Abnormal expression of HOXD11 promotes the malignant behavior of glioma cells and leads to poor prognosis of glioma patients

System analysis based on the migration- and invasion-related gene sets identifies the infiltration-related genes of glioma

The current database has no information on the infiltration of glioma samples. Here, we assessed the glioma samples' infiltration in The Cancer Gene Atlas (TCGA) through the single-sample Gene Set Enrichment Analysis (ssGSEA) with migration and invasion gene sets. The Weighted Gene Co-expression Network Analysis (WGCNA) and the differentially expressed genes (DEGs) were used to identify the genes most associated with infiltration. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the major biological processes and pathways. Protein-protein interaction (PPI) network analysis and the least absolute shrinkage and selection operator (LASSO) were used to screen the key genes. Furthermore, the nomograms and receiver operating characteristic (ROC) curve were used to evaluate the prognostic and predictive accuracy of this clinical model in patients in TCGA and the Chinese Glioma Genome Atlas (CGGA). The results showed that turquoise was selected as the hub module, and with the intersection of DEGs, we screened 104 common genes. Through LASSO regression, TIMP1, EMP3, IGFBP2, and the other nine genes were screened mostly in correlation with infiltration and prognosis. EMP3 was selected to be verified in vitro. These findings could help researchers better understand the infiltration of gliomas and provide novel therapeutic targets for the treatment of gliomas.

A new machine learning method for cancer mutation analysis

It is complicated to identify cancer-causing mutations. The recurrence of a mutation in patients remains one of the most reliable features of mutation driver status. However, some mutations are more likely to happen than others for various reasons. Different sequencing analysis has revealed that cancer driver genes operate across complex pathways and networks, with mutations often arising in a mutually exclusive pattern. Genes with low-frequency mutations are understudied as cancer-related genes, especially in the context of networks. Here we propose a machine learning method to study the functionality of mutually exclusive genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering. These networks have indicated critical biological components in the essential pathways, especially those mutated at low frequency. Studying the network and not just the impact of a single gene significantly increases the statistical power of clinical analysis. The proposed method identified important driver genes with different frequencies. We studied the function and the associated pathways in which the candidate driver genes participate. By introducing lower-frequency genes, we recognized less studied cancer-related pathways. We also proposed a novel clustering method to specify driver modules. We evaluated each driver module with different criteria, including the terms of biological processes and the number of simultaneous mutations in each cancer. Materials and implementations are available at: https://github.com/MahnazHabibi/MutationAnalysis.

Molecular Analysis of Prognosis and Immune Infiltration of Ovarian Cancer Based on Homeobox D Genes

Background

Homeobox D (HOXD) genes were associated with cancer pathogenesis. However, the role of HOXD genes in ovarian cancer (OC) and the possible mechanisms involved are unclear. In this study, we analyzed the function and regulatory mechanisms and functions of HOXD genes in OC based on comprehensive bioinformatics analysis.

Methods

Expression of HOXD1/3/4/8/9/10/11/12/13 mRNA was analyzed between OC tissue and normal tissue using ONCOMINE, GEO, and TCGA databases. The relationship between HOXD expression and clinical stage was studied by GEPIA. The Kaplan-Meier plotter was used to analyze prognosis. cBioPortal was used to analyze the mutation and coexpression of HOXDs. GO and KEGG analyses were performed by the DAVID software to predict the function of HOXD coexpression genes. Immune infiltration analysis was used to evaluate the relationship between the expression of HOXD genes and 24 immune infiltrating cells.

Results

The expression of HOXD3/4/8/9/10/11 was significantly lower in OC tissues than in normal ovarian tissues, while the expression of HOXD1/12/13 was significantly higher in OC tissues. The expression of HOXD genes was associated with FIGO stage, primary therapy outcome, tumor status, anatomic neoplasm subdivision, and age. The expression levels of HOXD1/3/4/8/9/10 correlated with tumor stage. HOXD1/8/9 could be served as ideal biomarkers to distinguish OC from normal tissue. Low HOXD9 expression was associated with shorter overall survival (OS) (HR: 0.75; 95% CI: 0.58–0.98; P = 0.034) and progression-free survival (PFS) (HR: 0.69; 95% CI: 0.54–0.87; P = 0.002). The HOXD coexpression genes were associated with pathways including cell cycle, TGF-beta signaling pathway, cellular senescence, and Hippo signaling pathway. HOXD genes were significantly associated with immune infiltration.

Conclusion

The expression of HOXD genes is associated with clinical characteristics. HOXD9 is a new biomarker of prognosis in OC, and HOXD1/4/8/9/10 may be potential therapeutic targets. The members of the HOXD genes may be the response to immunotherapy for OC.

miR-138-5p-mediated HOXD11 promotes cell invasion and metastasis by activating the FN1/MMP2/MMP9 pathway and predicts poor prognosis in penile squamous cell carcinoma

The presence and extent of regional lymph node and distant metastasis are the most fatal prognostic factors in penile squamous cell carcinoma (PSCC). However, the available biomarkers and detailed mechanisms underlying the metastasis of PSCC remain elusive. Here, we explored the expression landscape of HOX genes in twelve paired PSCC tissues, including primary tumors, metastatic lymph nodes and corresponding normal tissues, and highlighted that HOXD11 was indispensable in the progression of PSCC. HOXD11 was upregulated in PSCC cell lines and tumors, especially in metastatic lymph nodes. High HOXD11 expression was associated with aggressive features, such as advanced pN stages, extranodal extension, pelvic lymph node and distant metastasis, and predicted poor survival. Furthermore, tumorigenesis assays demonstrated that knockdown of HOXD11 not only inhibited the capability of cell proliferation, invasion and tumor growth but also reduced the burden of metastatic lymph nodes. Further mechanistic studies indicated that miR-138-5p was a tumor suppressor in PSCC by inhibiting the translation of HOXD11 post-transcriptionally through binding to the 3' untranslated region. Furthermore, HOXD11 activated the transcription of FN1 to decompose the extracellular matrix and to promote epithelial mesenchymal transition-like phenotype metastasis via FN1/MMP2/MMP9 pathways. Our study revealed that HOXD11 is a promising prognostic biomarker and predicts advanced disease with poor outcomes, which could serve as a potential therapeutic target for PSCC.