF183, FGFs, FGFRs, ADCY7, and ZBTB7A in UCEC [50-52]. However, best to our knowledge, none of these or any other biomarkers have been generalized so far in ESCA, KIRC, LIHC, LUAD, and UCEC patients of different clinicopathological features. Therefore, the heterogeneity-specific behavior of these markers leads to the high ESCA, KIRC, LIHC, LUAD, and UCEC-associated mortality rates and remains a major therapeutic obstacle for clinicians and doctors. In the current study, a notable overexpression of GINS4 was observed in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological parameters, including different cancer stages, patient’s races, genders, and age groups relative to controls. Furthermore, GINS4 prognostic values and promoter methylation levels have also proven its useful significance as a novel potential biomarker of these cancers. Therefore, our study is the first to report a shared clinicopathological features-specific diagnostic and prognostic potential of GINS4 in five different cancers including ESCA, KIRC, LIHC, LUAD, and UCEC, which may open up new therapeutic avenues for these cancer patients.
Furthermore, to know the possible roles of miRNAs and TFs in the dysregulation of GINS4, we predicted the potential miRNAs and TFs of GINS4 using Enrichr from TRRUST 2019 and miRTarBase 2017 sources. Our results revealed the ten most significant miRNAs and TFs that can potentially regulate GINS4 expression, including hsa-miR-193b-3p, hsa-miR-215-5p, hsa-miR-192-5p, hsa-miR-3613-3p, hsa-miR-372-5p, hsa-miR-373-5p, hsa-miR-371b-5p hsa-miR-616-5p, hsa-miR-371a-5p, and hsa-miR-6849-5p miRNAs and E2F4, RBL2, MEN1, E2F1, E2F3, BRCA1, E2F4, OTX2, FOXM1, and FOXO3 TFs. This important piece of information might also help to understand the GINS4 oncogenic roles in more detail.
Next, we have also identified different mutant genes that can alter GINS4 expression via MuTarget. The top 3 mutant genes that we selected in each ESCA, KIRC, LIHC, LUAD, and UCEC, respectively, are TP53, NELL2, and RUNX1 in ESCA, SLC22A4, PTPRZ1, and VARS2 in KIRC, TP53, CSMD3, and CDH10 in LIHC, TP53, KIF19, and RB1 in LUAD, and TP53, TCOF1, and ZNF780A in UCEC. By connecting these mutant genes with GINS4 expression, it is easier for clinicians to identify potential multi-gene-based therapies for ESCA, KIRC, LIHC, LUAD, and UCEC patients.
Previous studies have revealed that accessing the relationships between immune cells infiltration, tumor purity, and biomarker gene expression is quite valuable for developing the appropriate immunotherapy [53]. Based on the markers gene expression, different studies have explored correlations between tumor purity and marker gene expression to predict the clinical outcomes in different cancers [54]. Moreover, in a recent study, Nataliya et al. have accessed the immune cells in the normal and cancerous human HNSC tissues using the CIBERSORT algorithm. In view of their results, it was observed that different immune cells, including neutrophil, B cells, CD4+ cells, and CD8+ T cells were increased in the cancerous tissues relative to normal controls [55]. However, little information is already available regarding tumor purity and the prognostic roles of immune cells infiltration in ESCA, KIRC, LIHC, LUAD, and UCEC patients. Interestingly, in our study, we revealed that GINS4 was noticeably correlated with the tumor purity and immune cells infiltration, which may help clinicians to gain deeper insights into the tumor microenvironment landscape of ESCA, KIRC, LIHC, LUAD, and UCEC. However, we lack direct evidence on how GINS4 regulates tumor purity and immune cells infiltration in these cancers, therefore, the precise pathways and mechanisms need further studies.
The PPI network of GINS4 has shown that it directly interacts with 23 different other genes, and correlation analysis between GINS4 and these genes expression has revealed a strong positive correlation. Moreover, GINS4 associated genes were found significantly (P<0.05) enriched in DNA replication BP, DNA helicase activity MF, MCM complex CC, and different KEGG terms including DNA replication, Cell cycle, Glucagon signaling pathway, Biosynthesis of antibiotics, and Cysteine and methionine metabolism. These results have shown that GINS4 might be involved in a variety of BP, MF, and CC by interacting with its associated genes that participate in caner development. In addition, the two most significant KEGG terms including DNA replication and cell cycle are important processes involved in duplication, growth, and division of the genome [56,57]. Dysregulation of DNA replication and the cell cycle are one of the most common events in cancer development [58-60]. Moreover, defects in these pathways have also been reported to have an adverse effect on cancer prognosis [61,62]. Our study suggested that GINS4, via its associated genes, may play a critical role in tumorigenesis by regulating DNA replication and cell cycle processes. Additionally, by querying CTD, we have excavated several available compounds that could enhance or inhibit GINS4 expression, implying their significance in the treatment of ESCA, KIRC, LIHC, LUAD, and UCEC.
Conclusion
This detailed in silico study has effectively uncovered the diagnostic and prognostic roles of GINS4 in ESCA, KIRC, LIHC, LUAD, and UCEC by analyzing its expression and correlations of its expression with different parameters. However, prior to clinical implication, we strongly recommend oncology researchers around the globe to further investigate GINS4 roles on a larger scale in ESCA, KIRC, LIHC, LUAD, and UCEC, and to deeply explore the biology of GINS4 in the immune microenvironment of these cancers, which will aid in successful immunotherapy.
Acknowledgements
The authors extended their appreciation to the Researchers Supporting Project number (RSP-2021/374) King Saud University, Riyadh, Saud Arabia.
Disclosure of conflict of interest
None.
Supporting Information
References
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