C1orf106, an innate immunity activator, is amplified in breast cancer and is required for basal-like/luminal progenitor fate decision

C1orf106 (INAVA) Is a SMAD3-Dependent TGF-β Target Gene That Promotes Clonogenicity and Correlates with Poor Prognosis in Breast Cancer

Transforming Growth Factor-β (TGF-β) can have both tumour-promoting and tumour-suppressing activity in breast cancer. Elucidating the key downstream mediators of pro-tumorigenic TGF-β signalling in this context could potentially give rise to new therapeutic opportunities and/or identify biomarkers for anti-TGF-β directed therapy. Here, we identify C1orf106 (also known as innate immunity activator INAVA) as a novel TGF-β target gene which is induced in a SMAD3-dependent but SMAD2/SMAD4-independent manner in human and murine cell lines. C1orf106 expression positively correlates with tumourigenic or metastatic potential in human and murine breast cancer cell line models, respectively, and is required for enhanced migration and invasion in response to TGF-β stimulation. C1orf106 promoted self-renewal and colony formation in vitro and may promote tumour-initiating frequency in vivo. High C1orf106 mRNA expression correlates with markers of aggressiveness and poor prognosis in human breast cancer. Taken together, our findings indicate that C1orf106 may act as a tumour promoter in breast cancer.

Targeting MYO1B impairs tumorigenesis via inhibiting the SNAI2/cyclin D1 signaling in esophageal squamous cell carcinoma

Myosin‐related proteins play an important role in cancer progression. However, the clinical significance, biological functions, and mechanisms of myosin 1B (MYO1B), in esophageal squamous cell carcinoma (ESCC) remain unclear. The clinical relevance of MYO1B, SNAI2, and cyclin D1 in ESCC was determined by immunohistochemistry, Oncomine, and GEPIA databases. The oncogenic roles of MYO1B were determined by CCK8, colony formation assays, wound healing, and Transwell assay. MYO1B, SNAI2, and cyclin D1 at mRNA and protein levels in ESCC cells were detected by qPCR and Western blot analysis. In our study, we found that MYO1B expression was increased in ESCC tissue samples and correlated with tumor stage, TNM stage, and poor outcomes. Functional assays indicated that depletion of MYO1B impaired oncogenesis, and enhanced chemosensitivity in ESCC. Bioinformatic analysis and mechanistic studies illustrated that SNAI2 was a key downstream effector of MYO1B. Suppression of MYO1B downregulated expression of SNAI2, thereby inhibiting the SNAI2/cyclin D1 pathway. Furthermore, a selective inhibitor of cyclin D1 activation reversed siMYO1B cells overexpressing SNAI2‐elicited aggressive phenotypes of ESCC cells. MYO1B positively correlated with SNAI2 and cyclin D1 in ESCC samples, and higher SNAI2 expression was also associated with poor prognosis in ESCC patients. Our finding demonstrated that MYO1B activates the SNAI2/cyclin D1 pathway to drive tumorigenesis and cisplatin cytotoxicity in ESCC, indicating that MYO1B is a potential therapeutic target for patients with ESCC.

Targeting MYO1B impairs tumorigenesis via inhibiting the SNAI2/cyclin D1 signaling in esophageal squamous cell carcinoma

Myosin-related proteins play an important role in cancer progression. However, the clinical significance, biological functions, and mechanisms of myosin 1B (MYO1B), in esophageal squamous cell carcinoma (ESCC) remain unclear. The clinical relevance of MYO1B, SNAI2, and cyclin D1 in ESCC was determined by immunohistochemistry, Oncomine, and GEPIA databases. The oncogenic roles of MYO1B were determined by CCK8, colony formation assays, wound healing, and Transwell assay. MYO1B, SNAI2, and cyclin D1 at mRNA and protein levels in ESCC cells were detected by qPCR and Western blot analysis. In our study, we found that MYO1B expression was increased in ESCC tissue samples and correlated with tumor stage, TNM stage, and poor outcomes. Functional assays indicated that depletion of MYO1B impaired oncogenesis, and enhanced chemosensitivity in ESCC. Bioinformatic analysis and mechanistic studies illustrated that SNAI2 was a key downstream effector of MYO1B. Suppression of MYO1B downregulated expression of SNAI2, thereby inhibiting the SNAI2/cyclin D1 pathway. Furthermore, a selective inhibitor of cyclin D1 activation reversed siMYO1B cells overexpressing SNAI2-elicited aggressive phenotypes of ESCC cells. MYO1B positively correlated with SNAI2 and cyclin D1 in ESCC samples, and higher SNAI2 expression was also associated with poor prognosis in ESCC patients. Our finding demonstrated that MYO1B activates the SNAI2/cyclin D1 pathway to drive tumorigenesis and cisplatin cytotoxicity in ESCC, indicating that MYO1B is a potential therapeutic target for patients with ESCC.

Identification and Validation of the Diagnostic Characteristic Genes of Ovarian Cancer by Bioinformatics and Machine Learning

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning.

Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level.

Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis.

Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Background

Dermatomyositis is an autoimmune disease characterized by damage to the skin and muscles. CD4⁺ T cells are of crucial importance in the occurrence and development of dermatomyositis (DM). However, there are few bioinformatics studies on potential pathogenic genes and immune cell infiltration of DM. Therefore, this study intended to explore CD4⁺ T-cell infiltration–associated key genes in DM and construct a new model to predict the level of CD4⁺ T-cell infiltration in DM.

Methods

GSE46239, GSE142807, GSE1551, and GSE193276 datasets were downloaded. The WGCNA and CIBERSORT algorithms were performed to identify the most correlated gene module with CD4⁺ T cells. Matascape was used for GO enrichment and KEGG pathway analysis of the key gene module. LASSO regression analysis was used to identify the key genes and construct the prediction model. The correlation between the key genes and CD4⁺ T-cell infiltration was investigated. GSEA was performed to research the underlying signaling pathways of the key genes. The key gene-correlated transcription factors were identified through the RcisTarget and Gene-motif rankings databases. The miRcode and DIANA-LncBase databases were used to build the lncRNA-miRNA-mRNA network.

Results

In the brown module, 5 key genes (chromosome 1 open reading frame 106 (C1orf106), component of oligomeric Golgi complex 8 (COG8), envoplakin (EVPL), GTPases of immunity-associated protein family member 6 (GIMAP6), and interferon-alpha inducible protein 6 (IFI6)) highly associated with CD4⁺ T-cell infiltration were identified. The prediction model was constructed and showed better predictive performance in the training set, and this satisfactory model performance was validated in another skin biopsy dataset and a muscle biopsy dataset. The expression levels of the key genes promoted the CD4⁺ T-cell infiltration. GSEA results revealed that the key genes were remarkably enriched in many immunity-associated pathways, such as JAK/STAT signaling pathway. The cisbp_M2205, transcription factor-binding site, was enriched in C1orf106, EVPL, and IF16. Finally, 3,835 lncRNAs and 52 miRNAs significantly correlated with key genes were used to build a ceRNA network.

Conclusion

The C1orf106, COG8, EVPL, GIMAP6, and IFI6 genes are associated with CD4⁺ T-cell infiltration. The prediction model constructed based on the 5 key genes may better predict the level of CD4⁺ T-cell infiltration in damaged muscle and lesional skin of DM. These key genes could be recognized as potential biomarkers and immunotherapeutic targets of DM.

Meta-analysis based gene expression profiling reveals functional genes in ovarian cancer

Background: Ovarian cancer causes high mortality rate worldwide, and despite numerous attempts, the outcome for patients with ovarian cancer are still not well improved. Microarray-based gene expressional analysis provides with valuable information for discriminating functional genes in ovarian cancer development and progression. However, due to the differences in experimental design, the results varied significantly across individual datasets.

Methods: In the present study, the data of gene expression in ovarian cancer were downloaded from Gene Expression Omnibus (GEO) and 16 studies were included. A meta-analysis based gene expression analysis was performed to identify differentially expressed genes (DEGs). The most differentially expressed genes in our meta-analysis were selected for gene expression and gene function validation.

Results: A total of 972 DEGs with P-value < 0.001 were identified in ovarian cancer, including 541 up-regulated genes and 431 down-regulated genes, among which 92 additional DEGs were found as gained DEGs. Top five up- and down-regulated genes were selected for the validation of gene expression profiling. Among these genes, up-regulated CD24 molecule (CD24), SRY (sex determining region Y)-box transcription factor 17 (SOX17), WFDC2, epithelial cell adhesion molecule (EPCAM), innate immunity activator (INAVA), and down-regulated aldehyde oxidase 1 (AOX1) were revealed to be with consistent expressional patterns in clinical patient samples of ovarian cancer. Gene functional analysis demonstrated that up-regulated WFDC2 and INAVA promoted ovarian cancer cell migration, WFDC2 enhanced cell proliferation, while down-regulated AOX1 was functional in inducing cell apoptosis of ovarian cancer.

Conclusion: Our study shed light on the molecular mechanisms underlying the development of ovarian cancer, and facilitated the understanding of novel diagnostic and therapeutic targets in ovarian cancer.

Differential Expression of a Panel of Ten CNTN1-Associated Genes during Prostate Cancer Progression and the Predictive Properties of the Panel Towards Prostate Cancer Relapse

Contactin 1 (CNTN1) is a new oncogenic protein of prostate cancer (PC); its impact on PC remains incompletely understood. We observed CNTN1 upregulation in LNCaP cell-derived castration-resistant PCs (CRPC) and CNTN1-mediated enhancement of LNCaP cell proliferation. CNTN1 overexpression in LNCaP cells resulted in enrichment of the CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_3 gene set that facilitates endocrine resistance in breast cancer. The leading-edge (LE) genes (n = 10) of this enrichment consist of four genes with limited knowledge on PC and six genes novel to PC. These LE genes display differential expression during PC initiation, metastatic progression, and CRPC development, and they predict PC relapse following curative therapies at hazard ratio (HR) 2.72, 95% confidence interval (CI) 1.96–3.77, and p = 1.77 × 10⁻⁹ in The Cancer Genome Atlas (TCGA) PanCancer cohort (n = 492) and HR 2.72, 95% CI 1.84–4.01, and p = 4.99 × 10⁻⁷ in Memorial Sloan Kettering Cancer Center (MSKCC) cohort (n = 140). The LE gene panel classifies high-, moderate-, and low-risk of PC relapse in both cohorts. Additionally, the gene panel robustly predicts poor overall survival in clear cell renal cell carcinoma (ccRCC, p = 1.13 × 10⁻¹¹), consistent with ccRCC and PC both being urogenital cancers. Collectively, we report multiple CNTN1-related genes relevant to PC and their biomarker values in predicting PC relapse.

Triple-negative breast cancer: new treatment strategies in the era of precision medicine

Triple-negative breast cancer (TNBC) remains the most aggressive cluster of all breast cancers, which is due to its rapid progression, high probabilities of early recurrence, and distant metastasis resistant to standard treatment. Following the advances in cancer genomics and transcriptomics that can illustrate the comprehensive profiling of this heterogeneous disease, it is now possible to identify different subclasses of TNBC according to both intrinsic signals and extrinsic microenvironment, which have a huge influence on predicting response to established therapies and picking up novel therapeutic targets for each cluster. In this review, we summarize basic characteristics and critical subtyping systems of TNBC, and particularly discuss newly found prospective targets and relevant medications, which were proved promising in clinical trials, thus shedding light on the future development of precision treatment strategies.

CBP-mediated Slug acetylation stabilizes Slug and promotes EMT and migration of breast cancer cells

Slug, a member of the Snail family of transcriptional repressors, plays a key role in cancer progression, cellular plasticity, and epithelial to mesenchymal transition (EMT). Slug is a fast-turnover protein and its stability is controlled by post-translational modifications. Here, we identified that Slug is acetylated by acetyltransferase CREB-binding protein (CBP) in breast cancer cells. CBP directly interacts with the C-terminal domain of Slug through its catalytic histone acetyltransferase (HAT) domain, leading to acetylation of Slug at lysines 166 and 211. Analysis with acetylation-specific antibodies revealed that Slug is highly acetylated in metastatic breast cancer cells. Notably, Slug acetylation, mediated by CBP at lysines 166 and 211, doubles its half-life and increases its stability. Further, acetylated Slug downregulates the expression of E-cadherin, the epithelial marker, and upregulates the expression of N-cadherin and vimentin, thereby promoting breast cancer cell migration. In conclusion, the present study demonstrates that CBP-mediated Slug acetylation increases its stability, promoting EMT and migration of breast cancer cells.