Functional Enrichment Analysis of Tumor Microenvironment-Driven Molecular Alterations That Facilitate Epithelial-to-Mesenchymal Transition and Distant Metastasis.

Nowadays, hepatocellular carcinoma (HCC) is the second leading cause of cancer deaths, and identifying the effective factors in causing this disease can play an important role in its prevention and treatment. Tumors provide effective agents for invasion and metastasis to other organs by establishing appropriate communication between cancer cells and the microenvironment. Epithelial-to-mesenchymal transition (EMT) can be mentioned as one of the effective phenomena in tumor invasion and metastasis. Several factors are involved in inducing this phenomenon in the tumor microenvironment, which helps the tumor survive and migrate to other places. It can be effective to identify these factors in the use of appropriate treatment strategies and greater patient survival. This study investigated the molecular differences between tumor border cells and tumor core cells or internal tumor cells in HCC for specific EMT genes. Expression of NOTCH1, ID1, and LST1 genes showed a significant increase at the HCC tumor border. Targeting these genes can be considered as a useful therapeutic strategy to prevent distant metastasis in HCC patients.

Prediction of anti-TNF therapy failure in ulcerative colitis patients by ensemble machine learning: A prospective study.

Nowadays, anti-TNF therapy remarkably improves the medical management of ulcerative colitis (UC), but approximately 40 % of patients do not respond to this treatment. In this study, we used 79 anti-TNF-naive patients with moderate-to-severe UC from four cohorts to discover alternative therapeutic targets and develop a personalized medicine approach that can diagnose UC non-responders (UCN) prior to receiving anti-TNF therapy. To this end, two microarray data series were integrated to create a discovery cohort with 35 UC samples. A comprehensive gene expression and functional analysis was performed and identified 313 significantly altered genes, among which IL6 and INHBA were highlighted as overexpressed genes in the baseline mucosal biopsies of UCN, whose cooperation may lead to a decrease in the Tregs population. Besides, screening the abundances of immune cell subpopulations showed neutrophils' accumulation increasing the inflammation. Furthermore, the correlation of KRAS signaling activation with unresponsiveness to anti-TNF mAb was observed using network analysis. Using 50x repeated 10-fold cross-validation LASSO feature selection and a stack ensemble machine learning algorithm, a five-mRNA prognostic panel including IL13RA2, HCAR3, CSF3, INHBA, and MMP1 was introduced that could predict the response of UC patients to anti-TNF antibodies with an average accuracy of 95.3 %. The predictive capacity of the introduced biomarker panel was also validated in two independent cohorts (44 UC patients). Moreover, we presented a distinct immune cell landscape and gene signature for UCN to anti-TNF drugs and further studies should be considered to make this predictive biomarker panel and therapeutic targets applicable in the clinical setting.

GPX2 and BMP4 as Significant Molecular Alterations in The Lung Adenocarcinoma Progression: Integrated Bioinformatics Analysis.

Objective: Non-small cell lung adenocarcinoma (NSCLC) is the most common type of lung cancer, which is considered as the most lethal and prevalent cancer worldwide. Recently, molecular changes have been implicated to play a significant role in the cancer progression. Despite of numerous studies, the molecular mechanism of NSCLC pathogenesis in each sub-stage remains unclear. Studying these molecular alterations gives us a chance to design successful therapeutic plans which is aimed in this research. Materials and Methods: In this bioinformatics study, we compared the expression profile of 7 minor stages of NSCLC adenocarcinoma, including GSE41271, GSE42127, and GSE75037, to clarify the relation of molecular alterations and tumorigenesis. At first, 99 common differentially expressed genes (DEG) were obtained. Then, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to uncover the association of significant cellular and molecular changes. Finally, gene expression profile interactive analysis (GEPIA) was employed to validate the results by RNA-seq expression data. Results: Primary analysis showed that BMP4 was downregulated through the tumor progression to the stage IB and GPX2 was upregulated in the course of final tumor development to the stage IV and distant metastasis. Functional enrichment analysis indicated that BMP4 in the TGF-ß signaling pathway and GPX2 in the glutathione metabolism pathway may be the key genes for NSCLC adenocarcinoma progression. GEPIA analysis revealed a correlation between BMP4 downregulation and GPX2 upregulation and lung adenocarcinoma (LUAD) progression and lower survival chances in LUAD patients which confirm microarray data. Conclusion: Taken together, we suggested GPX2 as an oncogene by inhibiting apoptosis, promoting EMT and increasing glucose uptake in the final stages and BMP4 as a tumor suppressor via inducing apoptosis and arresting cell cycle in the early stages through lung adenocarcinoma (ADC) development to make them candidate genes to further cancer therapy investigations.