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Frequent gene mutations and the correlations with clinicopathological features in clear cell renal cell carcinoma: preliminary study based on Chinese population and TCGA database

BMC Urology volume 24, Article number: 170 (2024) Cite this article

Abstract

Background

Large-scale sequencing plays important roles in revealing the genomic map of ccRCC and predicting prognosis and therapeutic response to targeted drugs. However, the relevant clinical data is still sparse in Chinese population.

Methods

Fresh tumor specimens were collected from 66 Chinese ccRCC patients, then the genomic RNAs were subjected to whole transcriptome sequencing (WTS). We comprehensively analyzed the frequently mutated genes from our hospital’s cohort as well as TCGA-KIRC cohort.

Results

VHL gene is the most frequently mutated gene in ccRCC. In our cohort, BAP1 and PTEN are significantly associated with a higher tumor grade and DNM2 is significantly associated with a lower tumor grade. The mutant type (MT) groups of BAP1 or PTEN, BAP1 or SETD2, BAP1 or TP53, BAP1 or MTOR, BAP1 or FAT1 and BAP1 or AR had a significantly correlation with higher tumor grade in our cohort. Moreover, we identified HMCN1 was a hub mutant gene which was closely related to worse prognosis and may enhance anti-tumor immune responses.

Conclusions

In this preliminary research, we comprehensively analyzed the frequently mutated genes in the Chinese population and TCGA database, which may bring new insights to the diagnosis and medical treatment of ccRCC.

Peer Review reports

Introduction

Renal cell carcinoma (RCC) is a very common malignant tumor. In 2023 worldwide statistics the incidence rate of RCC accounts for about 4% of adult malignancy and it is the 6th most common malignant tumor in males and the 9th in females [1]. Approximately, 25% of RCC patients already have local or distant metastatic lesions when diagnosed and more than 20% of RCC patients after surgery still have metastasis and recurrence [2,3,4]. RCC is not sensitive to conventional chemotherapy, and although molecular targeted therapy and immunotherapy have improved the efficacy of treatment for metastatic renal cell carcinoma (mRCC), the treatment varies greatly among individual patients [5,6,7]. High genetic heterogeneity and lack of effective therapeutic targets are the main reasons for the poor treatment outcome of mRCC [8]. Different patients carry tumor cells with different genetic mutation profiles and genetic phenotypes, resulting in different individual sensitivity to treatment. Mutated genes have demonstrated significant value in targeted therapy for RCC patients, but there are still no reliable indicators developed to predict progression risk and drug efficacy. Therefore, it is urgent and challenging to further explore the frequently mutated genes and their functions in RCC for clinical applications.

As is well-known, VHL gene is the most prevalent mutation in ccRCC from The Cancer Genome Atlas (TCGA) online database (https://tcga-data.nci.nih.gov/tcga/). Taking the pathogenesis of ccRCC into consideration, the response to anti-VEGF tyrosine kinase inhibitors should be better when comparing patients with VHL gene mutation to those without theoretically. However, there is still controversy regarding the relationship between the status of the VHL gene and clinical outcomes [9]. So, there may be some other important mutated genes and their associated biological pathways working in the progression of ccRCC. Advances in high-throughput sequencing and reduction of sequencing costs have enabled discovery of somatic mutations from whole transcriptome level in ccRCC, which have been studied in multiple research projects, such as TCGA, Catalogue of Somatic Mutations in Cancer (COSMIC) database. The large-scale sequencing work in western countries have helped to reveal the genome map for ccRCC and identify a number of molecular classification systems that can predict prognosis and therapeutic response to targeted drugs [10, 11]. Bioinformatics analysis of these high-throughput data can identify key genes associated with ccRCC prognosis [12]. However, the relevant clinical data from Chinese population is still sparse and the cost for whole genome-wide detection of somatic mutations is still a high burden for developing countries like China. Guo and Wang et al. studied the profiles of gene alterations in Chinese ccRCC and described the discrepancies between different races [13, 14]. Guo et al. also emphasized the importance of ubiquitin-mediated proteolysis pathway in ccRCC oncogenesis including VHL gene(27%), BAP1 gene(8%), CUL7 gene(3%), BTRC gene(2%) and other genes, not just VHL gene [13].

In this preliminary study, we explored genetic alterations from Peking University Cancer Hospital (PUCH)-ccRCC cohort and identified the frequently mutated genes. Unlike the previous studies [13, 14], we explored whether the frequent gene alterations have relevance to tumor stage and grade within PUCH-ccRCC and TCGA-KIRC cohort, as well as overall or cancer-specific survival in TCGA cohort. Although mutations in certain genes (e.g., VHL, BAP1, and PTEN) have been reported, our study provides new perspectives and data support in analyzing the association of these mutations with clinicopathological features in the Chinese population. To our knowledge, this is the largest scale research focusing on the relationship between mutated genes and the clinicopathological outcomes in Chinese patients with ccRCC.

Materials and methods

RNA isolation and transcriptome sequencing

At our institution, patients diagnosed with ccRCC have been meticulously confirmed through histopathological examination. This includes the observation of characteristic histologic features, the application of immunohistochemical markers that substantiate the diagnosis (CAIX+,PAX8+,CK7-, et al.), and the verification of chromosomal 3p deletion via the Fluorescence in Situ Hybridization (FISH) technique. The grading of ccRCC tumors adheres strictly to the criteria established by the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system. In this research, we collected freshly and surgically removed tumor tissues from PUCH-ccRCC cohort. Total RNA was extracted by Qiagen’s RNeasy kit, and the integrity and purity of RNA samples were ensured by Agilent Bioanalyzer before RNA sequencing was performed. Only RNA samples with 28 S/18S > 1 and RIN > 7 were used for RNA sequencing and library construction. Transcriptomic sequencing was performed at 150 bp paired ends by a certified service provider on the Illumina HiSeq X platform.

Gene mutation analysis on PUCH-ccRCC RNAseq dataset

Quality-controlled raw RNA-seq reads were aligned to the human reference genome compendium hg19. Somatic mutations in expressed genes were detected by GATK variant discovery toolkit and STAR mapping software from the matched data of the 66 tumor samples [15, 16]. To exclude low-quality mutations and germline mutations, we performed several filtering steps: (1) exclusion of low-quality candidate events (i.e., indel mutations in the poly-N region or substitution allele rate < 20%, and mutation events not tagged as PASS by GATK or substitution allele depth < 5); (2) filtering out the mutation events with frequencies > 0.5% observed in the 6500 Exome Project and the 1000 Genome Project; (3) the putative events should be in the exon region and be protein-altering. The remaining putative mutations were inferred as driving mutation by an online website, the Cancer Genome Interpreter (https://www.cancergenomeinterpreter.org/). We have reviewed our data handling processes during the course of the study and have strengthened our criteria for mutation filtering to ensure that only high-quality mutation events are included.

Retrieval and processing of TCGA dataset

Whole-exome sequencing-based somatic mutation data and clinicopathological information including age, gender, overall survival (OS) TNM stage and survival status of ccRCC patients were downloaded from the TCGA-KIRC cohort (https://tcga-data.nci.nih.gov/tcga/). Retrieved data were processed and analyzed in R-software (v4.2.2).

Statistical analysis on gene mutation

In order to detect driver mutations from ccRCC patients, we further analyzed the correlation between frequent mutations with clinical categories and other important variants, separately using single or paired mutated genes. The log-rank test and Kaplan-Meier survival analysis were used to analyze whether statistical differences existed between stratified survival groups in TCGA patients using the R package ‘Survival’. Other statistical tests were used to analyze genomic and clinical data, including Fisher’s exact test, the paired T test, Mann-Whitney-Wilcoxon test and Cox proportional hazards analysis. P < 0.05 was considered statistically significant and the analyses were performed primarily with R.

Identification and validation analysis of shared genes in PUCH and TCGA cohorts

To verify the heterozygosity between the 2 cohorts, we selected two gene sets with the number of mutated genes ≥ 3 in the PUCH-ccRCC cohort (supplementary Table 1) and ≥ 8 in the TCGA-KIRC cohort (supplementary Table 2), and mapped the common mutated genes (CMGs) using Venn diagrams. These cutoffs were chosen based on a balance between statistical power and the relevance of the genes to the disease phenotype. After obtaining CMGs, we used the Gene Set Cancer Analysis (GSCA) online website (http://bioinfo.life.hust.edu.cn/GSCA/) to show the mutation status of CMGs [17]. Subsequently, R package “Survival” was used to identify the CMGs with prognostic value. After that, we used the TCGA-KIRC cohort to validate the relationships between the hub mutant gene expressions and the prognosis for ccRCC. Univariate and multivariate Cox regression analyses were applied to assess the prognostic significance with this hub gene. The regions where these mutation sites in this gene mainly occur, their frequency, and their effects on protein and amino acid length were analyzed, also using the GSCA online website.

Relationship between the hub mutated gene and immune cell infiltration

CIBERSORT was applied to quantify the relative proportions of infiltrating immune cells. Then we used R package “corrplot” and CIBERSORT to calculate the correlation between immune cells. Finally, we used R package “limma” to estimate the differences between gene mutations and immune cell types between this gene’s mutant- and wild- groups.

Statistical analysis

Data evaluation in this study was performed using R language software (version 4.2.2) and results were considered statistically significant when P < 0.05. In order to ensure the accuracy and credibility of the statistical data, we introduced multiple test correction in the differential expression analysis and survival analysis (the significant P-value level is < 0.05), and used batch correction in the analysis of different datasets.

In our study, we utilized a total of 66 participants, with the first group comprising 68% and the second group 32% of the sample—this selection was based on the group exhibiting the most significant variance. The significance level was set at 0.05. G*Power software is a widely used tool for power analysis and is designed to determine the sample size required for a study, given the expected effect size, significance level, and statistical power. It supports a wide range of statistical tests, including t-tests, F-tests, chi-square tests, z-tests, and some exact tests. Through the application of G*Power software (version 3.1.9.2) for our calculations, we achieved a statistical power of 0.7664, which approaches the desired threshold of 0.8. This suggests that the findings obtained from our sample size are robust and credible, while also reflecting a judicious allocation of research resources (supplementary Fig. 1).

Results

Clinicopathological characteristics of the PUCH-ccRCC patients

This cohort consists of 66 ccRCC patients who underwent surgical treatment, including partial nephrectomy (n = 36,54.5%) or radical nephrectomy (n = 30,45.5%) in Peking University Cancer Hospital & Institute, from August 2016 to January 2020. No positive family history was found. No neoadjuvant therapy was applied except for one patient with lung metastasis using 10 months targeted therapy before the operation. The detail clinicopathological characteristics of the 66 patients were showed in Table 1.

Table 1 Basic clinicopathological features of PUCH-ccRCC patients
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Summary of frequently mutated genes and their relationship with VHL gene mutation state

We first evaluated the gene mutation prevalence in PUCH-ccRCC cohort. Among them, 9 genes had a mutation frequency above 10% (Fig. 1A). Our findings suggested that the most commonly mutated gene is VHL, which is consistent with previous findings [13, 14]. Different from the TCGA-KIRC cohort and two previous studies focusing on Chinese ccRCC patients, we found TFEB, NCOR2, FAT1, AR, DNM2, and ERBB3 were also frequently mutated genes in PUCH-ccRCC cohort. Their mutation frequencies in TCGA database are only 0.4%, 2.2%, 4.7%, 0.4%, 0.2% and 2.7% [13, 14]. On the contrary, the mutation frequency of PBRM1, MTOR, KDM5C, PTEN, and TP53, which are all frequent mutated genes in TCGA database as well as two previous studies about Chinese ccRCC patients, are 9.1% (6/66), 6.1% (4/66), 0% (0/66), 4.5% (3/66), and 1.5% (1/66) in our cohort. Their mutation frequencies in TCGA cohort are 37.4%, 7.9%, 6.8%, 5.2%, and 4.6% (Fig. 1B). Among them, the mutation frequencies of PBRM1, KDM5C, and TP53 in PUCH-ccRCC cohort are much lower than those in TCGA-KIRC cohort.

Fig. 1
figure 1

Summary of frequently mutated genes in PUCH-ccRCC and TCGA-KIRC cohort. (A) The gene mutation prevalence in PUCH-ccRCC cohort was evaluated. 9 genes had a mutation frequency above 10%, including VHL (45/66,68.2%), TFEB (12/66,18.2%), NCOR2 (10/66,15.2%), AR (9/66,13.6%), BAP1 (9/66,13.6%), FAT1 (9/66,13.6%), DNM2 (8/66,12.1%), ERBB3 (7/66,10.6%), and SETD2 (7/66,10.6%). (B) The mutation prevalence of top 50 genes in TCGA-KIRC cohort was showed

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In the TCGA database, only PBRM1 mutation was significantly associated with VHL mutation (P = 1.66E-06, Fisher’s exact test) due to their adjacent location on chromosome 3p. Combining our data and previously relevant studies [13, 14], we analyzed the association between VHL gene status and 12 frequently mutated genes. The results showed a significant correlation only between PTEN and VHL gene status (P = 0.029) (Table 2).

Table 2 The association between VHL and other frequently mutated genes in PUCH-ccRCC cohort
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Frequently mutated genes related to pathological outcomes regardless of VHL mutation state in PUCH and TCGA cohort

We further investigated the relationships between the frequently mutated genes and the pathological characteristics (Table 3). In PUCH- ccRCC cohort, BAP1 and PTEN are significantly associated with a higher tumor grade (G3&G4, P = 0.004; P = 0.030). DNM2 is significantly associated with a lower tumor grade (G1&G2, P = 0.046). In the TCGA-KIRC cohort, BAP1 is significantly associated with a higher tumor stage (stage III&IV, P = 5.704E-05). Though BAP1 and PTEN is not significantly associated with a higher tumor grade (P > 0.05), the percentage of G3&G4 is higher in both BAP1 and PTEN mutation groups. The similarity between the above study and the TCGA database indicated that there may be partially similar results of significantly mutated genes between different races.

Table 3 Frequently mutated genes and their association with tumor stage and tumor grade in PUCH-ccRCC cohort.
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We analyzed paired mutated genes (either one component being mutated) and their correlation with the stage and grade in PUCH-ccRCC cohort. There were no significant correlations between the distinct MT groups and the tumor stage (P > 0.05). In Table 4, we described the statistically different paired genes. We found that the MT groups of some paired mutated genes had a significant correlation with the tumor grade (I&II vs. III&IV, P < 0.05) (Table 4). Those MT groups except PTEN or VHL, NCOR2 or DNM2, and TFEB or DNM2, tended to have a higher tumor grade (P < 0.05) (Table 4).

Table 4 Paired mutated genes and their association with tumor stage as well as tumor grade in PUCH-ccRCC cohort (presenting only significant groups)
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Frequently mutated genes related to clinical outcomes in TCGA cohort

Taking advantage of the TCGA database, we analyzed the association between overall survival (OS) and the state of each frequently mutated gene depicted in our study. Only BAP1 and TP53 were significantly associated with an inferior OS (P = 0.0002; P = 0.0082). We then analyzed the association between the aforementioned paired mutated genes and OS in TCGA-KIRC cohort. In Table 5, we described the statistically different paired genes. As a result, the MT groups of some paired mutated genes had a significantly inferior OS in TCGA cohort (P < 0.05) (Table 5). The median OS in each group of mutated or wild type was listed in Table 5 (P-values are based on the log-rank test in Kaplan-Meier survival analysis).

Table 5 Paired mutated genes and their association with overall survival in TCGA database (presenting only significant groups)
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HMCN1 mutation was correlated with prognosis of ccRCC

We used Venn diagram to compare the difference in the number of gene mutations between PUCH-ccRCC and TCGA-KIRC cohorts. The result showed that 12 common mutated genes-CMGs shared by the 2 above cohorts (Fig. 2A). Figure 2B summarized the SNV classes of these CMGs in ccRCC. The expression levels of the CMGs were showed in Fig. 2C. Then we validated the correlation between the CMGs and prognosis in the TCGA-KIRC cohort. The results showed that compared with wild-type patients, the OS rates of mutant patients with BAP1 (P = 0.002), HMCN1 (P = 0.025), and KMT2C (P = 0.042) were significantly worse (Fig. 2D and E, and 2F). We then assessed clinical characteristics and the prognostic values of the above 3 genes. Univariate Cox regression analysis confirmed that age, grade, stage, tumor mutation burden (TMB) and HMCN1 were significantly associated with ccRCC survival (Fig. 2G). Multivariate Cox regression analysis showed HMCN1 was an independent prognostic factor in ccRCC (P = 0.005) (Fig. 2H). In the TCGA-KIRC cohort, the regions where the mutation sites in HMCN1 mainly occur, its frequency and effects on protein and amino acid length were showed in Fig. 2I.

Fig. 2
figure 2

Identification and validation analysis of CMGs in PUCH-ccRCC and TCGA-KIRC cohort. (A) Venn diagram was used to compare the difference in the number of gene mutations between PUCH-ccRCC and TCGA-KIRC cohorts. The result showed that 12 CMGs (VHL, SETD2, SPEN, FAT1, KMT2C, STAG2, BRCA2, KMT2D, BAP1, FAT4, HMCN1, ATM) shared by the 2 above cohorts. (B) The SNV classes of these CMGs were summarized in ccRCC. (C) The expression levels of the common mutated genes (CMGs) in the two cohorts. (D-F) The correlation between the CMGs and prognosis were validated in TCGA-KIRC cohort. The results showed that patients with mutant type of BAP1(p = 0.002), HMCN1(p = 0.025) and KMT2C(p = 0.042) had a significantly worse OS rate compared to patients with wild type. (G) Univariate Cox regression analysis showed that age, grade, stage, tumor mutation burden (TMB) and HMCN1 were significantly associated with ccRCC survival: age (HR = 1.938, CI:1.296–2.898, p = 0.001), grade (HR = 2.462, CI:1.560–3.886, p < 0.001), stage (HR = 3.814, CI:2.494–5.831, p < 0.001), TMB (HR = 1.185, CI:1.079–1.301, p < 0.001), HMCN1 (HR = 2.224, CI:1.077–4.590, p < 0.031). (H) Multivariate Cox regression analysis confirmed that HMCN1 was an independent prognostic factor for ccRCC (HR = 2.859, CI:1.373–5.952, P = 0.005). (I) In the TCGA-KIRC cohort, the regions where the mutation sites in HMCN1 mainly occur, its frequency and effects on protein and amino acid length were showed. (J) The correlation between immune cells in ccRCC patients. (K) The content of immune cells between the HMCN1 mutant- and wild- group, and the results showed significant differences in CD4 naive T cells (p = 0.002), follicular helper T cells (p = 0.027), activated NK cells (p = 0.024), and activated Dendritic cells (p = 0.016)

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Relationship between HMCN1 mutation type and immune cell infiltration

The immune microenvironment plays a non-negligible role in the process of tumor development. The correlation between immune cells in ccRCC patients may provide insight into the immune microenvironment (Fig. 2J). We further analyzed the immune cell content of between the HMCN1 mutant- and wild- group, and the results found significant differences in CD4 naive T cells (P = 0.002), follicular helper T cells (P = 0.027), activated NK cells (P = 0.024), and activated Dendritic cells (P = 0.016) (Fig. 2K), suggesting that mutant type of HMCN1 may enhance anti-tumor immune responses.

Discussion

As is well known, ccRCC is mainly caused by the mutation of VHL gene located on chromosome 3p25 [18]. This tumor suppressor gene plays a critical role in hypoxia response and participates in the stimulation of ccRCC oncogenesis and angiogenesis [19]. However, large scale genomic profiling studies have found large genetic heterogeneity in ccRCC. Racial diversity may be one of the main causes leading to the difference in genetic profiles. As a result, understanding the genetic landscape and discovering the discrepancies between different races are under active demand. In the two previous studies of ccRCC from Chinese patients, only the profiles of somatic gene mutations were described and their relationship with the clinical and pathological outcomes was not mentioned [13, 14]. In this research, we performed an analysis of somatic gene mutations and taking together with the previous studies in Chinese ccRCC patients, we further demonstrated a possible distinct profile of mutated genes between Chinese and Western population, which might probably indicate different biological features and diverse prognosis or treatment response in different populations. Although mutations in certain genes (e.g., VHL, BAP1, and PTEN) have been reported, our study provides new perspectives and data support in analyzing the association of these mutations with clinicopathological features in the Chinese population.

Genetic heterogeneity exists among different ethnic groups, a phenomenon that reflects a combination of biological and environmental factors. Natural selection, genetic drift, and differences in cultural practices and lifestyles are key evolutionary factors that shape these genetic differences [20,21,22]. For example, findings suggest that genome-wide variation in cytosine modifications between European and African populations can have complex effects on traits [23]. Similarly, breast cancer incidence and mortality vary among different populations. African-American, Hispanic, Asian and Native American women have lower incidence but higher mortality compared with non-Hispanic white women [24]. In future studies, in-depth exploration of these mechanisms will not only enhance our understanding of human genetic diversity, but also be important for personalized medicine for different ethnic groups. We also propose directions for future research, including conducting studies in larger ethnic groups to validate our findings and utilizing multi-omics approaches to explore the molecular mechanisms behind these genetic heterogeneities.

Since 2006, multiple antiangiogenetic drugs targeting VHL/HIF-1/VEGF pathway and MTOR inhibitors targeting PI3K/AKT/MTOR pathway have been approved for using in metastatic ccRCC sequentially [19]. However, the prognosis or the treatment response can only be predicted roughly depending on certain clinical and pathological characteristics [25]. Astonishingly, no consistent relationship has been found between VHL status and clinical outcome in metastatic ccRCC [18]. Kim et al. had a pooled meta-analysis from ten studies to evaluate the odds ratios for pathological features and hazard ratios for OS. There was no significant relationship between VHL alterations and nuclear grade, disease stage and OS [26]. So, we presume that other simultaneous or metachronous genetic mutations other than VHL gene may be present to have synergy or independent deleterious effects. Utilizing the ccRCC data of PUCH and TCGA cohort, we further analyzed the relationships between the frequently mutated genes and the clinicopathological outcomes in this study. In PUCH-ccRCC cohort, we found that BAP1 and PTEN had a significantly correlation with higher tumor grade, DNM2 had a significantly correlation with lower tumor grade. In TCGA-KIRC cohort, BAP1 and TP53 had a significantly correlation with a worse OS of ccRCC. These results indicated that the mutation of BAP1, PTEN, and TP53 might have an enhanced effect on ccRCC progression, while DNM2 might have a negative regulation on ccRCC tumor cell activity.

Previous studies have indicated that the loss of androgen receptor (AR) expression is associated with aggressive disease, whereas patients with AR-positive tumors exhibit a favorable prognosis [27]. The impact of AR on RCC progression may operate through a series of miRNAs independent of VHL status [28,29,30]. TFEB, a member of the microphthalmia transcription factor (MiT) family, is associated with RCC that often falls under the category of MiT family translocation RCC, which is less common and less aggressive compared to TFE3-fusion associated RCC. However, TFEB-rearranged RCC is associated with a more aggressive clinical course, and vascular endothelial growth factor A (VEGFA) may be linked to the aggressiveness in TFEB-rearranged RCC [31]. Despite this, TFEB proteins are involved in numerous complex molecular pathways in carcinogenesis [32]. The dynamin 2 (DNM2) gene has been found to be associated with the development and inferior outcome of leukemia [33]. We have initially explored the relationship between the aforementioned mutated genes and the clinicopathological features in ccRCC, but more in-depth specific studies are still required to elucidate their molecular biological functions.

Among these genes, BAP1 is the most frequently mutated and is located at chromosome position 3p21, adjacent to the VHL gene region. BAP1 encodes the histone deubiquitinating enzyme BRCA1-associated protein 1, which acts as a tumor suppressor [34]. Patients with BAP1-mutant ccRCC have been reported to have a poor prognosis [9, 35, 36]. BAP1 exerts its tumor suppressive activity based on its nuclear localization and deubiquitination activity, and BAP1-deficient cancer cells have been reported to be more sensitive to Olaparib and more susceptible to radiation [37, 38]. Given the high mutation rate of BAP1, BAP1-associated ccRCC in the Chinese population should garner more attention. A Phase II clinical trial (ID: NCT03207347) is currently underway to evaluate the clinical response to PARP inhibitors in patients with BAP1-mutated cancers, including RCC [39]. Based on the aforementioned results, we can infer that BAP1, PTEN, and TP53 may play significant roles in the progression of ccRCC. Patients with mutations in BAP1 or PTEN, BAP1 or TP53, may be categorized as a group of high-risk ccRCC, requiring more aggressive postoperative treatment to reduce the recurrence rate.

HMCN protein family has two close analogues (HMCN1 and HMCN2). HMCN1, produced mainly by stromal cells and usually associated with calcium binding, is a cell polarity-associated protein that may play a biological role at epithelial cell junctions [40]. HMCN1 is involved in many processes such as anchoring of mechanosensory neurons in the epidermis, stabilization of genital syncytia, and organization of hemispheric forms in the epidermis [41]. Previous studies have shown that HMCN1 can promote ovarian cancer invasion by regulating cancer-associated fibroblasts [42]. Kikutake found that HMCN1 mutant allele was strongly associated with a poorer prognosis in breast cancer patients [43]. Gong found that HMCN1 mutation might have an important clinical significance in ccRCC by using information from public databases [44]. In the present study, we verified the above points with our own sequencing data and we identified that patients with mutant type of HMCN1 had a significantly poor prognosis compared to wild-type patients in ccRCC.

To deepen our understanding of the mechanisms by which HMCN1 mutations influence ccRCC prognosis, we propose a detailed exploration of the molecular pathways affected by HMCN1. This could involve investigating the interaction between HMCN1 and other key proteins involved in cell adhesion, migration, and the tumor microenvironment. Additionally, we will examine the impact of HMCN1 mutations on the signaling pathways that regulate cell survival and apoptosis, which are critical for cancer progression.

In recent years, the dawn of the immunotherapy era has brought with it an expanding comprehension of the tumor microenvironment within RCC. Research has revealed that a prevalent, yet seemingly dysfunctional phenotype of “resident” NK cells is notably present in patients with metastatic ccRCC. This presence correlates with diminished survival rates, particularly in those with advanced stages of the disease, even amongst those undergoing treatment with immune checkpoint inhibitors (ICIs). The potential restoration of NK cell functionality emerges as a promising therapeutic avenue for individuals battling ccRCC [45]. Our current study highlights the immune cell infiltration results, which exhibit marked disparities in follicular helper T cells and CD4 naive T cells. This suggests that the mutant HMCN1 variant may amplify anti-tumor immune responses. Our discoveries offer fresh perspectives for prognostic assessment and precision therapy in ccRCC patients. Nonetheless, comprehensive research is imperative to unravel the intricate mechanisms by which HMCN1 influences ccRCC.

Our preliminary study still has some limitations. Firstly, we recognize that the original sample size was small, which limits the statistical validity and generalizability of the study. To address this issue, we are actively expanding the sample size and plan to include more patient data in future studies to improve the reliability and representativeness of the study. We plan to collect a larger sample of ccRCC patients by collaborating with other medical centers in the country. To ensure the representativeness of the sample, we will use random sampling and ensure that the sample covers different ages, genders and geographic locations. In addition, we will also ensure the diversity and breadth of the sample through multi-center collaboration. Secondly, the limitation of our study is that exploration was performed at the transcriptional level, the state of genome, translation and the alteration of the relevant pathway, are all not clear. In future studies, we plan to use proteomics techniques to analyze how these mutations affect protein expression and function. We also plan to use cellular and animal models to validate the effects of these mutations on relevant signaling pathways and their role in tumor development and drug response.

Data availability

The PUCH-ccRCC cohort dataset for this study is available at Gene Expression Omnibus (GEO) with accession number: GSE126964 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126964).

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Acknowledgements

We sincerely thank all the patients involved in this research for their collaboration and support.

Funding

This study was supported by the Natural Science Foundation of Beijing (Grant No. 7224322 and No. 7212010), Capital’s Funds for Health Improvement and Research (Grant No. 2020-2-1024), Science and Technology Development Fund of Beijing Anzhen Hospital (No. AZYZR202304) and National Natural Science Foundation of China (No. 81372738).

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Author notes

  1. Qiang Zhao, Baoan Hong and Xuezhou Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Urology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China

    Qiang Zhao

  2. Department of Urology, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, 100029, P. R. China

    Baoan Hong, Xuezhou Zhang & Ning Zhang

  3. Data Science and Bioinformatics, Crown Bioscience Inc., Suzhou, China

    Jia Xue & Sheng Guo

Authors
  1. Qiang Zhao
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  2. Baoan Hong
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  5. Sheng Guo
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  6. Ning Zhang
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Contributions

Conceptualization, Baoan Hong and Ning Zhang; Data curation, Qiang Zhao and Baoan Hong; Funding acquisition, Ning Zhang and Baoan Hong; Project administration, Ning Zhang; Resources, Baoan Hong, Jia Xue, Sheng Guo, Ning Zhang; Software, Xuezhou Zhang and Sheng Guo; Supervision, Baoan Hong and Ning Zhang; Validation, Xuezhou Zhang and Jia Xue; Visualization, Jia Xue; Writing – original draft, Qiang Zhao and Xuezhou Zhang; Writing – review & editing, Baoan Hong and Ning Zhang. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ning Zhang.

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Ethics approval and consent to participate

The study was approved by the Medical Ethics Committee of Peking University Cancer Hospital (approval ID: 2020KT21) and conducted following Good Clinical Practice and the Declaration of Helsinki. Informed consent was obtained from the patients and their families for this study.

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Not applicable.

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The authors declare no competing interests.

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Zhao, Q., Hong, B., Zhang, X. et al. Frequent gene mutations and the correlations with clinicopathological features in clear cell renal cell carcinoma: preliminary study based on Chinese population and TCGA database. BMC Urol 24, 170 (2024). https://doi.org/10.1186/s12894-024-01559-9

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Keywords

BMC Urology

ISSN: 1471-2490

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