Introduction

Gastric cancer (GC) remains a prevailing and lethal disease worldwide1. In China, GC is the fifth most common cancer and the third leading cause of cancer-related deaths2. The high mortality rate is largely attributed to the fact that GC patients usually diagnosed at an advanced stage and the complex molecular mechanisms driving tumor progression3,4. Chemotherapy is the most common treatment for GC patients, but its efficacy is limited5. In recent years, emerging strategies such as targeted therapy and immune checkpoint inhibitors have been recognized as promising new strategies to treat GC patients6,7. For instance, targeted therapies against human epidermal growth factor receptor 2 (HER2)8,9 and Claudin18.210 improve the prognosis of patients with advanced GC. However, not all patients benefit from anti-HER2 or anti- Claudin18.2 treatment, particularly those with negative HER2 or Claudin18.2 expression. Therefore, it is imperative to further elucidate the molecular mechanisms underlying GC progression fully so as to identify novel effective therapeutic targets11.

TMC5 is a member of the TMC protein family, which comprises eight members (TMC1-8). Previous studies have highlighted the role of the TMC family in various cancers. For instance, Song et al. reported that TMC gene family are promising biomarkers for prognosis and immunotherapeutic response according to a pan-cancer analysis12. Lu et al. reported that TMC8 was highly expressed in hepatocellular carcinoma(HCC) and associated with poor prognosis13. Similarly, a pan-cancer analysis indicated that the expression level of TMC5 is upregulated in a variety of tumors and closely related to prognosis and immune infiltration14. Zhang et al. found that TMC5 is highly expressed in prostate cancer, and silencing of TMC5 inhibits the proliferation of prostate cancer cells15. TMC5 is also associated with the tumor microenvironment and prognosis of HCC16. Despite these findings, the role of TMC5 in GC remains poorly understood, warranting further investigation.

This study aims to bridge the existing knowledge gap regarding TMC5 in GC by investigating its expression and functional significance. Our findings are expected to contribute valuable insights into the molecular underpinnings of GC and may pave the way for the development of targeted therapeutic strategies aimed at improving patient outcomes. Given the pressing need for novel biomarkers and therapeutic targets in GC, understanding the role of TMC5 could have significant implications for clinical practice and future research endeavors in this field.

Materials and methods

Bioinformatic analysis

Subcellular expression of TMC5 was investigated through the Human Protein Atlas (HPA) (https://proteinatlas.org/).We obtained gene expression datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database to assess TMC5 mRNA expression levels. The Xiantao Academic (https://www.xiantaozi.com/) and UALCAN database (the University of Alabama at Birmingham CANcer data analysis portal) (https://ualcan.path.uab.edu/) were utilized to investigate the associations between TMC5 expression levels and various clinical characteristics of GC. The receiver operating characteristic (ROC) curve was generated using the pROC software. Kaplan-Meier Plotter (http://kmplot.com/analysis/) was employed to investigate the associations between TMC5 expression and prognosis of GC.

We downloaded and visualized the somatic mutation data of GC patients using the maftools package in R software. cBioPortal database (https://www.cbioportal.org/) was employed to study the mutation frequency, type, distribution, and specific loci.

We downloaded the methylation data from cBioPortal database, and the cor.test function in R software was used to perform correlation tests between TMC5 methylation and expression. DNA methylation status of the TMC5 gene’s CpG sites and the prognostic significance of these CpG sites in GC were explored using the MethSurv database. The data of m6A-related genes are based on the research of Juan Xu et al.17,18,19,20.

We used the One - Class Logistic Regression (OCLR) algorithm constructed by Malta et al. to calculate the mRNAsi21,22. The Spearman correlation was conducted to process the RNA expression data, and then mapped the stemness index to the range of [0, 1] through a linear transformation.

We obtained the corresponding single-cell data in the “.h5” format file and annotation results from the Tumor Immune Single-cell Hub (TISCH) database23. The Model-based Analysis of Single-cell Transcriptomics RegulOme (MAESTRO) and Seurat packages in R software were used to process and analyze the single-cell data, and perform cell clustering and grouping again using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method. The immunedeconv package in R software was used to complete immune score assessment. Next, we utilized algorithms, including Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and xCell to estimate the correlation between TMC5 expression and immune cell infiltration24,25,26.

We used the Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) package in R software to complete the Spearman correction analysis between TMC5 and Immune Score, ESTIMATE score, and stromal score. Tumor Immune Dysfunction and Exclusion score (TIDE) was conducted to assess the therapeutic effect of immune checkpoint inhibitors27. The ggplot2 package was employed to calculate and visualize the Spearman correction analysis between TMC5 and Tumor Mutation Burden (TMB) score of GC. The AnnotationDbi, org.Hs.eg.db, stringr, stringi, ggplot2, and RColorBrewer packages in R software were conducted to analyze the expression differences of immune checkpoint-related genes between the high- and low-expression groups of TMC5.

We used LinkedOmics (http://www.linkedomics.org/), TIMER (https://cistrome.shinyapps.io/timer/) and GEPIA (http://gepia.cancer-pku.cn/) databases to explore the co-expression genes. Following this, we conducted gene set enrichment analysis (GSEA) via LinkInterpreter to assess the Gene Ontology Biological Process (GO_BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways pertaining to TMC5 and its co-expressed genes. The data of ferroptosis-related genes are based on a systematic analysis of the abnormalities and functions of ferroptosis in cancer conducted by ZeXian Liu et al.28.

Samples and immunohistochemistry

A total of 79 GC patients, who received radical gastrectomy, were pathologically and clinically diagnosed at the Second Hospital of Lanzhou University. Following surgical excision, all tumor specimens were confirmed to be GC through hematoxylin and eosin (HE) staining. Normal gastric tissues were collected from a standardized distance of 2 cm from the margins of resected neoplastic tissues and were validated through pathological assessment. This research was sanctioned by the Ethics Committee of Lanzhou University Second Hospital and adheres to the principles outlined in the Declaration of Helsinki. The informed consent was obtained from all subjects and/or their legal guardian(s).

The expression levels of TMC5 in both GC and adjacent non-cancerous tissues were assessed using immunohistochemistry (IHC). The TMC5 rabbit polyclonal antibody was procured from Thermofisher. An anti-TMC5 antibody was utilized at a dilution of 1:300 for IHC, following the manufacturer’s guidelines. The evaluation of TMC5 expression was conducted using the immunohistochemistry score (H-score) system, calculated as H-score = staining intensity × staining area grade. Staining intensity was categorized as follows: 0 (no staining), 1+ (weak staining), 2+ (moderate staining), and 3+ (strong staining). The staining area was classified into the following categories: 0 (no cell staining), 1+ (< 25% stained cells), 2+ (25% to < 50% stained cells), and 3+ (≥ 50% stained cells). In this investigation, an H-score of less than 3 was designated as low expression, while an H-score of 3 or greater was classified as high expression.

Cell culture

All cells were stored in our laboratory, including GES-1, AGS, MKN28, KATOIII, MKN45, HGC27, MGC803. All cell lines were cultured in Roswell Park Memorial Institute 1640 (RPMI-1640) medium with supplementation of 10% fetal bovine serum (FBS) (Invitrogen, Carlsbad, CA, USA) and appropriate amounts of penicillin (100U/ml) and streptomycin (100 μg/ml). Cells were incubated in a humidified atmosphere of 5% CO2 at 37℃.

Lentivirus transfection

Lentivirus (Genechem, China) for silence TMC5 was transfected into HGC27 and MKN45 cell lines (viral titer: 2 × 108 TU/ml) and lentivirus (Genecopoeia, China) for overexpression TMC5 was transfected into AGS cell line (viral titer: 1.45 × 108 TU/ml). Empty lentivirus vector was designed as a negative control. Cell transfection was performed as the manufacturer’s protocol.

Western blot assays

Proteins were subjected to electrophoretic separation utilizing 10% SDS-PAGE, subsequently transferred onto polyvinylidene fluoride (PVDF) membranes (Millipore, Billerica, MA, USA). The membranes underwent a blocking process for 2 h at room temperature using a 5% skim milk solution in TBST. Following this, they were incubated overnight at 4℃ with specific primary antibodies. Post-incubation, the membranes were thoroughly washed three times with TBST and then incubated with secondary antibodies for 2 h at room temperature. This was followed by an additional three washes with TBST. The detection of antibody binding was achieved through enhanced chemiluminescence techniques. The antibodies used in this study were as follows: anti-GAPDH (1:10,000, Thermofisher), anti-TMC5 (1:1000, Thermofisher).

Cell counting kit‑8 (CCK8) assay

AGS, HGC27 or MKN45 cells from each stably transduced samples were seeded in a 96-well plate (Corning, USA) and cultured in an incubator at 37 °C. At the specific time points, 10 µl CCK8 solution (Solarbio, China) was added and then incubated for 1 h. Finally, the absorbance value was measured at the wavelength of 450 nm.

5‑Ethynyl‑2′‑deoxyuridine (EdU) assay

The EdU assay was conducted utilizing an EdU assay kit sourced from RiboBio (China). For this procedure, cells were treated with a 50 µM EdU solution at room temperature for a duration of 6 to 8 h. Following incubation, the cells were fixed using a 4% neutral paraformaldehyde solution at room temperature for 30 min. Subsequently, cell membranes were permeabilized with phosphate-buffered saline (PBS) containing 0.5% Triton X-100 for 20 min, after which the cells underwent three washing cycles with PBS. The cells were then subjected to staining with Apollo staining reagent (KeyGEN, China) for 20 min, followed by three washes with NaCl/PI. The nuclei of the cells were stained with 4′,6-diamidino-2-phenylindole (DAPI, KeyGEN) and examined using a microscope for visualization.

Colony‑formation assay

Stably transduced cells were trypsinized and cultured in 6 well plates for 10–14 days, respectively. For scoring colonies, the cells were fixed in 1 ml methanol for 10 min and stained with 0.1% crystal violet for 10 min. Number of colonies was quantified.

Cell invasion and wound healing assays

Stably transduced cells were trypsinized and cultured in the upper chamber of the transwell chamber. After incubation for 24 h, cells invading to the lower chambers were fixed and stained. The number of invaded cells was measured under a microscope. As to wound healing assay, we first starved cells seeded in six-well plates for 12 h to leave them in cell cycle synchronization, and then scratched the confluent monolayer of cells with sterile 200-µl pipette tips for artificially creating wounds. The wound healing process was observed and photographed under a microscope at indicated time points.

Animal studies

To explore the role of TMC5 in regulating GC growth, we evenly divided 6 BALB/c nude mice(Beijing Vital River Laboratory Animal Technology Co., Ltd) into 2 groups in a random manner. MKN45-Vector and MKN45-shTMC5 cells were injected subcutaneously into nude mice. The tumor volume was calculated by the formula (length ×width2)/2 every 5 days. All mice were euthanized by excessive isoflurane anesthesia after 25 days, and the tumors were photographed.

Two groups of metastatic models were designed, NOD/SCID mice (Beijing Vital River Laboratory Animal Technology Co., Ltd) were placed under isoflurane anesthesia and laparotomized, after which MKN45-Vector and MKN45-shTMC5 cells were injected into the spleen to form in vivo liver metastasis. All mice were euthanized by excessive isoflurane anesthesia for excision of liver after 26 days. Animal protocols were approved by the Institutional Animal Care and Use Committee of Lanzhou University Second Hospital. Moreover, all animal experiments were also performed based on the Declaration of Helsinki and the guidelines established by the National Institutes of Health Guide for the Care and Use of Laboratory Animals. This study was reported in accordance with ARRIVE guidelines.

RNA extraction and qRT‑PCR

Total RNA was extracted from GC cell lines utilizing AG RNAex Pro Reagent (Accurate Biology, China) according to the manufacturer’s instructions. Subsequently, the extracted RNA was reverse transcribed into cDNA(Accurate Biology, China). Quantitative real-time (qRT)-PCR was performed on qRT-PCR system (Bio-Rad) employing SYBR Green Premix (Accurate Biology, China). qRT-PCR protocol consisted of the following conditions: 95 °C 30 s; (95 °C 5 s, 60 °C 30 s) × 40 amplification cycles. GAPDH served as internal control for mRNA. The relative expression levels of the genes were calculated using the comparative 2 − ΔΔCt method.

Statistical analysis

The statistical analyses and graphical representations in this study were predominantly conducted utilizing the R software, unless specified noted. To determine the significance of the results obtained, a variety of statistical tests were applied. Specifically, the Student’s t-test was utilized for parametric datasets, while the chi-square test was employed to assess categorical variables. Furthermore, Pearson’s correlation coefficient was calculated to investigate linear relationships, whereas the Spearman’s correlation coefficient was utilized to examine non-linear associations. For nonparametric comparisons, the Wilcoxon rank-sum test was applied. The findings from the Kaplan-Meier Plotter and MethSurv databases are illustrated alongside hazard ratios (HR) and corresponding P-values, which were determined through log-rank tests. Additionally, Spearman’s rank correlation analysis was executed to assess the statistical relationships between TMC5 expression and factors associated with immunotherapy. The criteria for statistical significance were established as *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Results

TMC5 is highly expressed in GC and predicts poor prognosis

According to the information from HPA, TMC5 was mainly expressed in plasma membrane and nucleoplasm (Fig. 1A). The TMC5 expression levels were significantly increased in the GC tumor tissues than in normal tissues (Fig. 1B). Further analysis revealed that TMC5 exhibited higher expression levels in GC patients older than 65 years compared to those younger than 65 years(Fig. 1C), showed elevated expression in Caucasians relative to African-Americans and Asians(Fig. 1D), and demonstrated increased expression in intestinal-type gastric adenocarcinomas(Fig. 1E).

Then, we plotted the ROC curve and found that the area under the curve (AUC) was high, demonstrating the valuable diagnostic ability of TMC5 in GC (Fig. 1F). Figure 1G shows that GC patients with high TMC5 expression had poorer overall survival (OS), first progression and post progression survival according to the Kaplan-Meier Plotter database.

Fig. 1
figure 1

TMC5 is highly expressed in GC and predicts poor prognosis. (A) Subcellular expression of TMC5 based on the HPA. (B)TMC5 mRNA expression levels in GC and normal samples. (C–E) TMC5 expression levels were associated with age, race and histological subtypes according to the Xiantao Academic (C) and UALCAN database (D, E). (F)The ROC curve reflected the TMC5’s diagnostic ability for GC. (G) TMC5’s influence on poorer OS, First Progression and Post Progression survival from the Kaplan-Meier Plotter database. *p < 0.05; **p < 0.01; ***p < 0.001.

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Mutation and Methylation of TMC5 in GC

We assessed TMC5 mutation rates in GC, the relatively low mutation rate indicates that TMC5 could be a stable biomarker and therapeutic target (Fig. 2A, B). Then, we obtained the mutational data of TMC5 from the cBioPortal website, Fig. 2C illustrates the mutation rate, type and distribution of the mutation spot. Further analysis revealed the alteration frequency across various histological subtypes of GC(Supplementary Fig. 1A) and the mutation status of genes in the signaling pathway associated with TMC5 mutation(Supplementary Fig. 1B).

TMC5 methylation was negatively correlated with TMC5 expression level in GC (Fig. 2D). Utilizing the MethSurv tool, we found 11 methylated CpG islands: cg07792815, cg07500547, cg005160554, cg25690265, cg07157333, cg13639881, cg03521656, cg05335886, cg05951521, cg03088525, and cg08051231 (Fig. 2E). Among these, methylation levels of two CpG islands, cg25690265 and cg03521656, were associated with the prognosis of GC patients (p < 0.05) (Fig. 2F, G). Additionally, the association between TMC5 expression levels in GC and 24 m6A-related genes was explored. TMC5 expression was significantly and positively correlated with CBLL1, METTL14, METTL3, RBM15, VIRMA, WTAP, YTHDC1, YTHDC2, YTHDF3, ZC3F13, EIF3A, HNRNPA2B1, HNRNPC, RBMX, YTHDF1, and YTHDF2(Supplementary Fig. 1C).

Fig. 2
figure 2

Mutation and methylation analysis of TMC5 in GC. (A) The mutation distribution of the TMC5 gene. (B) The somatic mutation landscape of GC cohort. (C) The mutation rate, type and distribution of the mutation spot according to the cBioPortal website. (D) Correlation between TMC5 methylation and expression level in GC. (E) The heat map of DNA methylation at CpG sites in the TMC5 gene by the MethSurv database. (F, G) The association between methylation levels of two CpG islands of the TMC5 gene and OS of GC patients: (F) cg25690265; (G) cg03521656.

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TMC5 is upregulated in GC

IHC assay was used to study the expression of TMC5 in GC samples. The TMC5 expression was significantly increased in the tumor tissues than the normal tissues (Fig. 3A, B). The high-expression rate of GC tissues was 64.56% (54/79) and that of paracancerous tissues was 24.05% (19/79), and the H-score of GC tissues was significantly higher than that in paracancerous tissues (Supplementary Table 1).

We further examined TMC5 expression in various GC cell lines (AGS, MKN28, KATOIII, MKN45, HGC27, MGC803) and normal gastric epithelial cells (GES-1) using Western blot. TMC5 expression was upregulated in human GC cell lines (MKN45, KATOIII, HGC-27 and MGC-803) compared to GES-1(Fig. 3C). To investigate the role of TMC5 in GC, we stably overexpressed TMC5 in AGS cells (Fig. 3D) and silenced TMC5 in HGC-27 and MKN45 cell lines (Fig. 3E, F).

Fig. 3
figure 3

Expression of TMC5 in GC and construction of lentiviral transfection models. (A, B) Expression of TMC5 in GC patients’ samples; (C) Expression of TMC5 in seven cell lines; (D)Overexpression of TMC5 in AGS cells; (E, F) Knockdown of TMC5 in HGC-27 and MKN45 cells.

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TMC5 promotes proliferation of GC

To investigate the role of TMC5 in GC proliferation, we conducted three in vitro functional assays, including CCK-8 assay (Fig. 4A), colony formation assay (Fig. 4B), and EdU assay (Fig. 4C). The results demonstrated that TMC5-silenced HGC-27 and MKN45 cells exhibited significantly reduced proliferation capabilities, whereas AGS cells with TMC5 overexpression showed markedly enhanced proliferation.

To further confirm results of in vitro assays, we constructed nude mouse cell-derived xenograft (CDX) models using TMC5-silence MKN45 cells, as well as the corresponding vector control cell. The growth condition of subcutaneous tumors in nude mice was measured and recorded every five days. As shown in Fig. 4D, knockdown of TMC5 inhibited tumor growth in vivo. Taken together, these results demonstrated that TMC5 promotes proliferation of GC.

Fig. 4
figure 4

The effect of TMC5 on the proliferation of GC. (A) CCK8 assay. (B) Colony-formation assay. (C) EdU assay. (D) The volume of subcutaneous tumors in knockdown group was smaller than control group. *p < 0.05; **p < 0.01; ***p < 0.001.

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TMC5 promotes metastasis of GC

To explore the role of TMC5 in GC migration and invasion, we performed wound healing assay and transwell assay. As shown in Fig. 5A and B, TMC5-silenced HGC-27 and MKN45 cells exhibited significantly reduced migration and invasion, while TMC5-overexpressing AGS cells demonstrated enhanced migration and invasion capabilities (Fig. 5C). Given TMC5’s role in promoting migration and invasion in vitro, we further examined its potential to enhance GC metastasis in vivo. TMC5-silenced MKN45 cells and their corresponding vector control cells were injected into the spleens of nude mice. Twenty-six days later, the mice were sacrificed, and liver metastatic nodules were measured. Notably, mice injected with TMC5-silenced MKN45 cells had fewer liver metastatic nodules compared to the control group (Fig. 5D). These results demonstrated that TMC5 plays a positive role on metastasis of GC.

Fig. 5
figure 5

The effect of TMC5 on metastasis of GC cell lines. (A–C) Wound healing assay and transwell assay were performed for HGC-27 (A), MKN45 (B) and AGS(C) cell lines. (D) Reduced liver metastatic nodules in mice injected with TMC5-silenced MKN45 cells compared to control mice. *p < 0.05; **p < 0.01; ***p < 0.001.

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TMC5 enhances chemotherapy resistance of GC

The cancer stemness plays a critical role in chemotherapy resistance. We investigated the relationship between TMC5 and GC stemness. We found that the stemness in GC patients with high expression of TMC5 was higher than that in patients with low expression (Fig. 6A).

To further investigate the correlation between TMC5 and chemotherapy resistance, we examined the effect of TMC5 silencing on the sensitivity of HGC-27 cells to paclitaxel. The inhibition rate curve demonstrated that the inhibitory effect of paclitaxel on HGC27 cells was significantly enhanced after TMC5 silencing (Fig. 6B). Additionally, the dose-response curve and IC50 values revealed that the IC50 of the TMC5-silenced group was lower than that of the control group, indicating increased sensitivity to paclitaxel in TMC5-silenced HGC-27 cells (Fig. 6C). These results suggest that TMC5 enhances chemotherapy resistance in GC.

Fig. 6
figure 6

TMC5 enhances chemotherapy resistance of GC. (A) Correlation between TMC5 expression and GC stemness. (B) The inhibition rate curve. (C) Dose-response curve and IC50 values of HGC-27 for paclitaxel. G1, TMC5 low expression; G2, TMC5 high expression. *p < 0.05; **p < 0.01; ***p < 0.001.

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The role of TMC5 in the tumor microenvironment and immunotherapy response

We analyzed single-cell RNA sequencing (scRNA-seq) data from dataset GSE167297. After quality control and filtration, we used t-SNE to reduce the dimensionality of the data and used a graph-based clustering algorithm to divide the cells into nine different clusters (Fig. 7A). Further analysis revealed that TMC5 was highly expressed in glandular mucous cells, pit mucous cells, and epithelial cells (Fig. 7A).

Using CIBERSORT software, we compared immune cell infiltration levels between the high and low TMC5 expression groups. Significant differences were observed in several immune cell types, including monocytes, memory B cells, resting NK cells, activated myeloid dendritic cells, and neutrophils (Fig. 7B). Meanwhile, xCell analysis identified differentially infiltrated immune cells, such as NK T cells, M2 macrophages, CD4 + memory T cells, CD4 + central memory T cells, mast cells, gamma delta T cells, regulatory T cells, and hematopoietic stem cells (Fig. 7C).

Next, we explored the role of TMC5 in immunotherapy response. As shown in Fig. 7D, TMC5 was negatively correlated with ESTIMATE score and stromal score in GC. Additionally, we found that TMC5 expression was negatively correlated with TIDE score and positively correlated with TMB score (Fig. 7E, F). Furthermore, TMC5 expression was positively associated with immune checkpoint genes, including CD274, HAVCR2, and SIGLEC15 (Fig. 7G).

Fig. 7
figure 7

Correlation analysis of TMC5’s expression and immune cells’ infiltration as well as immunotherapy response in GC. (A) Immune cell infiltration levels based on scRNA-seq data. (B, C) Differentially infiltrated immune cells identified by CIBERSORT (B) and xCell (C) analyses. (D) Correlation analysis between TMC5 and immune score, ESTIMATE score and stromal score. (E, F) Negative correlation between TMC5 expression and TIDE score (E), and positive correlation with TMB score (F). (G) Association of TMC5 expression with immune checkpoint genes. G1, TMC5 low expression; G2, TMC5 high expression. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.

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TMC5 co-expression network

We analyzed the TMC5 co-expression network using the LinkedOmics database. Heatmaps displayed the top 50 genes positively (Fig. 8A) and negatively (Fig. 8B) associated with TMC5. Scatter diagrams illustrated the top 10 positively and negatively correlated genes (Supplementary Fig. 2). Subsequently, GSEA was performed to identify the GO_BP and KEGG pathways associated with the genes co-expressed with TMC5. GO_BP analysis revealed that TMC5 and its co-expressed genes were primarily involved in regulating signaling pathways and transportation (Fig. 8C). Additionally, KEGG pathway analysis29 indicated significant enrichment of TMC5 co-expressed genes in biosynthesis, metabolic pathways, and multiple classic cancer-related pathways, including TNF, mTOR, Wnt, and TGF-β signaling pathways (Fig. 8D).

After comprehensively analyzing TMC5 co-expressed genes in the TIMER (Fig. 8E) and GEPIA (Fig. 8F) databases, we selected Tripartite Motif-Containing Protein 31(TRIM31), Dimethylarginine Dimethylaminohydrolase 1(DDAH1), and Anti-Silencing Function 1B(ASF1B) for further validation. Finally, qRT-PCR results confirmed that the expression levels of TRIM31, DDAH1, and ASF1B in MKN45 and HGC-27 cells were positively correlated with TMC5 expression (Fig. 8G).

Additionally, we investigated the association between TMC5 and ferroptosis-related genes. TMC5 expression was positively correlated with FANCD2, FDFT1, HSPA5, NFE2L2, SAT1, SLC7A11, ACSL4, ATP5MC3, CARS1, CS, DPP4, LPCAT3, and NCOA4, while negatively correlated with GPX4, HSPB1, and ALOX15 (Fig. 8H).

Fig. 8
figure 8

TMC5 co-expression network. (A, B) The top 50 positively (A) and negatively (B) co-expressed genes of TMC5. (C) GO_BP analysis for TMC5. (D) KEGG pathway analysis for TMC5. (E, F) Association of TMC5 with TRIM31, DDAH1, and ASF1B based on the TIMER (E) and GEPIA (F) databases. (G) qRT-PCR analysis showing the association between TMC5 and TRIM31, DDAH1, ASF1B. (H)Association of TMC5 with ferroptosis-related genes. G1, TMC5 low expression; G2, TMC5 high expression. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.

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Discussion

This study comprehensively investigated the role of TMC5 in GC, revealing its significant upregulation in tumor tissues compared to normal tissues and its association with poor prognosis. We demonstrated that TMC5 promotes GC cell proliferation, metastasis, and chemotherapy resistance. Additionally, TMC5 was found to modulate the tumor microenvironment and influence immune cell infiltration, suggesting its potential as a therapeutic target. The study’s strengths lie in its multi-faceted approach, combining clinical data with mechanistic insights, which provides a robust foundation for understanding TMC5’s role in GC.

According to the analysis of data from diverse public databases14, TMC5 exhibits dual, context-dependent roles in different cancers, with high expression linked to poor survival in kidney renal clear cell carcinoma, hepatocellular carcinoma, and lung squamous cell carcinoma, while low expression correlates with worse outcomes in esophageal carcinoma, head and neck squamous cell carcinoma, and uterine corpus endometrial carcinoma. We found that the expression of TMC5 in GC is significantly higher than in normal samples, and is correlated with poor prognosis of GC patients. Further experimental validation confirmed the upregulation of TMC5 in GC tissues and cell lines. In addition, the mutation rate of TMC5 is low in GC. Aberrant DNA methylation in GC can affect biological behaviors such as cell proliferation, apoptosis, invasion, and metastasis by regulating the expression of oncogenes and tumor suppressor genes30. By integrating the methylation data from the cBioPortal database and the survival analysis function of the MethSurv database, we analyzed the epigenetic regulatory mechanism and clinical value of TMC5 in GC, from the level of DNA methylation to its significance in clinical prognosis. Collectively, these findings highlight the potential role of TMC5 as an oncogenic factor and stable biomarker for prognosis, and indicate that targeting this gene could provide therapeutic strategies.

In terms of gene functionality and cell behavior, the results indicating that TMC5 enhances the proliferation, migration, and invasion of GC cells are particularly noteworthy. Silencing TMC5 significantly reduced these capabilities in GC cell lines, suggesting that TMC5 functions as an oncogene in GC. Li et al. reported that TMC5 drives hepatocellular carcinoma progression by regulating epithelial-mesenchymal transition31.The identification of TMC5’s involvement in promoting aggressive tumor behaviors sheds light on the adaptive mechanisms of cancer cells in hostile environments. These findings underscore the necessity for further research into the cellular pathways affected by TMC5 and how these pathways can be manipulated to combat GC progression effectively.

Chemotherapy is one of the main treatments for GC, especially for advanced GC patients. Chemotherapy resistance is a pivotal factor contributing to unfavorable outcomes in GC32. Therefore, specifically clarifying the mechanisms of chemotherapeutic drug resistance and identifying biomarkers is critical for developing both optimal and novel treatment strategies33. Cancer stemness plays important role in treatment resistance in GC34. According to the bioinformatic analysis, we found that TMC5 is positively related to stemness. The enhanced sensitivity of HGC-27 cells to paclitaxel upon TMC5 silencing, as reflected by decreased IC50 values, highlights its role in chemotherapy resistance. Understanding the molecular underpinnings, perhaps involving drug efflux pumps, DNA repair mechanisms, or anti-apoptotic pathways, could guide the development of combination therapies35. TMC5 may serve as a predictive biomarker, facilitating personalized chemotherapy regimens aimed at maximizing efficacy while minimizing toxicity.

The observed correlations between TMC5 expression and immune cell infiltration, along with its associations with ESTIMATE score, stromal score, TIDE score, TMB score and immune checkpoint genes, suggest a complex interplay in the tumor immune microenvironment12,36. Strategies targeting TMC5 in combination with immunotherapies, such as immune checkpoint inhibitors, hold promise for enhancing treatment responses37. Furthermore, deciphering TMC5’s impact on immune cell differentiation and function could lead to the development of more effective immunomodulatory agents38.

Our co-expression analysis identified several genes associated with TMC5, and qRT-PCR validation confirmed the positive correlation between TMC5 and TRIM31, DDAH1, ASF1B. TRIM31 is significantly overexpressed in GC and correlates with poor prognosis, promoting GC progression by activating the Wnt/β-catenin signaling pathway through the regulation of Axin1 protein stability39,40. DDAH1 has been implicated in Wnt/GSK-3β signaling in GC41. ASF1B, which is significantly upregulated in GC tissues and cells, promotes GC cell proliferation, migration, and invasion through pathways such as FOXM1/ASF1B/PRDX3 and Myc42,43,44,45. Additionally, GSEA analyses suggest that the molecular mechanisms underlying TMC5’s effects may involve its interaction with various oncogenic signaling pathways, including TNF, mTOR, Wnt, and TGF-β pathways. These findings suggest that TMC5 modulate critical cellular processes, thus contributing to GC development and progression. Moreover, the exploration of TMC5’s role in these pathways could pave the way for innovative approaches to treating GC through the modulation of these signaling cascades and the development of TMC5-targeted therapies.

Despite these insights, our study has certain limitations. The relatively small sample size of clinical, animal data and single-cell analysis may limit the robustness of our conclusions. Furthermore, the lack of in-depth mechanistic studies on TMC5’s interactions with signaling pathways and epigenetic regulators represents a gap that future research should address. In future studies, we plan to conduct comprehensive in vitro and in vivo experiments combined with pathological investigations to further elucidate TMC5’s role in GC development, progression and treatment sensitivity. These investigations will particularly focus on its interactions with the tumor immune microenvironment and specific signaling pathways, to uncover the precise molecular mechanisms involved.

In conclusion, our study establishes TMC5 as a significant player in GC, correlating with poor prognosis and aggressive tumor behavior. TMC5’s multifaceted role in GC development encompasses not only tumor cell proliferation and metastasis but also modulation of the tumor immune landscape, suggesting its potential as both a prognostic biomarker and therapeutic target. These findings provide a theoretical basis for future research aimed at elucidating the mechanistic pathways involving TMC5 and exploring its utility in clinical settings, ultimately contributing to improved management strategies for GC patients.