TNMD in pan-cancer analysis: Exploring its impact on immune modulation and uncovering functional insights in colorectal cancer

doi:10.21203/rs.3.rs-5305163/v1

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Research Article

TNMD in pan-cancer analysis: Exploring its impact on immune modulation and uncovering functional insights in colorectal cancer

https://doi.org/10.21203/rs.3.rs-5305163/v1

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Background

Tendomodulin (TNMD) is pivotal in various malignancies, including colorectal cancer (CRC). However, its comprehensive impact across cancers, particularly its immunomodulatory function in CRC, remains underexplored. This study explored the role of TNMD in CRC by focusing on its immunomodulatory functions through comprehensive molecular and clinical analyses.

Methods

Multiple bioinformatics databases and analytical tools were utilized for the TNMD in pan-cancer analysis. To validate the role of TNMD in CRC, we performed experiments, including immunofluorescence (IF), immunohistochemistry (IHC), real-time quantitative reverse transcription PCR (qPCR), Western blotting, and cell migration assays.

Results

TNMD expression and gene mutation vary across cancers and offer high diagnostic value. Survival analysis has found that TNMD is associated with prognosis in multiple cancers. Notably, in patients with high microsatellite instability (MSI-H) CRC, TNMD expression correlated positively with various immune cells, particularly natural killer (NK) cells, whereas it was inversely correlated with regulatory T cells (Tregs). Crucially, in patients with microsatellite stability (MSS) CRC, high TNMD expression was associated with better immunotherapy outcomes, indicating its potential as a biomarker for patient stratification and tailored treatment approaches. Furthermore, single-cell sequencing data revealed stronger interactions between TNMD-positive tumor cells and fibroblasts or macrophages in the tumor microenvironment. Finally, TNMD was overexpressed in CRC tumor tissues and cell lines, thereby promoting invasion and metastasis.

Conclusions

Our findings reveal a critical immunomodulatory role of TNMD in CRC, particularly in influencing tumor–immune interactions. Beyond its potential diagnostic and prognostic biomarker, TNMD promotes CRC metastasis and invasion, thus emerging as a promising therapeutic target. These findings highlight TNMD's significance in CRC and potentially other malignancies.

TNMD

Pan-cancer

MSI-H

MSS

Immunotherapy

Since the 20th century, cancer has posed a major health threat, with tumor heterogeneity hindering therapy effectiveness and resulting in low cure rates. Integrating targeted and immunotherapies is crucial for future advancements. Leveraging bioinformatics, molecular biology, genetics, and immunology is essential for identifying new tumor biomarkers and developing precise diagnostics and personalized treatments to improve cure rates.

The Tenomodulin (TNMD) gene on chromosome Xq22 encodes a protein linked to chondromodulin-1. Discovered in mouse muscle cells, TNMD undergoes selective splicing, with its most common isoform containing seven exons, promoting cartilage cell growth and inhibiting endothelial tube formation [1–3]. Genetic variations in TNMD correlate with type 2 diabetes, central obesity, chronic obstructive pulmonary disease, and serum levels of systemic immune mediators in a body-type-dependent manner [4–7]. Despite advancements in TNMD research, its role in tumors remains limited, primarily focusing on prognostic implications in cervical cancer and pathogenesis in breast cancer [8, 9]. Our research demonstrates that TNMD is upregulated across various tumors and significantly associated with cancer grading, staging, and patient survival outcomes. Importantly, TNMD correlates strongly with microsatellite instability (MSI) and tumor mutational burden (TMB) in colorectal adenocarcinoma (COAD). Despite these pivotal findings, considerable gaps remain in research regarding the potential of TNMD as a diagnostic biomarker in pan-cancer settings, particularly within the tumor immune microenvironment. A deeper exploration of TNMD’s function in this microenvironment is crucial, as it could enhance our understanding of tumor progression and revolutionize cancer management, particularly in COAD and rectal adenocarcinoma (READ), through more tailored diagnostic and therapeutic strategies. Our study employs The Cancer Genome Atlas (TCGA) + Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO) datasets for a pan-cancer analysis of TNMD, analyzing its expression, survival outcomes, and protein interactions. We focused on TNMD expression differences in microsatellite stability (MSS) and microsatellite instability-high (MSI-H) COAD, as well as immune cell infiltration, using single-cell sequencing and in vitro experiments to explore its role in colorectal cancer (CRC) progression. This research aims to bridge a significant knowledge gap and catalyze advancements in oncological therapeutics.

Data collection, gene expression analysis, and genetic alteration analysis

RNA expression data were downloaded from TCGA (https://portal.gdc.cancer.gov/) and GTEx (https://www.gtexportal.org/home/). We assessed TNMD mRNA levels in 31 normal and 33 tumor tissues using a log2(value + 1) transformation. Using the “stats” and “car” packages, we compared TNMD expression between cancer and normal tissues, visualized with “ggplot2,” and applied the Wilcoxon rank sum test (p < 0.05). A summary of TNMD RNA and protein expression was obtained from the Human Protein Atlas (HPA) (https://www.proteinatlas.org/). cBioPortal analyzed the genetic alteration frequency, mutation type, and mutation site for TNMD [10]. We downloaded mRNA profiles of patients with CRC from GEO (GSE81986, GSE35452, GSE179351), using GSE81986 as the validation dataset.

ROC analysis, pathological stage difference, and survival analysis

Patients in TCGA tumor data were divided into high and low TNMD mRNA expression groups using median values and 50% cut-offs. Receiver operating characteristic (ROC) curves were generated using the “pRO” package and displayed with “ggplot.” Histograms of TNMD expression across cancer stages were created in R with “ggplot” to assess the significance of each stage. Survival analyses were performed with the survival package, using surv_cutpoint from survminer for optimal cut-offs, with results visualized using survminer and ggplot2.

PPI network analyses and gene function enrichment analysis

Potential interacting proteins of TNMD were identified from the STRING database with a confidence threshold of > 0.4. These proteins were analyzed in a PPI network and visualized in Cytoscape (v3.8.2), using the MCC from cytoHubba to identify the top 10 hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using “ggplot,” “igrap,” and “ggrap” packages in R, visualizing closely associated genes from STRING. A significance cut-off for GO and KEGG was set at p-value < 0.05.

GSEA

Patients in the TCGA cohort for each cancer type were divided into high and low groups based on TNMD expression levels. Differentially expressed genes (DEGs) between these groups were used for Gene Set Enrichment Analysis (GSEA). The cancer-related hallmark gene set file (h.all.v2023.2.Hs.symbols.gmt) was downloaded from the Molecular Signatures Database. Results were summarized and visualized in a bubble plot using the R package ggplot2.

Single-cell sequencing data download, processing, and cell-cell communication analysis

The single-cell dataset GSE132465 of CRC was downloaded from the GEO database, containing 23 samples. Quality control was performed by retaining cells with less than 20% mitochondrial genes and more than 500 total genes. We identified 2000 highly variable genes. The 23 samples were integrated using Harmony, and different cell clusters were annotated with the SingleR package. We utilized CellChat, a tool that quantitatively infers intercellular communication networks from scRNA-seq data. It uses a database of human ligand-receptor interactions and pattern recognition techniques to detect communication at the pathway level and calculate the communication network of aggregated cells. All parameters were set to default.

Correlational research on TNMD, MSI, and TMB

We obtained the MSI scores for each tumor from a previous study (Landscape of Microsatellite Instability Across 39 Cancer Types, DOI:10.1200/PO.17.00073). The MSI scores were integrated with the gene expression data for the samples. We used the TMB function from the R package maftools to calculate the TMB for each tumor. The TMB and gene expression data were integrated for further analysis. Correlation between TNMD, TMB, and MSI was assessed using Spearman correlation analysis.

TIDE and Merck18 scores

To predict the response to immunotherapy across various TNMD subgroups, we computed TIDE scores and Merck18 scores for CRC samples using the algorithm developed by previous research [11, 12].

Cell culture and transfection

The colorectal carcinoma cell lines LOVO, HCT116, HT29, and the colon mucosa cell line NCM460 were obtained from the Chinese Academy of Sciences. LOVO, HCT116, and HT29 were cultured in RPMI Medium 1640 while NCM460 in DMEM (Gibco BRL, NY, USA), supplemented with 10% fetal bovine serum (FBS), 100 mg/mL penicillin, and streptomycin. The cells were incubated at 37°C with 5% CO2. At 70% confluence, the cells were transfected using RNAiMAX (Invitrogen, USA) and specific siRNA duplexes (Additional Table 1).

Table 1

TCGA cancer abbreviations and the corresponding cancer type.
Abbreviations	Cancer Type
ACC	Adrenocortical carcinoma
BLCA	Bladder Urothelial Carcinoma
BRCA	Breast invasive carcinoma
CESC	Cervical squamous cell carcinoma and endocervical adenocarcinoma
CHOL	Cholangiocarcinoma
COAD	Colon adenocarcinoma
ESCA	Esophageal carcinoma
ESCC	Esophageal Squamous Cell Carcinoma
GBM	Glioblastoma multiforme
HNSC	Head and Neck squamous cell carcinoma
KICH	Kidney Chromophobe
KIRC	Kidney renal clear cell carcinoma
KIRP	Kidney renal papillary cell carcinoma
LAML	Acute Myeloid Leukemia
LGG	Brain Lower Grade Glioma
LUSC	Lung squamous cell carcinoma
LIHC	Liver hepatocellular carcinoma
LUAD	Lung adenocarcinoma
OV	Ovarian serous cystadenocarcinoma
PAAD	Pancreatic adenocarcinoma
PRAD	Prostate adenocarcinoma
READ	Rectum adenocarcinoma
STAD	Stomach adenocarcinoma
SARC	Sarcoma
SKCM	Skin Cutaneous Melanoma
TGCT	Testicular Germ Cell Tumors
THCA	Thyroid carcinoma
THYM	Thyroid carcinoma
UCEC	Uterine Corpus Endometrial Carcinoma

Western Blot analysis

Cells were washed three times with cold PBS and lysed with RIPA buffer containing protease inhibitors. Protein levels were determined using the Pierce BCA Protein Assay. Proteins were separated by SDS-PAGE, transferred to nitrocellulose membranes, and blocked with 5% milk. After overnight incubation with primary antibodies at 4°C, membranes were washed and incubated with HRP-conjugated secondary antibodies. Detection was conducted using ECL chemiluminescent reagents.

Real-time quantitative RT–PCR

Total RNA was isolated using the RNA-Quick Purification kit (ES Science, Cat. RN001), and DNA contamination was removed with DNase. cDNA synthesis was conducted using Roche’s kit (Cat. 0489703001). Real-time PCR was conducted on an ABI Prism 7500 using SYBR Green, with results analyzed via the 2−∆∆Ct method, normalized to GAPDH. Reactions were performed in triplicate. Primer details are presented in Additional Table 2.

Immunohistochemistry staining

Tissue sections were heated at 60°C for 1 hour, dewaxed in xylene, rehydrated through ethanol, and treated with citrate buffer (pH 6.0) for antigen retrieval. Sections were treated with 3% hydrogen peroxide for 10 minutes, incubated overnight with an anti-DTL antibody at 4°C, followed by a secondary antibody at 37°C for 30 minutes. After DAB staining for 4–5 minutes and hematoxylin counterstaining for 1 minute, sections were differentiated, washed, dehydrated, cleared, and sealed with neutral resin. TNMD positivity was assessed using Visiopharm software, which defined regions of interest, captured signals, and output TNMD density values for tumor and adjacent tissue.

Immunofluorescence staining

Cell nuclei were stained with DAPI (blue, ThermoFisher). In MSI-H COAD and READ paraffin sections, TNMD was stained red (Origene), and the NK cell marker NCAM1 was stained green (Proteintech). Sections were treated with xylene and ethanol, followed by rinsing. Antigen retrieval was performed by heating in EDTA (pH 8.0), with subsequent PBS washes. Sections were treated with 3% H2O2, blocked with BSA, and incubated overnight with primary antibodies at 4°C. After applying secondary antibody application and washing, a color flare signal reagent was added. The process was repeated for additional antibodies. Finally, DAPI was applied, sections were sealed to prevent quenching, and imaging was performed using a fluorescence microscope.

Woundhealing assay

Human colon cancer cells (LOVO), cultured in 1640 medium enriched with 10% FBS, were plated in 6-well dishes. Upon reaching near confluence, the medium was switched to a serum-free variant, and the cells were exposed to 1 µg/mL of mitomycin C for an hour. Sterile pipette tips were used to create wounds. The cells were then rinsed with PBS and maintained in a fresh, serum-free medium. Imaging was performed after incubating the cells at 37°C for 24 or 36 hours.

Cell invasion assay

Transwell chamber filters (Corning, NY, USA, Cat. 3422) were coated with Matrigel (BD, NY, USA, Cat. 356234). Following lentiviral infection, LOVO cells were resuspended in serum-free DMEM or MEM media. Subsequently, 20,000 cells in a 300 µL volume were placed in the upper chamber. These chambers were then incubated in wells filled with 500 µL of DMEM or MEM containing 10% FBS at 37°C for 18 hours. Cells adhering to the upper membrane surface were removed with cotton swabs, while those on the lower surface were stained and counted. Each membrane was assessed across four high-powered fields.

Statistical analysis

Statistical evaluations were carried out utilizing GraphPad Prism software, version 8.0. Results are presented as the mean ± SD unless otherwise. Continuous variables were analyzed using two-tailed paired t-tests under a bidirectional hypothesis. Each experiment was independently replicated three times. Statistically significant variations were marked by p < 0.05 (*), p < 0.01 (**), or p < 0.001 (***).

TNMD expression and genetic alteration analysis in pan-cancer

To elucidate the expression patterns of TNMD across various cancers, we analyzed TNMD mRNA levels in tumor tissues and adjacent normal samples, utilizing data from TCGA and the GTEx project. TNMD was significantly upregulated in several cancers, including GBM, low-LGG, COAD, READ, LIHC, PAAD, TGCT, UCS, LAML, ACC, and THYM. Conversely, it exhibited markedly lower expression in cancers, including BLCA, BRCA, CESC, CHOL, ESCA, HNSC, KICH, KIRC, KIRP, LUAD, LUSC, OV, PRAD, SKCM, STAD, THCA, and UCEC (Fig. 1a). Further investigations revealed that TNMD mRNA expression levels were highest in UCS, READ, COAD, SARC, and KIRC among all TCGA tumors (Fig. 1b). Utilizing resources from the HPA, we identified distinctive TNMD mRNA expression patterns across various cancer cell lines, with the highest levels detected in uterine cancer, testis cancer, and uncategorized cell lines (Fig. 1c). TNMD protein expression also varied significantly across tumor types, showing the most pronounced levels in CRC, lymphoma, thyroid cancer, liver cancer, and pancreatic cancer (Fig. 1d). Moreover, an analysis using the cBioPortal online tool revealed that the primary genetic alterations in TNMD across diverse TCGA cancer samples included missense mutations, amplifications, and deep deletions, with mutations being the most frequent DNA alteration observed (Fig. 1e).

TNMD shows high diagnostic efficacy and correlates with pathologic stages in multiple cancers

TNMD demonstrates substantial diagnostic efficacy across multiple malignancies. Specifically, in eight cancer types (CESC, BRCA, OSCC, KIRP, KICH, ESCC, THYM, and HNSC), TNMD’s area under the curve (AUC) spans from 0.867 to 0.981 (Figs. 2a-h). This study examined TNMD expression variations in 33 tumor types across T, N, and pathologic stages. The findings reveal significant correlation between TNMD expression and the pathologic T stage in BRCA, COAD, HNSC, and KICH (Figs. 2i-l), the pathologic N stage in BRCA, PRAD, STAD, and HNSC (Figs. 2m-p), and the overall pathologic stage in HNSC, KICH, OSCC, and COAD (Fig. 2q-t). Furthermore, TNMD expression correlates with the T stage in BLCA, KIRC, OSCC, and LUSC; the N stage in KICH, KIRC, OSCC, and LUSC; and the pathologic stage in BLCA, BRCA, KIRC, GBMLGG, PAAD, TGCT, LUSC, and THCA (Additional Fig. 1).

Higher TNMD expression correlates with improved survival outcomes in multiple cancers.

We employed the surv_cutpoint function from the survminer package to determine optimal cut-off values and evaluate TNMD’s prognostic significance across 33 tumor types in the TCGA database. TNMD expression was significantly associated with overall survival (OS) in BRCA (p = 0.02), GBM (p = 0.024), ACC (p = 0.021), READ (p = 0.001), COAD (p = 0.014), STAD (p = 0.005), KIRP (p = 0.019), KIRC (p = 0.001), and LGG (p = 0.003) (Fig. 3a-i). In READ, COAD, GBM, and KIRC, higher TNMD expression levels were correlated with improved prognosis compared to lower expression levels. In BRCA, elevated TNMD expression enhanced survival rates compared to those with lower expression during the initial 4000 days post-diagnosis; lower TNMD expression predicted superior long-term survival beyond this period. In the other tumors, lower TNMD expression consistently predicted superior survival.

TNMD interacts with key genes and influences critical pathways in pan-cancer

Using the STRING database, we identified 44 genes closely associated with TNMD and constructed a protein-protein interaction (PPI) network (Fig. 4a). The cytohubba plugin in Cytoscape identified ten central hub genes: COL1A1, COL1A2, CCN2, GAPDH, TGFβ1, MMP13, TGFβ3, DCN, TNMD, and RUNX2 (Fig. 4b). GO/KEGG enrichment analyses were performed on these TNMD-associated genes (Fig. 4c). Patients from the TCGA database were grouped by high and low TNMD expression levels and DEGs between these groups were used for hallmark enrichment analysis via GSEA. A bubble plot generated with the ggplot2 package in R visualized key findings, highlighting significant pathways: Xenobiotic Metabolism, UV Response Downregulation, Myogenesis, KRAS Signaling Upregulation, Interferon Gamma Response, G2M Checkpoint, Epithelial-Mesenchymal Transition, and Allograft Rejection (Fig. 4d).

TNMD expression associates with MSI and TMB in CRC

Our research explores TNMD’s correlation with MSI and TMB in COAD, where TNMD showed the strongest association with TMB and MSI in COAD compared to other cancers (Fig. 5a, b). An MSI score above 0.4 indicates MSI-H, while below 0.4 indicates MSS [13]. Most patients with COAD in TCGA are MSS (Fig. 5c). A correlation heatmap identified 30 genes closely linked to TNMD in MSI-H COAD (Fig. 5d), involved in UV response, TNF-alpha signaling via NF-kB, and KRAS signaling pathways, impacting the tumor immune environment (Fig. 5e). Another heatmap for patients with MSS highlighted 30 TNMD-associated genes (Fig. 5f), enriched in metabolic and hormonal pathways critical for immune cell function (Fig. 5g) [14–18]. Survival analysis revealed high TNMD expression correlated with better prognosis in MSI-H COAD, while lower expression was linked to improved outcomes in MSS (Fig. 5h, i). P-values slightly exceeded 0.05 due to small sample sizes in the MSI-H and MSS groups in TCGA. These findings underscore TNMD’s complex role in COAD, suggesting it influences tumor behavior and patient survival based on MSI status.

TNMD modulates immune cell infiltration and regulatory factors in CRC

Given the significant negative correlation between TNMD expression and MSI and TMB in COAD, we focused on this cancer type to examine TNMD’s role in modulating the immune landscape, which may impact the efficacy of emerging immunotherapies. Immune stroma scores were significantly higher in the MSI-H group than in the MSS group (Additional Fig. 2a). Immune infiltration assessment revealed substantially higher scores in the MSI-H group (Additional Fig. 2b ). Scatter plots correlated TNMD expression with increased levels of monocytes, resting NK cells, and CD4 + T cells in the MSI-High group (Fig.s 6a,b,c). In the MSS group, TNMD’s expression positively correlated with regulatory T cells (Tregs) (Fig. 6d), suggesting a poorer prognosis for patients with elevated TNMD expression (Fig. 5f). Validation using the GSE81986 dataset supported these findings, showing higher Treg levels in the TNMD-High group (p = 0.096) (Fig. 6e). This slightly higher p-value might relate to the smaller dataset size compared to TCGA. Immunofluorescence double staining on pathological sections from three patients with MSI-H CRC, targeting TNMD and the NK cell marker NCAM1. The top panel of Fig. 6f displays immunofluorescence images depicting both TNMD expression and NK cell abundance, while the lower panel reveals TNMD expression alongside reduced NK cell counts. These findings corroborate a positive correlation between TNMD expression and NK cell numbers, consistent with our initial bioinformatics predictions (Fig. 6g). The heatmap illustrates TNMD’s role in COAD immune regulation, showing significant negative correlations with key immune checkpoints (SIGLEC15, LAG3, PDCD1, CD274) and most chemokines, contrasted by positive associations with CCL20 and CXCL14 and an inverse relationship with CXCL16, along with varied interactions with other immune modulators (Fig. 6g).

Increased TNMD expression enhances chemoradiotherapy and immunotherapy outcomes in CRC.

We analyzed the relationship between TNMD expression and outcomes of chemoradiotherapy and immunotherapy in patients with COAD using the Genomics of Drug Sensitivity in Cancer database. Scatter plots revealed correlations between TNMD levels and IC50 values for various drugs: Vorinostat and VE-822 were more effective at higher TNMD levels, while Lapatinib and Bortezomib showed resistance (Fig. 7a). Further analysis detailed TNMD’s impact on drug responses. A significant positive correlation (R = 0.82, p < 0.001) was found between TNMD levels and the IC50 of Lapatinib in RECOAD cell lines, affecting the RTK pathway (Fig. 7b). Conversely, a negative correlation (R=-0.35, p = 0.02) was observed with VE-822, a DNA damage response pathway inhibitor (Fig. 7c). Radiotherapy sensitivity associated with TNMD expression was confirmed in the GSE35452 database, revealing improved outcomes at higher expression levels (p < 0.05) (Fig. 7d). Additionally, TNMD expression was examined before and after chemotherapy and immunotherapy in patients with MSS-type CRC using the GSE179351 database. Among five patients with paired data, only one with increased TNMD expression during treatment showed a partial response; the others experienced disease progression (Fig. 7e). Further analysis using TCGA data and TIDE revealed that higher TNMD expression correlated with better immunotherapy responses (Fig. 7f). In patients with MSS-type CRC, those with elevated TNMD expression had higher Merck18 scores, indicating effective immunotherapy responses (Fig. 7g). Validation through TIDE analysis in the GSE81986 database supported that patients with higher TNMD levels are more likely to benefit from immunotherapy (Additional Fig. 2c, d). These findings suggest that increased TNMD expression in patients with MSS-type CRC potentially enhances outcomes in both chemoradiotherapy and immunotherapy.

TNMD influences immune cell interactions in the CRC microenvironment

To explore the impact of TNMD on the cellular landscape of the CRC immune microenvironment, we performed single-cell RNA sequencing (scRNA-seq) on 23 tissue samples from patients with CRC. Batch effects across individual patients were corrected using the Harmony algorithm (Fig.s 8a, b). Post-correction, we identified 24 cell clusters (Additional Fig. 3a.) and annotated eight cell types SingleR package: epithelial cells, monocytes, B cells, CD4 + T cells, macrophages, CD8 + T cells, fibroblasts, and endothelial cells (Fig. 8c). Marker gene expression patterns validated these classifications (Fig. 8d). The robustness of these annotations was verified by analyzing upregulated genes and unique biological features specific to each type (Fig. 8d), revealing that TNMD expression is predominantly in epithelial cells (Fig. 8e). GO analysis linked TNMD’s roles in tumor cell invasion, metastasis, and inflammatory responses, particularly in immune response and inflammation regulation (Fig. 8f). KEGG analysis highlighted pathways affected by Human T-cell leukemia virus type 1 infection, emphasizing its impact on T-cell functionality and tumor immune evasion, particularly in steroid, lipid, and pyruvate metabolism (Fig. 8g). Comparing TNMD-positive and TNMD-negative tumor subpopulations showed stronger interactions between TNMD-positive tumor cells with fibroblasts and macrophages, mediated by distinct ligand-receptor pairs (Fig. 8h, i; Additional Fig. 3a). These findings underscore TNMD’s role in shaping the CRC immune microenvironment, highlighting its potential as a therapeutic target for modulating tumor immunity.

TNMD promotes LOVO cell migration and invasion in vitro and clinical validation

To validate the bioinformatics findings on TNMD expression, we conducted Western blot analyses on tumor and adjacent normal tissues from seven patients with CRC. Our findings revealed significantly higher TNMD levels in the tumor tissues (Fig. 9a). Further analyses of normal colon mucosal cells (NCM460) and three colon cancer cell lines (LOVO, HCT116, HT29) showed elevated TNMD levels, particularly in LOVO cells (Fig. 9b). Immunohistochemical staining of pathological slides from 23 patients with CRC confirmed higher TNMD expression in tumor tissues (Figs. 9c, d). To explore TNMD’s potential role in promoting tumor metastasis, as indicated by bioinformatic analysis, we conducted wound healing and Transwell invasion assays using LOVO cells. The expression of epithelial-mesenchymal transition (EMT)-related markers in LOVO cells was evaluated using Western blot analysis after Si-TNMD treatment. The wound healing assay demonstrated that TNMD knockdown significantly impaired LOVO cell migration (Figs. 9e, f), while the Transwell invasion assay confirmed a reduced invasion capacity following TNMD depletion (Figs. 9g, h). We validated TNMD knockout efficiency at both the mRNA and protein levels (Fig. 9i, j). Subsequent Western blot analyses post-knockdown revealed increased E-cadherin and decreased N-cadherin and Vimentin, consistent with EMT reversal (Fig. 9j). These results suggest that TNMD knockdown effectively inhibits migration and invasion in LOVO colon cancer cells.

Previous research has highlighted TNMD’s role in chondrocyte proliferation and endothelial cell formation, but its function in pan-cancers, especially CRC, remains under-explored [19, 20]. This study utilizes TCGA and GTEx databases to analyze TNMD across 33 cancer types, covering gene expression, diagnostic significance, survival outcomes, genetic variations, gene enrichment pathways, and immune interactions through infiltration assessments and single-cell analyses. It also investigates TNMD’s influence on CRC treatment responses to radiotherapy, chemotherapy, and immunotherapy in MSS and MSI-H subtypes. Experimental verification confirms that TNMD knockdown significantly reduces invasion and metastasis in the COAD cell line LOVO, indicating its potential as a therapeutic target.

The initial phase of the study examined TNMD mRNA and protein levels across various cancer tissues and cell lines, revealing significant expression differences between tumor and adjacent normal tissues and among genetic mutations across cancers. These differences arise from TNMD’s unique biological properties and molecular variants affecting gene expression patterns [21]. For instance, the Tp53 gene could promote or suppress tumor growth depending on the cancer variant [22, 23]. Additionally, genetic mutations and genomic rearrangements in tumors can alter TNMD expression, leading to its overexpression or absence in certain cancers [24, 25].

The study indicates TNMD’s substantial diagnostic value across various cancers, with expression levels significantly correlating with pathological stages and OS rates, positioning it as an independent prognostic marker and a potential treatment target. So far, only Aozheng Chen’s research has identified TNMD’s prognostic significance in cervical cancer, with limited data on its role in other cancers [8]. This gap highlights the need for further research into TNMD’s specific functions across cancers. Moreover, the study questions why significant disparities in TNMD expression between cancerous and normal tissues do not correlate with survival times, suggesting that patients’ survival is affected by genetic heterogeneity, treatment variations, and the interplay of genetics, physiological state, and tumor microenvironment [26–28].

This study enhances our comprehension of TNMD by constructing a PPI network, identifying ten central hub genes, and creating a pan-cancer hallmark pathway enrichment analysis heatmap linked to TNMD. The association between TNMD expression, hub genes, and hallmark pathways highlights their joint impact on cancer. Abnormal expression of COL1A1 and COL1A2 contributes to breast and lung cancer, while CCN2, DCN, and MMP13 are critical for extracellular matrix dynamics, cell growth, migration, tissue remodeling, and cellular signaling in fibroblasts [29–33]. Notably, pan-cancer analysis reveals significant associations between TNMD and the INTERFERON GAMMA RESPONSE and ALLOGRAFT REJECTION pathways. These pathways are crucial for tumor immunity, involving various immune cells and promoting anti-tumor activities, highlighting the intricate relationship between tumors and host immune responses [34, 35].

Our research indicates a strong link between TNMD expression and both TMB and MSI in COAD, prompting further exploration into TNMD’s role. In MSI-H CRC, TNMD significantly correlates with ACE2 and CTTNBP2, genes crucial for modulating tumor immunity and evasion strategies during immune checkpoint therapies [36]. For MSS CRC, IL-12A, vital for immune activation and currently explored as a therapeutic agent, is significant [37], suggesting elevated TNMD expression may enhance immunotherapy efficacy. Our study contrasts with existing literature by providing a nuanced understanding of how TNMD’s expression correlates with immune response modifiers in different CRC subtypes. A strong negative correlation with PDCD1 indicates that higher TNMD expression potentially mitigates T-cell immunosuppression, enhancing immune checkpoint inhibitor effectiveness. This finding significantly deviates from prior studies that have not linked TNMD expression with specific immunoregulatory pathways in CRC subtypes [38]. A positive correlation between TNMD and CCL20 suggests that high TNMD expression may enhance immune cell infiltration, benefiting cytotoxic T-cell-mediated therapies. This finding enriches our understanding of the immune landscape in CRC that previous studies may have overlooked [39]. Moreover, a negative correlation with TGFB1 implies that elevated TNMD expression could weaken TGFβ1-mediated immunosuppression, activating stronger anti-tumor immune responses, a perspective not extensively covered in existing research. These insights suggest new therapeutic avenues based on TNMD expression levels in CRC [40].

Our research demonstrates a significant inverse relationship between TNMD expression in RECOAD cell lines and the IC50 for VE-822, an ATR kinase inhibitor that disrupts the ATR-mediated cell cycle checkpoint pathway. This inhibitor is particularly relevant for cancers reliant on DNA repair mechanisms, suggesting its utility in enhancing chemotherapy and radiotherapy efficacy [41, 42]. Patients with CRC and higher radiosensitivity show increased TNMD levels (Fig. 7c, 7d). Additionally, our study explores immune therapy outcomes beyond traditional DNA damage responses. Using the TIDE method and the“Merck1” feature set, we analyzed immune responses among patients with MSS-type CRC from the TCGA database, correlating higher TNMD expression with better responses to immune checkpoint inhibitors (Fig. 7f, 7g) [43]. This highlights TNMD’s potential as a biomarker in predicting immune therapy efficacy in MSS-type COAD. Our findings suggest that patients with higher TNMD expression are more likely to benefit from immune therapies, diverging from studies focused solely on cancer cell sensitivity to DNA damage. A parallel analysis of the GSE81986 dataset corroborated these findings (Additional Fig. 2). However, the limited sample size in TCGA and GSE81986 databases affected statistical significance, prompting further validation of these preliminary findings.

We utilized single-cell sequencing data from the GSE132465 dataset, including samples from patients with CRC, to examine tumor immunity at the cellular level. The functional enrichment analysis (Fig. 8d) reveals immune-related functions, including B cell- and lymphocyte-mediated immunity, highlighting TNMD’s involvement in enhancing tumor immunotherapy responses. A significant innovation of this study is the application of single-cell sequencing to reveal distinct cellular interactions and pathways in tumor immunity. The GO analysis radar chart emphasizes CXCR chemokine receptor binding’s role in leukocyte migration and activation, which is critical for immune surveillance and tumor eradication [44]. Additionally, the analysis highlights the influence of endopeptidase inhibitors on immune cell targeting within the tumor microenvironment, reshaping the immune landscape against tumor cells [45]. Our KEGG analysis also explores the detrimental effects of Human T-cell leukemia virus 1 infection on T-cell functionality, which is essential for understanding tumor immune evasion mechanisms [46]. Interactions between TNMD-positive tumor subpopulations, fibroblasts, and macrophages are primarily mediated by specific ligand-receptor pairs. These interactions are crucial for tumor progression and involve pathways such as JAG1-Notch1 and PDGF, affecting tumor survival, proliferation, angiogenesis, and immune cell infiltration [47–49]. Our single-cell analysis offers new perspectives into the relationship between TNMD expression and tumor immunity in CRC, emphasizing the importance of these molecular details for further experimental and therapeutic explorations.

In our experiments, TNMD was significantly overexpressed in CRC tissues compared to adjacent non-tumor tissues, validated by Western blotting and immunohistochemistry. In vitro, TNMD knockdown in LOVO cells impaired migration and invasion, highlighting its role in COAD metastasis. These results validate computational predictions and demonstrate the reliability of these methodologies in identifying therapeutic targets. This study revealed complex roles for TNMD in CRC. High TNMD expression in cancerous tissues, coupled with reduced invasion and metastasis following TNMD knockdown, suggests a pro-oncogenic role. However, its correlation with better prognosis in COAD and READ cases may imply a tumor-suppressive role. These contradictions likely reflect TNMD’s complex roles in different biological contexts. Its high expression might represent an adaptive response under specific conditions, potentially involving immune regulation or other mechanisms that promote or inhibit tumor growth and spread. In MSS-type COAD, lower TNMD expression correlates with better prognoses but is positively associated with Tregs, indicating immune escape. Further analysis indicates high TNMD expression correlates with higher TIDE scores and better responses to immune therapy, suggesting a dual function in modulating immune responses—maintaining immunosuppression and enhancing immune activity. TNMD’s role in CRC varies across tumor microenvironments and genetic backgrounds, highlighting the dynamic nature of biomarkers presenting challenges and opportunities for precision medicine. Future studies will explore how TNMD specifically influences tumor behavior and immune responses.

Although this study offers significant insights into the role of TNMD across various cancers and its association with the responsiveness of CRC patients to radiotherapy and immunotherapy, several limitations are acknowledged. It does not investigate the specific mechanisms by which TNMD influences immunotherapy outcomes in CRC. These gaps underscore the necessity for future research to conduct comprehensive molecular investigations, utilize in vitro animal models, and engage in clinical trials to corroborate these preliminary findings.

This study highlights TNMD’s pivotal role in tumor biology and therapeutic response. Analysis of TNMD mRNA and protein expression in multiple tumors and cell lines highlighted its diagnostic potential, correlation with pathological stages, and impact on OS. The interaction between TNMD with protein networks and cancer pathways was thoroughly explored. In CRC, the study linked TNMD expression to responses to radiotherapy, chemotherapy, and immunotherapy, emphasizing its impact on treatment outcomes. Single-cell sequencing further revealed how TNMD shapes the tumor microenvironment, influencing immune cell infiltration and anti-tumor immunity. Experimental validation confirmed TNMD’s overexpression in tumors and CRC cells and its role in promoting invasion and metastasis, positioning it as a potential therapeutic target.

AUC

area under the curve

COAD

colorectal adenocarcinoma

CRC

colorectal cancer

DEGs

differentially expressed genes

FBS

fetal bovine serum

HPA

Human Protein Atlas

READ

rectal adenocarcinoma

GEO

Gene Expression Omnibus

Gene Ontology

GSEA

Gene Set Enrichment Analysis

GTEx

Genotype-Tissue Expression

KEGG

Kyoto Encyclopedia of Genes and Genomes

MSS

microsatellite stability

MSI

microsatellite instability

MSI-H

microsatellite instability-high

overall survival

PPI

protein-protein interaction

ROC

receiver operating characteristic

TNMD

Tenomodulin

TCGA

The Cancer Genome Atlas

TMB

tumor mutational

Ethics approval and consent to participate

The authors affirm that all procedures followed the Declaration of Helsinki's guidelines and received approval from the Institutional Review Committee and the Medical Ethics Committee of the Fifth Affiliated Hospital of Wenzhou Medical University. All participants provided their written consent.

Consent for publication

Not applicable

Availability of data and materials

The data analyzed in this study can be downloaded from the GEO (GSE81986, GSE35452, GSE179351, and GSE81986) and TCGA (https://portal.gdc.cancer.gov/) and GTEx (https://www.gtexportal.org/home/).

Competing interests

The authors declare no competing interests.

Funding

This work was supported by the Lingyan Project of Zhejiang Province (grant numbers 2023C03062).

Author contributions

C.J.F. and J.J.S. designed the study and wrote the manuscript. C.J.F. and Y.Y. performed most of the experiments and analyzed the data. W.J.P., H.Q., Z.C., and W.Y.F. performed part of the experiments. C.B. and Z.L.L. collected the clinical samples. W.B. and G.B.Y. analyzed and interpreted the data. All authors read and approved the final manuscript.

Acknowledgments

We thank our colleagues from Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology and Anorectal Surgery of The Fifth Affiliated Hospital of Wenzhou Medical University who provided insight and expertise that greatly assisted the research, although they may not agree with all of the interpretations/conclusions of this paper. We would like to thank Editage (www.editage.cn) for English language editing.

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TNMD in pan-cancer analysis: Exploring its impact on immune modulation and uncovering functional insights in colorectal cancer

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Data collection, gene expression analysis, and genetic alteration analysis

ROC analysis, pathological stage difference, and survival analysis

PPI network analyses and gene function enrichment analysis

GSEA

Single-cell sequencing data download, processing, and cell-cell communication analysis

TIDE and Merck18 scores

Cell culture and transfection

Western Blot analysis

Real-time quantitative RT–PCR

Immunohistochemistry staining

Immunofluorescence staining

Woundhealing assay

Cell invasion assay

Statistical analysis

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1