The present study aimed to identify the recurrence-associated genes in colon cancer, which may provide theoretical evidence for the development of novel methods to prevent tumor recurrence. Colon cancer and matched normal samples microarray data (E-GEOD-39582) were downloaded from ArrayExpress. Genes with significant variation were identified, followed by the screening of differentially expressed genes (DEGs). Subsequently, the co-expression network of DEGs was constructed using the weighted correlation network analysis (WGCNA) method, which was verified using the validation dataset. The significant modules associated with recurrence in the network were subsequently screened and verified in another independent dataset E-GEOD-33113. Function and pathway enrichment analyses were also conducted to determine the roles of selected genes. Survival analysis was performed to identify the association between these genes and survival. A total of 434 DEGs were identified in the colon samples, and stress-associated endoplasmic reticulum protein family member 2 (SERP2) and long non-coding RNA-0219 (LINC0219) were determined to be the vital DEGs between all the three sub-type groups with different clinical features. The brown module was identified to be the most significant module in the co-expression network associated with the recurrence of colon cancer, which was verified in the E-GEOD-33113 dataset. Top 10 genes in the brown module, including EGF containing fibulin like extracellular matrix protein 2 (EFEMP2), fibrillin 1 (FBN1) and secreted protein acidic and cysteine rich (SPARC) were also associated with survival time of colon cancer patients. Further analysis revealed that the function of cell adhesion, biological adhesion, extracellular matrix (ECM) organization, pathways of ECM-receptor interaction and focal adhesion were the significantly changed terms in colon cancer. In conclusion, SERP2, EFEMP2, FBN1, SPARC, and LINC0219 were revealed to be the recurrence-associated molecular and prognostic indicators in colon cancer by WGCNA co-expression network analysis.

Background Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment. Methods Gene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC). Results Consensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses. Conclusions The molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

The present study aimed to establish a prediction model for hepatocellular carcinoma (HCC) based on the cross talk genes from important biological pathways involved in HCC. Differentially expressed genes (DEGs) for HCC were identified from mRNA profiles of GSE36376, which were mapped to protein-protein interaction (PPI) networks from BioGrid and the human protein reference database. Then critical genes based on the deviation score and the degree of node were selected from the novel PPI network. Cross talk genes were screened from the network established based on the associations of gene-gene, gene-pathway and pathway-pathway. A classifier based on specific cross talk genes was constructed for prediction of HCC using the random forest algorithm. Finally, the diagnostic performance of this prediction model was verified by predicting survival time of patients with HCC from the genome cancer atlas (TCGA) and other independent gene expression omnibus (GEO) databases. From the novel PPI network, a total of 200 critical genes were screened out and they were significantly enriched in 23 pathways, which have been reported to be significantly associated with the development of HCC. Based on these identified pathways, cross talk genes were identified including AKT1, SOS1, EGF, MYC, IGF1, ERBB2, CDKN1B, SHC2, VEGFA and INS. The prediction model has a relative average classification accuracy of 0.94 for HCC, which has a stable predicting efficacy for survival time of HCC patients validated in the TCGA database and two other independent GEO datasets. In conclusion, a total of 39 cross talk genes in HCC were identified and a classifier based on the cross talk genes was constructed, which indicates a high prognosis prediction efficacy in several independent datasets. The results provide a novel perspective to develop a multiple gene diagnostic tool for HCC prognosis, which also provided potential biomarkers or therapeutic targets for HCC.

Background. Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide nowadays. Block of proliferation 1 (BOP1), a nucleolar protein involved in rRNA processing and ribosome assembly, is associated with tumor development in certain cancers of digestive system. Therefore, we hypothesized that BOP1 might play an important role in gastric cancer development. Methods. Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) were used to identify the differentially expressed genes and their clinical relevance. qPCR and western blot were performed further to examine the levels of BOP1 mRNA and protein, respectively. Cell viability, apoptosis, migration and invasion were investigated in gastric cancer cell lines with BOP1 silencing or overexpression. The epithelial mesenchymal transition (EMT) associated proteins, including E-cadherin and N-cadherin, were measured using immunoblotting. Finally, the downstream pathway of BOP1 were explored using bioinformatic analysis and qPCR. Results. BOP1 was found up-regulated in gastric tumor tissues compared with paired normal tissues (P < 0.0001). Its expression was associated with more advanced pathological grades (P = 0.0006) and tumor location (P = 0.002), as well as a poor survival (HR 1.27, P = 0.015). BOP1 expression was increased in 4 kind of tumor cell lines compared with the normal group. The overexpression of BOP1 promoted cell proliferation and inhibit cell apoptosis, while silencing BOP1 showed a reversed trend. Immunoblotting results suggested that BOP enhanced N-cadherin, a mesenchymal marker, while reduced E-cadherin, an epithelial marker. Finally, bioinformatic prediction showed that the cell cycle could be a downstream pathway of BOP1. Conclusions. The present study demonstrated that BOP1 contributed to the development of gastric cancer by promoting proliferation, invasion and epithelial mesenchymal transformation, which could be a biomarker or therapeutic target in GC.

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase expressed at early stages of normal development and in various cancers including ALK-positive anaplastic large cell lymphoma (ALK+ ALCL), in which it is the main therapeutic target. ALK tyrosine kinase inhibitors (ALK TKIs) have greatly improved the prognosis of ALK+ALCL patients, but the emergence of drug resistance is inevitable and limits the applicability of these drugs. Although various mechanisms of resistance have been elucidated, the problem persists and there have been relatively few relevant clinical studies. This review describes research progress on ALK+ ALCL including the application and development of new therapies, especially in relation to drug resistance. We also propose potential treatment strategies based on current knowledge to inform the design of future clinical trials.