Transmembrane protein 106C promotes the development of hepatocellular carcinoma

Unveiling expression patterns, mechanisms, and therapeutic opportunities of transmembrane protein 106C: From pan-cancers to hepatocellular carcinoma

BACKGROUND

Although transmembrane protein 106C (TMEM106C) has been elucidated to be overexpressed in cancers, its underlying mechanisms have not yet been fully understood.

AIM

To investigate the expression levels and molecular mechanisms of TMEM106C across 34 different cancer types, including liver hepatocellular carcinoma (LIHC).

METHODS

We analyzed TMEM106C expression patterns in pan-cancers using microenvironment cell populations counter to evaluate its association with the tumor microenvironment. Gene set enrichment analysis was conducted to identify molecular pathways related to TMEM106C. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) analysis was conducted to identify upstream transcriptional regulators of TMEM106C. In LIHC, we examined mRNA profiles, performed in-house quantitative polymerase chain reaction, immunohistochemistry, and constructed a co-expression gene network. Functional assays, including cell counting kit-8, cell cycle, apoptosis, migration, and invasion, were conducted. The effect of nitidine chloride (NC) on LIHC xenograft was evaluated through RNA sequencing and molecular docking. Finally, potential therapeutic agents targeting TMEM106C were predicted.

RESULTS

TMEM106C was significantly overexpressed in 27 different cancer types and presaged poor prognosis in four of these types, including LIHC. Across pan-cancers, TMEM106C was inversely correlated to the abundances of immune and stromal cells. Furthermore, TMEM106C was significantly linked to cell cycle and DNA replication pathways in pan-cancers. ChIP-seq analysis predicted CCCTC-binding factor as a pivotal transcriptional factor targeting the TMEM106C gene in pan-cancers. Integrated analysis showed that TMEM106C was upregulated in 4657 LIHC compared with 3652 normal liver tissue [combined standardized mean difference = 1.31 (1.09, 1.52)]. In-house LIHC samples verified the expression status of TMEM106C. Higher TMEM106C expression signified worse survival conditions in LIHC patients treated with sorafenib, a tyrosine kinase inhibitor (TKI). Co-expressed analysis revealed that TMEM106C were significantly enriched in the cell cycle pathway. Knockout experiments demonstrated that TMEM106C plays a crucial role in LIHC cell proliferation, migration, and invasion, with cell cycle arrest occurring at the DNA synthesis phase, and increased apoptosis. Notably, TMEM106C upregulation was attenuated by NC treatment. Finally, TMEM106C expression levels were significantly correlated with the drug sensitivity of anti-hepatocellular carcinoma agents, including JNJ-42756493, a TKI agent.

CONCLUSION

Overexpressed TMEM106C was predicted as an oncogene in pan-cancers, which may serve as a promising therapeutic target for various cancers, including LIHC. Targeting TMEM106C could potentially offer a novel direction in overcoming TKI resistance specifically in LIHC. Future research directions include in-depth experimental validation and exploration of TMEM106C's role in other cancer types.

Lifting the veils on transmembrane proteins: Potential anticancer targets

Cancer, as a prevalent cause of mortality, poses a substantial global health burden and hinders efforts to enhance life expectancy. Nevertheless, the prognosis of patients with malignant tumors remains discouraging, owing to the lack of specific diagnostic and therapeutic targets. Therefore, the development of early diagnostic indicators and novel therapeutic drugs for the prevention and treatment of cancer is essential. Transmembrane proteins (TMEMs) are a class of proteins that can span the phospholipid bilayer and are stably anchored. They are associated with fibrotic diseases, neurodegenerative diseases, autoimmune diseases, developmental disorders, and cancer. It has been found that the expression levels of TMEMs were elevated or reduced in cancer cells, exerting pro/anticancer effects. These aberrant expression levels have also been linked to the prognostic and clinicopathological features of diverse tumors. In this review, the structures, functions, and roles of TMEMs in cancer were discussed, and the scientific perspectives were described. This review also explored the potential of TMEMs as tumor drug candidates from the perspective of targeted therapies, and the challenges that need to be overcome in a wide range of preclinical and clinical anticancer research were summarized.

Characterization of the Clinical Significance and Immunological Landscapes of a Novel TMEMs Signature in Hepatocellular Carcinoma and the Contribution of TMEM201 to Hepatocarcinogenesis

Aberrant transmembrane protein (TMEM) expression is implicated in tumor progression, but its functional role in hepatocellular carcinoma (HCC) is unclear. Thus, we aim to characterize the functional contributions of TMEM in HCC. In this study, four novel TMEM-family genes (TMEMs), TMEM106C, TMEM201, TMEM164, and TMEM45A, were screened to create a TMEMs signature. These candidate genes are distinguished between patients with varying survival statuses. High-risk HCC patients had a significantly worse prognosis and more advanced clinicopathological characteristics in both the training and validation groups. The GO and KEGG analyses unveiled that the TMEMs signature might play a crucial role in cell-cycle-relevant and immune-related pathways. We found that the high-risk patients had lower stromal scores and a more immunosuppressive tumor microenvironment with massive infiltration of macrophages and Treg cells, whereas the low-risk group had higher stromal scores and gamma delta T-cell infiltration. Moreover, the expression level of suppressive immune checkpoints increased as the TMEM-signature scores increased. Furthermore, the in vitro experiments validated TMEM201, one feature of the TMEMs signature, and facilitated HCC proliferation, survival, and migration. The TMEMs signature provided a more precise prognostic evaluation of HCC and reflected the immunological status of HCC. Of the TMEMs signature studied, TMEM201 was found to significantly promote HCC progression.

Seven Hub Genes Predict the Prognosis of Hepatocellular Carcinoma and the Corresponding Competitive Endogenous RNA Network

Purpose

This study was aimed at identifying hub genes and ceRNA regulatory networks linked to prognosis in hepatocellular carcinoma (HCC) and to identify possible therapeutic targets.

Methods

Differential expression analyses were performed to detect the differentially expressed genes (DEGs) in the four datasets (GSE76427, GSE6764, GSE62232, and TCGA). The intersected DEmRNAs were identified to explore biological significance by enrichment analysis. We built a competitive endogenous RNA (ceRNA) network of lncRNA-miRNA-mRNA. The mRNAs of the ceRNA network were used to perform Cox and Kaplan-Meier analyses to obtain prognosis-related genes, followed by the selection of genes with an area under the curve >0.8 to generate the random survival forest model and obtain feature genes. Furthermore, the feature genes were subjected to least absolute shrinkage and selection operator (LASSO) and univariate Cox analyses were used to identify the hub genes. Finally, the infiltration status of immune cells in the HCC samples was determined.

Results

A total of 1923 intersected DEmRNAs were identified in four datasets and involved in cell cycle and carbon metabolism. ceRNA network was created using 10 lncRNAs, 67 miRNAs, and 1,923 mRNAs. LASSO regression model was performed to identify seven hub genes, SOCS2, MYOM2, FTCD, ADAMTSL2, TMEM106C, LARS, and KPNA2. Among them, TMEM106C, LARS, and KPNA2 had a poor prognosis. KPNA2 was considered a key gene base on LASSO and Cox analyses and involved in the ceRNA network. T helper 2 cells and T helper cells showed a higher degree of infiltration in HCC.

Conclusion

The findings revealed seven hub genes implicated in HCC prognosis and immune infiltration. A corresponding ceRNA network may help reveal their potential regulatory mechanism.

Identification and Validation of a Novel Prognostic Gene Model for Colorectal Cancer

Aims

Colon cancer (CRC), with high morbidity and mortality, is a common and highly malignant cancer, which always has a bad prognosis. So it is urgent to employ a reasonable manner to assess the prognosis of patients. We developed and validated a gene model for predicting CRC risk.

Methods

The Gene Expression Omnibus (GEO) database was used to extract the gene expression profiles of CRC patients (N = 181) from GEO to identify genes that were differentially expressed between CRC patients and controls and then stable signature genes by firstly using both robust likelihood-based modeling with 1000 iterations and random survival forest variable hunting algorithms. Cluster analysis using the longest distance method was drawn out, and Kaplan–Meier (KM) survival analysis was used to compare the clusters. Meanwhile, the risk score was evaluated in three independent datasets including the GEO and Illumina HiSeq sequencing platforms. The corresponding risk index was calculated, and samples were clustered into high- and low-risk groups according to the median. And survival ROC analysis was used to evaluate the prognostic model. Finally, the Gene Set Enrichment Analysis (GSEA) was performed for further functional enrichment analyses.

Results

A 10-gene model was obtained, including 7 negative impact factors (SLC39A14, AACS, ERP29, LAMP3, TMEM106C, TMED2, and SLC25A3) and 3 positive ones (CNPY2, GRB10, and PBK), which related with several important oncogenic pathways (KRAS signaling, TNF-α signaling pathway, and WNT signaling pathway) and several cancer-related cellular processes (epithelial mesenchymal transition and cellular apoptosis). By using colon cancer datasets from The Cancer Genome Atlas (TCGA), the model was validated in KM survival analysis (P ≤ 0.001) and significant analysis with recurrence time (P = 0.0018).

Conclusions

This study firstly developed a stable and effective 10-gene model by using novel combined methods, and CRC patients might be able to use it as a prognostic marker for predicting their survival and monitoring their long-term treatment.

LINC00238 inhibits hepatic carcinoma progression by activating TMEM106C‑mediated apoptosis pathway

The present study aimed to explore the regulatory mechanism of long intergenic non-protein coding (LINC)00238 in hepatocellular carcinoma (HCC). LINC00238 expression in HCC tissues and cell lines was measured using reverse transcription-quantitative PCR. LncTar was used to predict the binding sites between LINC00238 and transmembrane protein 106C (TMEM106C). Survival analysis of LINC00238, TMEM106C and activating transcription factor 3 (ATF3) in patients with HCC was performed based on TCGA data. The proliferation, apoptosis, migration, and invasion of HCC cells were measured by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium assay, flow cytometer, wound healing and Transwell assays, respectively. LINC00238 promoted apoptosis and inhibited proliferation, migration and invasion of HCC cells. LINC00238 was downregulated in HCC. TMEM106C was a target of LINC00238 and TMEM106C expression was negatively regulated by LINC00238. TMEM106C suppressed the apoptosis pathway and decreased the expression of caspase-7, tissue inhibitor of metalloproteinase 2, programmed cell death 4 and ATF3. Notably, ATF3 was the upstream promoter of LINC00238 and positively regulated LINC00238 expression. In conclusion, LINC00238 inhibited HCC progression by inhibiting TMEM106 expression and activating the TMEM106C-mediated apoptosis pathway.

Prognostic Value of a Novel Signature With Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases

Background

Hepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer.

Methods

Differentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines.

Results

A total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P < 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2.

Conclusion

This study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.