Comprehensive Analysis of HMCN1 Somatic Mutation in Clear Cell Renal Cell Carcinoma

Background: Renal cell carcinoma (RCC) is a common malignancy of the genitourinary system and clear cell renal cell carcinoma (ccRCC) is the most representative subtype. The morbidity and mortality of ccRCC have gradually risen during recent years; however, the pathogenesis and potential biomarkers remain unclear. The purpose of our study was to find out prognostic genes correlated with somatic mutation and the underlying mechanisms of HMCN1 mutation in ccRCC. Methods: Somatic mutation data of two ccRCC cohorts were acquired from TCGA and cBioPortal. Genes frequently mutated in both datasets were extracted, from which tumor mutation burden and survival analysis revealed three prognostic genes. Further comprehensive analysis of HMCN1 mutation was carried out to identify differentially expressed genes and apply functional annotations. The correlation of HMCN1 mutation and tumor immunity was also evaluated. Results: HMCN1, SYNE1, and BAP1 mutations were associated with both tumor mutation burden and clinical prognosis in ccRCC. Gene enrichment analysis suggested the effects of HMCN1 mutation on biological processes and pathways linked to energy metabolism. HMCN1 mutation was also correlated with anti-tumor immunity. There were several limitations in the sample size and cohort availability of the present computational study. Conclusions: The present results inferred that HMCN1 mutation might have an important clinical significance for ccRCC patients by regulating metabolism and the immune microenvironment.


Introduction
Renal cell carcinoma (RCC) is a common malignant solid tumor of the urinary system with a morbidity of approximately 400,000 cases worldwide [1,2]. RCC are heterogeneous tumors with multiple histological subtypes [3]. The most representative one is clear cell renal cell carcinoma (ccRCC), which makes up approximately 70% of all cancers of the kidney [4].
Since there are no obvious symptoms and reliable diagnostic biomarkers at the beginning, 25-30% of RCC patients have already developed spread and metastasis by the time of diagnosis [5]. In the early stages, radical nephrectomy is the most effective treatment [6], but recurrence or metastasis occurs in 30% of patients after surgery [7,8]. Because of its intrinsic resistance, RCC is insensitive to both chemotherapy and radiotherapy, which makes the administration of advanced RCC a challenge [2,9]. Immune checkpoint inhibitor therapy has been widely applied in many types of tumors in the past few years, including ccRCC [10], but the therapeutic effectiveness is still controversial [2]. Although there have been significant improvements in the diagnosis and management of ccRCC, the underlying pathological mechanisms and prognosis-related biomarkers remain to be explored.

Data Acquisition
Single nucleotide variation (SNV) data of 537 ccRCC patients (mainly from western countries) was downloaded from TCGA [18]. We also downloaded the corresponding clinical information and transcriptome profile of TCGA cohort. SNV data of 106 Japanese ccRCC patients from Tokyo were extracted from UTokyo cohort [19], which was acquired from cBioPortal [20].

Genome-Wide Mutation Profiling
We analyzed and visualized the somatic mutation characteristics in each sample with Perl software and R package "GenVisR" [21]. The frequency of various variant classifications and distribution of different types of variant genes were visualized by the waterfall plot. We intersected the top 30 genes ranked by mutation rate in both groups.

TMB Calculation and Survival Analysis
TMB was defined as the mutation rate per million bases [22], which was assessed by dividing non-synonymous mutation numbers by the exon length. Non-synonymous mutations contain all missense, insertion/deletion, and frameshift variants [23,24]. In present study, TBM in each TCGA sample was calculated by Perl. The correlation of TMB score and gene mutation status was analyzed by R package "ggpubr". Then, Kaplan-Meier survival analysis and log-rank test was performed for evaluation with R packages "survminer" and "survival". We also built a Cox proportional hazards model consisting of all clinical pathological characteristics via univariate and multivariate Cox regression.

Differentially Expressed Genes (DEGs) Analysis
We employed the DEGs analysis to identify DEGs between HMCN1-mutant and wildtype samples with a cutoff of |Log FC| > 1 and FDR < 0.05. The resulting volcano map and heatmap were plotted by R package "edge R" and "pheatmap" [25].

Protein-Protein Interaction (PPI) and Submodule Analysis
To construct a PPI network, we used the online tool STRING [29]. The network was screened with combined score more than 0.4 and visualized with Cytoscape 3.9.1 [30]. Submodule analysis was carried out by MCODE [31] to identify hub modules that may perform independent functions. We selected the most highly connected modules with a degree cutoff = 2, node score cutoff = 0.2, k-core = 2, and max depth = 100. Functional annotations were performed subsequently for screened hub subnetworks.

Estimation of Immune Cell Infiltrating
We evaluated the infiltrating levels of 22 types immune-infiltrating cells according to CIBERSORT [32]. After calculation, the composition of different immune cells was demonstrated. Then R package "limma" was used to perform Wilcoxon rank-sum test in patients with different HMCN1 mutation statuses [33]. The result was visualized in the violin plot by R package "vioplot" [34]. We also compared the expression of immune checkpoint genes between HMCN1-mutant and wild type samples by R package "limma".

Statistical Analysis
R software (version 4.1.2) was applied for all statistical analyses. The correlation of gene mutation and TMB score was investigated by Mann-Whitney U test. Survival curves were generated by Kaplan-Meier survival analysis, and log-rank test was applied for evaluation. Univariate and multivariate Cox regression models were constructed for each clinical risk factor. A two-tailed p value < 0.05 was considered statistically significant in all comparisons. Figure 1 illustrates the workflow chart of our research. We first downloaded the required data for analysis from TCGA and cBioPortal and evaluated the variants of each sample in two cohorts. Detailed mutation information of the top 30 mutated genes is illustrated in Figure 2A,B, and different mutation types are distinguished by different color annotations. The top 10 mutated genes in TCGA cohort were VHL, PBRM1, TTN, SETD2, BAP1, MTOR, MUC16, KDM5C, HMCN1, and DNAH9. The top 30 mutated genes were figured out in Japanese patients from the UTokyo cohort as well. The most frequently mutated gene was VHL, followed by PBRM1, TTN, MUC16, SETD2, CSMD3, BAP1, AHNAK2, TET2, and MUC4. The Venn diagram in Figure 3A indicates that there are 12 genes carrying a relatively high mutation rate in both two cohorts.

Gene Mutations Related to TMB and Prognosis
In order to sort out the hub genes that probably serve a critical role in ccRCC, we first calculated the TMB values in all patients. Combining the mutation data with the TMB expression profile, we revealed that the TMB values of ccRCC patients were significantly associated with several gene mutations, including VHL, PBRM1, TTN, SETD2, BAP1, MTOR, HMCN1, CSMD3, and SYNE1 ( Figure 3B).
Then, we separated the patients in TCGA cohort into wild and mutant types depending on the gene mutation status and survival analysis was performed in combination with patients' survival data. Kaplan-Meier survival curves and log-rank tests were conducted to figure out prognosis-related mutations. Our results demonstrated that among the 12 mutated genes, only 3 gene mutations were associated with the prognosis of ccRCC patients significantly, including HMCN1, BAP1, and SYNE1, which were also TMB-related genes ( Figure 4A-C). The biological role and molecular mechanisms of BAP1 and SYNE1 Genes 2022, 13, 1282 4 of 16 mutations in ccRCC have been previously reported [35][36][37]. In this work, we further carried out an integrated analysis of HMCN1 mutation.
As was illustrated in Figure 4A, HMCN1 mutation was significantly associated with poorer clinical outcome. To determine if HMCN1 mutation was an independent predictive biomarker, further univariate and multivariate Cox regressions were employed. After the screening of a multivariate model, HMCN1 mutation remained significantly associated with overall survival under correction for clinical characteristics and TMB score. In addition, age, stage, and TMB score were also considered as important prognosis-related biomarkers. Older age, a more advanced stage, and a higher TMB value were significantly associated with a poorer clinical prognosis ( Figure 5A,B).

Identification of DEGs
We selected DEGs to further explore potential pathways by which HMCN1 mutation impacts ccRCC development. The differentially expressed mRNAs in HMCN1-mutant and wild-type samples were calculated with a criteria of log fold change >1.0 or <−1.0 and FDR < 0.05. Overall, we identified 134 DEGs, with 88 being upregulated in HMCN1-mutant samples and 46 being downregulated. The results were illustrated by a volcano map and heatmap in Figure 6A,B. Moreover, the lollipop chart downloaded from cBioPortal demonstrated that the mutation types of HMCN1 included splice, in-frame, and missense mutations across the entire gene ( Figure 6C).

Functional Annotations of DEGs
Gene enrichment analysis was performed after identifying DGEs. The top 20 GO and KEGG terms were illustrated in Figure 7A,B. GO analysis indicated that the main biological processes of genes differentially expressed in patients with HMCN1 mutation were enriched in the aerobic electron transport chain, ATP synthesis coupled electron transport and

Gene mutations Related to TMB and Prognosis
In order to sort out the hub genes that probably serve a critical role in ccRCC, we f calculated the TMB values in all patients. Combining the mutation data with the T expression profile, we revealed that the TMB values of ccRCC patients were significan associated with several gene mutations, including VHL, PBRM1, TTN, SETD2, BA MTOR, HMCN1, CSMD3, and SYNE1 ( Figure 3B).
Then, we separated the patients in TCGA cohort into wild and mutant types depe ing on the gene mutation status and survival analysis was performed in combination w patients' survival data. Kaplan-Meier survival curves and log-rank tests were conduc to figure out prognosis-related mutations. Our results demonstrated that among the mutated genes, only 3 gene mutations were associated with the prognosis of ccRCC tients significantly, including HMCN1, BAP1, and SYNE1, which were also TMB-rela genes ( Figure 4A-C). The biological role and molecular mechanisms of BAP1 and SYN mutations in ccRCC have been previously reported [35][36][37]. In this work, we further ried out an integrated analysis of HMCN1 mutation.

Gene mutations Related to TMB and Prognosis
In order to sort out the hub genes that probably serve a critical role in ccRCC, we firs calculated the TMB values in all patients. Combining the mutation data with the TMB expression profile, we revealed that the TMB values of ccRCC patients were significantly associated with several gene mutations, including VHL, PBRM1, TTN, SETD2, BAP1 MTOR, HMCN1, CSMD3, and SYNE1 ( Figure 3B).
Then, we separated the patients in TCGA cohort into wild and mutant types depend ing on the gene mutation status and survival analysis was performed in combination with patients' survival data. Kaplan-Meier survival curves and log-rank tests were conducted to figure out prognosis-related mutations. Our results demonstrated that among the 1 mutated genes, only 3 gene mutations were associated with the prognosis of ccRCC pa tients significantly, including HMCN1, BAP1, and SYNE1, which were also TMB-related genes ( Figure 4A-C). The biological role and molecular mechanisms of BAP1 and SYNE mutations in ccRCC have been previously reported [35][36][37]. In this work, we further car ried out an integrated analysis of HMCN1 mutation. As was illustrated in Figure 4A, HMCN1 mutation was significantly associated with poorer clinical outcome. To determine if HMCN1 mutation was an independent predictiv biomarker, further univariate and multivariate Cox regressions were employed. After th GSEA further confirmed that HMCN1 mutation may lead to alterations in key pathways related to energy metabolism. Figure 8A,C exhibited the hallmarks and pathways that considerably enriched in HMCN1 mutant samples, containing adipogenesis, oxidative phosphorylation, fatty acid metabolism, the TCA cycle, fructose and mannose metabolism, and porphyrin and chlorophyll metabolism. Pathways including apical junction, apical surface, mitotic spindle, adherens junction, focal junction, gap junction, and regulation of the actin cytoskeleton were significantly enriched in wild-type samples. The majority of these were closely associated with cell polarity, the cytoskeleton, and cell junctions ( Figure 8B).

PPI Network Establishment, Hub Genes, and Submodules Screening
We constructed a PPI network with 53 nodes and 442 edges using online tool STRING ( Figure 9A) and visualized it in Cytoscape. The key genes with the top 30 node degrees in the network were listed in the bar chart ( Figure 9B). Then, two significant modules containing 15 and 7 genes, respectively, were distinguished by MCODE ( Figure 9C,D). The following GO and KEGG functional annotations revealed that the main functional areas of genes in submodule 1 were significantly enriched in oxidative phosphorylation, the aerobic electron transport chain, and ATP synthesis coupled electron transport. Genes in submodule 2 exhibited significant enrichment in the negative regulation of hydrolase activity, sterol import, and cholesterol import. The main pathways of genes in submodule 1 were enriched in oxidative phosphorylation, non-alcoholic fatty liver disease, and Parkinson's disease. The genes in submodule 2 were enriched in cholesterol metabolism, the PPAR signaling pathway, and complement and coagulation cascades (Tables 1 and 2).
Genes 2022, 13, x screening of a multivariate model, HMCN1 mutation remained significantly ass with overall survival under correction for clinical characteristics and TMB score. I tion, age, stage, and TMB score were also considered as important prognosis-rel omarkers. Older age, a more advanced stage, and a higher TMB value were signi associated with a poorer clinical prognosis ( Figure 5A,B).

Identification of DEGs
We selected DEGs to further explore potential pathways by which HMCN1 m impacts ccRCC development. The differentially expressed mRNAs in HMCN1and wild-type samples were calculated with a criteria of log fold change >1.0 or <− FDR < 0.05. Overall, we identified 134 DEGs, with 88 being upregulated in HMCN

Functional Annotations of DEGs
Gene enrichment analysis was performed after identifying DGEs. The top 20 GO and KEGG terms were illustrated in Figure 7A,B. GO analysis indicated that the main biological processes of genes differentially expressed in patients with HMCN1 mutation were enriched in the aerobic electron transport chain, ATP synthesis coupled electron transport and mitochondrial ATP synthesis coupled electron transport. The main pathways enriched were oxidative phosphorylation, non−alcoholic fatty liver disease, and diabetic cardiomyopathy. GSEA further confirmed that HMCN1 mutation may lead to alterations in key pathways related to energy metabolism. Figure 8A,C exhibited the hallmarks and pathways that considerably enriched in HMCN1 mutant samples, containing adipogenesis, oxidative phosphorylation, fatty acid metabolism, the TCA cycle, fructose and mannose metab-

PPI Network Establishment, Hub Genes, and Submodules Screening
We constructed a PPI network with 53 nodes and 442 edges using online tool STRING ( Figure 9A) and visualized it in Cytoscape. The key genes with the top 30 node degrees in the network were listed in the bar chart ( Figure 9B). Then, two significant modules containing 15 and 7 genes, respectively, were distinguished by MCODE ( Figure 9C,D). The following GO and KEGG functional annotations revealed that the main functional areas of genes in submodule 1 were significantly enriched in oxidative phosphorylation, the aerobic electron transport chain, and ATP synthesis coupled electron transport. Genes in submodule 2 exhibited significant enrichment in the negative regulation of hydrolase activity, sterol import, and cholesterol import. The main pathways of genes in submodule 1 were enriched in oxidative phosphorylation, non-alcoholic fatty liver disease, and Parkinson's disease. The genes in submodule 2 were enriched in cholesterol metabolism, the PPAR signaling pathway, and complement and coagulation cascades (Tables 1 and 2).

HMCN1 Mutation-Related Tumor Immune Microenvironment
The composition of 22 subsets of immune-infiltrating cells calculated via CIBERSORT was illustrated in the bar graph of Figure 10A. The relationship of different immune cell components was also demonstrated ( Figure 10B). Then, we investigated the differences of immune cell distribution between different mutation status. The violin plot in Figure 10C presents that the abundance of T cells CD4 naïve and T cells follicular helper was significantly different in HMCN1-mutant samples. We also compared the expression of immune checkpoint genes between HMCN1-mutant and wild type samples, and revealed that in HMCN1-mutant samples, the expression of IDO1 was significantly upregulated ( Figure 10D). immune cell distribution between different mutation status. The violin plot in Figure 10C presents that the abundance of T cells CD4 naïve and T cells follicular helper was significantly different in HMCN1-mutant samples. We also compared the expression of immune checkpoint genes between HMCN1-mutant and wild type samples, and revealed that in HMCN1-mutant samples, the expression of IDO1 was significantly upregulated ( Figure  10D).

Discussion
In the current study, we first reviewed the mutation profile of 537 American and 106 Asian ccRCC patients and identified three gene mutations that were related to both TMB and clinical outcome. Among them, the correlation of HMCN1 mutation and ccRCC has not been reported yet. Further exploration revealed that in samples with HMCN1 mutation, pathways related to metabolism were significantly enriched. Moreover, we observed

Discussion
In the current study, we first reviewed the mutation profile of 537 American and 106 Asian ccRCC patients and identified three gene mutations that were related to both TMB and clinical outcome. Among them, the correlation of HMCN1 mutation and ccRCC has not been reported yet. Further exploration revealed that in samples with HMCN1 mutation, pathways related to metabolism were significantly enriched. Moreover, we observed a different infiltration level in naïve CD4 T and follicular helper T cells in the HMCN1-mutant group. The mutant samples also showed an increase in IDO1 expression.
HMCN executes its biological functions as an extracellular matrix protein. The HMCN family has two orthologs (HMCN1 and 2). HMCN1 is predominantly generated by stromal cells [38]. The function of HMCN1 has not been well explored yet. As a cell polarity-related gene commonly associated with calcium binding, the extracellular matrix component HMCN1 is found at the dermal-epidermal and tendon junctions and may be involved in the structural organization of epithelial cell junctions [38]. HMCN1 has been confirmed to be related to AMD. Its variant Gln5345Arg has been discovered in a large AMD family in America [16], and in a small subgroup of AMD patients, it may contribute to disease susceptibility [39].
Mutations and the altered expression of HMCN1 have been proved to be involved in malignant tumor development. HMCN1 regulates cancer-associated fibroblasts (CAFs) to reinforce the aggressiveness of ovarian cancer. CAFs in high-grade plasmacytoid carcinoma and clear cell carcinoma tissues show an upregulation of HMCN1, and tumor cells tend to be less invasive after silencing HMCN1 expression in fibroblasts [40]. In hepatocellular carcinoma, HMCN1 protein was also found to be overexpressed in tumors by proteomic analyses [41].
HMCN1 mutation has been found in many cancers such as gastric, colorectal [42], prostate [43], triple-negative breast cancer [44], and small-cell gallbladder neuroendocrine carcinoma [45]. HMCN1 mutation is associated with patients' pathological characteristics and clinical prognoses. A study of molecular profiles and metastatic markers in Chinese gastric cancer patients indicated that samples carrying HMCN1 mutation are associated with peritoneal metastasis [46]. The allelic mutation frequency of HMCN1 is also significantly related to the prognosis of breast cancer [17]. As is known to all, drug resistance can be attributed to ECM by interfering with drug permeation into cancer tissues and inducing apoptosis resistance. Being an important component of ECM, HMCN1 has a significantly lower expression in multi-drug resistant ovarian cancer cells [47].
In regards to kidney disease, HMCN1 variants play a role in renal pathophysiology [48] and are considered as a potential gene causing diabetic nephropathy in Mexican Americans [49]. In addition, patients with proteinuric nephropathy have higher levels of HMCN1 expression in their kidneys. In vitro and in vivo models reveal that HMCN1 contributes to the remodeling of the podocyte cytoskeleton and the increased expression of HMCN1 in podocytes can be stimulated in response to hyperglycemia [50], which is an important risk factor of RCC [51,52]. In fact, consistent with ovarian and liver cancers discussed above, we did observe a significant overexpression of HMCN1 in ccRCC samples in TCGA cohort. However, the correlation between hyperglycemia, the expression of HMCN1, and the development of ccRCC is not yet clear.
Since HMCN1 mutation plays an essential role in cancer development, it is necessary to elucidate the underlying mechanisms. Our GSEA results showed that HMCN1 mutation was mainly associated with cellular metabolism-related pathways including glucose and lipid metabolism, which are extremely necessary for tumorigenesis and cancer progression [53].
Abnormal metabolism is an important hallmark of cancer. Tumors can gain growth advantages through metabolic reprogramming processes, such as aerobic glycolysis and increased lipid synthesis [54]. Previous studies have shown that the reprogramming of glucose and lipid metabolism occurs frequently in ccRCC [55]. Although conventional research tends to assume that tumor cells primarily undergo glycolysis without oxidative metabolism, recent studies have found that certain types of tumor cells consume more oxygen and have higher levels of oxidative phosphorylation than normal cells. The application of oxidative phosphorylation inhibitors may target at the procedure of tumor cell metabolism and thus exert anti-tumor efficacy [56]. The increase of oxidative phosphorylation and mitochondrial membrane fusion-mediated NADH/NAD+ metabolism can promote the immortalization of neural stem cell tumors [57]. Moreover, numerous pieces of research have shown that the electron transport chain in mitochondria is required for cancer development [58], which is consistent with the results of our GO functional annotation. There have also been studies demonstrating that there may be a crosstalk between energy metabolism and ECM remodeling, further supporting our assumption that HMCN1 mutation might contribute to cellular metabolic reprogramming [59].
The present study found that key pathways altered in HMCN1-mutant ccRCC samples were oxidative phosphorylation and glucose and lipid metabolism, suggesting that drugs targeted at lipid and glucose metabolism and oxidative phosphorylation might contribute to developing new therapeutic strategies for ccRCC with HMCN1 mutation.
In terms of immune cell infiltration conditions, naive CD4T and follicular helper T (Tfh) cells show significantly different abundance in HMCN1 mutant samples, indicating that HMCN1 mutation may enhance anti-tumor immune responses. Naïve CD4T cells differentiate towards Tfh cells in response to transcription factor B-cell lymphoma 6 [60,61]. Tfh cells belong to a special subgroup of CD4T cells, which are involved in the regulation of protective antibody responses to pathogens. The capabilities of mature Tfh cells are to assist B cells in promoting antibody affinity maturation, class switch reorganization, and memory cells production [62,63]. Besides, Tfh cells were most relevant to CD8T cells and exhibited the highest negative association with M2 macrophages, further confirming the hypothesis that an altered tumor immune microenvironment induced by HMCN1 mutation may be involved in enhancing anti-tumor immunity.
Currently, an immune checkpoint inhibitor regimen shows impressive efficacy in multiple types of cancers [64][65][66]. Conventional immune checkpoints include CTLA4, PDCD1, CD274, IDO1, LAG3, TIGIT and so on [67]. IDO1 is a rate-limiting metabolic enzyme in tryptophan metabolism, which can convert tryptophan into kynurenine [68]. Interferon α stimulates the expression of IDO1 [69], which subsequently induces the inactivation of T cells and NK cells, and the promotion of Tregs and myeloid-derived suppressor cells (MDSCs) [70,71]. Numerous studies have shown that IDO1 is significantly overexpressed in a variety of human cancers and mediates immunosuppression [72][73][74]. In the present study, we revealed that HMCN1 mutation was associated with increased IDO1 expression, indicating that HMCN1 mutation may be a double-edged sword in regulating immune responses. Since our previous results have suggested that HMCN1 mutation can affect metabolic pathways, it may be important to further explore the correlation between HMCN1 mutation and anti-tumor immunity regulated by cellular metabolism.
Several limitations should be considered in this study. First, the sample size of TCGA dataset is limited and covers only patients from western countries, which makes it difficult to apply our conclusion to patients worldwide. There are also differences in age, gender, and ethnicity between samples, thus causing potential errors or biases. Because of insufficient expression and survival information in the UTokyo cohort, it is difficult for us to figure out whether HMCN1 mutation is also a predictive biomarker of prognosis and the immune microenvironment in Japanese patients. Therefore, a further exploration of the HMCN1 mutation pattern in Asian patients is necessary. Second, the TCGA dataset includes only 25 cases with HMCN1 mutation, which might cause biased results. Finally, our present study is only a correlation study based on multidimensional data, which lacks relevant basic experiments in cell lines and clinical samples. In the future, we will carry out clinical and biological experiments to validate the function and clinical significance of HMCN1 mutation in ccRCC and further explore the underlying mechanisms related to HMCN1 mutation in cancers.

Conclusions
In summary, we provided detailed insights into the critical role of HMCN1 mutation in regulating ccRCC progression. The present study suggested that HMCN1 mutation occurs frequently in ccRCC and was related to a higher TMB and a poorer clinical outcome. HMCN1 mutation was considered as an independent prognostic biomarker and may be relevant to cell metabolism and anti-tumor immunity.