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. 2022 Jan 31:12:813100.
doi: 10.3389/fonc.2022.813100. eCollection 2022.

Pan-Cancer Analysis Reveals Genomic and Clinical Characteristics of TRPV Channel-Related Genes

Xiaoxuan Wang  1 , Guanghao Li  2 , Yidan Zhang  1 , Lanfang Li  1 , Lihua Qiu  1 , Zhengzi Qian  1 , Shiyong Zhou  1 , Xianhuo Wang  1 , Qiang Li  2 , Huilai Zhang  1
Affiliations
Free PMC article

Pan-Cancer Analysis Reveals Genomic and Clinical Characteristics of TRPV Channel-Related Genes

Xiaoxuan Wang et al. Front Oncol. .
Free PMC article

Abstract

Background: Transient Receptor Potential channels (TRPs), a class of ion channels, were first described two decades ago. Many TRP family members are major participants in nociception and integration of heat and pain signals. Recent studies have revealed that subfamilies of this channel, such as members of transient receptor potential vanilloid (TRPV) channels, play important roles in breast, ovarian, prostate, and pancreatic cancers.

Methods: We performed a comprehensive analysis of TRPVs in 9125 tumor samples of 33 cancer types using multi-omics data extracted from The Cancer Genome Atlas (TCGA). We identified differences in mRNA expression in a pan-cancer analysis, and the genomic characteristics of single nucleotide variations, copy number variations, methylation features, and miRNA-mRNA interactions using data from TCGA. Finally, we evaluated the sensitivity and resistance to drugs targeting TRPV channel-related genes using the Cancer Therapeutics Response Portal (CTRP) and the Genomics of Drug Sensitivity in Cancer (GDSC) database. Finally, we validated the drug sensitive data and the importance of TRPV6 in two cancer cell lines using q-PCR assay, CCK8 assay, EdU assay and scratch assay.

Results: Extensive genetic alterations in TRPV channel-related genes and differences in gene expression were associated with the activity of cancer marker-related pathways. TRPV channel-related genes can be used as prognostic biomarkers. Several potential drugs, such as lapatinib, that may target TRPV channel-related genes were identified by mining the genomics of drug sensitivity.

Conclusion: This study revealed the genomic changes and clinical characteristics of TRPV channel-related regulatory factors in 33 types of tumors. This analysis may help uncover the TRPV channel-related genes associated with tumorigenesis. We also proposed novel strategies for tumor treatment.

Keywords: TRPV channel-related genes; genomics; methylation; pan-cancer; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
TRPV and subtype analysis of gene expression. (A) The heat map shows the expression profiles of the TRPV regulators in the GTEx dataset. (B) Significant differences in mRNA expression between normal and tumors in TCGA database. (C) Changes in mRNA expression in different cancer subtypes. (D) Survival analysis of TRPV regulators. The size of the dots represents the significance of the gene’s influence on survival for each cancer type,and the statistical significance of differences was determined by cox regression analysis. (E) TRPV1 and TRPV5 high expression contributes to the prognosis of cancer. All the significant differential expressed genes are shown in the figure.
Figure 2
Figure 2
Single nucleotide variation (SNV) frequency and variant mutation types of TRPV channels. (A) Mutation frequency of TRPV channels. Numbers represent the number of percentages that have the corresponding mutated gene for a given cancer. The number ‘0’ indicates that there was no mutation in the gene coding region, and the blank space indicates that there was no mutation in any region of the gene. (B) SNV waterfall plot showing the mutation distribution of TRPV channels and a classification of variant SNV types.
Figure 3
Figure 3
Copy number variation of TRPV channel-related genes. (A) CNV pie chart of 33 cancers. The CNV pie chart shows the combined heterozygous/homozygous CNV ratio of each regulator in each cancer. A pie chart representing the proportion of different types of CNV for each TRPV gene per cancer subtype; different colors represented different types of CNV. (B) The heterozygous CNV profile shows the percentage of heterozygous CNV, including the percentage of heterozygous amplification and deletion for each TRPV subtype gene in each cancer subtype. (C) The homozygous CNV profile shows the percentage of homozygous CNV, including the percentage of homozygous amplification and deletion for each TRPV subtype gene in each cancer. Only a gene with >5% CNV in a given cancer appears as a dot on the graph. (D) CNV correlation with TRPV channel mRNA expression. The Pearson product-moment correlation coefficient was used to study the association between CNV and mRNA expression. All dots with significant differences are shown. The size of a dot represents statistical significance, and the larger the size of the dot, the higher the statistical significance.
Figure 4
Figure 4
Methylation and survival of each TRPV channel-related genes. (A) Differential methylation in TRPV channels between tumor (T) and normal (N) samples in each cancer. Red dots represent increased methylation in tumors and blue dots represent decreased methylation in tumors. The darker the dot color, the larger the difference in methylation level. (B) Correlation between methylation and mRNA gene expression. Red points represent a positive correlation, and blue dots represent a negative correlation. The darker the dot color means the stronger the correlation. (C) Survival difference between TRPV regulators with high and low methylation levels and samples. Red dots represent worse survival of the hypermethylation group; blue dots represent the opposite. The dot size represents the statistical significance, the larger the dot size means, the higher the statistical significance. (D) Prognosis analysis of TRPV6 methylation status in brain lower grade glioma (LGG) and TRPV2 methylation in the lymphoid neoplasm, diffuse large B-cell lymphoma (DLBC). P < 0.05; FDR, false discovery rate.
Figure 5
Figure 5
The miRNA regulation network of TRPV channel-related genes. The connection between mRNA and miRNA indicates that the mRNA is regulated by this miRNA. The larger the yellow dot size, the greater the association with miRNA regulation. The size of arrow edge width depends on the absolute value of the correlation coefficient.
Figure 6
Figure 6
The pathway activity network between TRPV channels. (A) The combined percentage of TRPV regulator genes influencing pathway activity. (B) The line represents the connection between different pathways, where solid lines of the connecting pathways represent activation and dotted lines of the connecting pathways represent inhibition. The colors of the lines represent different types of cancer.
Figure 7
Figure 7
Drug sensitivity analysis of TRPV channel-related genes. (A) The gene set drug sensitivity analysis from Genomics of Drug Sensitivity in Cancer (GDSC) IC50 drug data. (B) The gene set drug sensitivity analysis from Cancer Therapeutics Response Portal (CTRP) IC50 drug data. The Pearson’s correlation indicates the correlation between gene expression and drugs sensitivity. Blue bubbles represented negative correlations, and red bubbles represented positive correlations; the deeper the color, the higher the correlation. The bubble size was positively correlated with the FDR significance. The black outline indicates an FDR < 0.05. Only the top 30 ranked drugs were included.
Figure 8
Figure 8
Effects of lapatinib and TRPV6 knockdown on cell proliferation and apoptosis in cancer cells. (A) TRPV channels mRNA expression in MDA-MB-231 and A549 cell lines after treatment with lapatinib. (B) The effect of TRPV6 knockdown and exposure to lapatinib on cell proliferation in MDA-MB-231 and A549 cell lines examined by the CCK8 assay and the (C) EdU assay. (D) The scratch assay was performed in MDA-MB-231 and A549 cells treated with shTRPV6 or 8 μM lapatinib. Values were expressed as mean ± SD from three independent experiments (Student’s t test, *P < 0.05, **P < 0.01, ***P < 0.001). Scale bars, 50 μm.
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