PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common malignant tumor. Identification of biomarkers and understanding their potential functions will facilitate the treatment and diagnosis in LUAD patients. The yellow module (cor = 0.31, P = 2e-6) was selected as the core module based on weighted gene co-expression network analysis (WGCNA) by integrating RNA-seq data and tumor stage. Two upregulated genes (PLAU and GREM1) in yellow module were identified to be biomarkers. Kaplan-Meier curve analysis displayed that high expression levels of them had a poor overall survival (OS). And, their high expression levels revealed higher tumor stage and relapse possibility in LUAD patients, and could be a prognostic parameter. Both biomarkers showed similar immune cell expression profiles in low- and high-expression groups. Strongly positive correlation between both biomarkers and biomarkers of tumor-infiltrating lymphocytes were also clarified in TCGA-LUAD cohort. Importantly, single gene GSEA showed that transcriptional mis-regulation in cancer and microRNAs in cancer were enriched in LUAD patients. Therefore, a miRNA-mRNA-transcription factors (TFs) co-expression regulatory networks was constructed for each biomarker, various miRNAs and TFs were related to PLAU and GREM1. Among which, 6 downstream TFs were overlapped genes for both biomarkers. Notably, 2 of these TFs (FOXF1 and TFAP2A) exhibited significantly abnormal expression levels. Among which, FOXF1 was downregulated and TFAP2A was upregulated in TCGA-LUAD cohort. Both TFs showed a significantly positive correlation with the expression level of PLAU. In conclusion, we identified 2 biomarkers related to immune response and achieved a good accuracy in predicting OS in patients with LUAD.

The integration of multidisciplinary approaches revealed PTGES3 as a novel drug target for breast cancer treatment.

BACKGROUND: The main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches. METHODS: Differentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated. RESULTS: A novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3. CONCLUSIONS: PTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.

Immune-related biomarkers predict the prognosis and immune response of breast cancer based on bioinformatic analysis and machine learning.

Breast cancer (BC) is the malignancy with the highest mortality rate among women, identification of immune-related biomarkers facilitates precise diagnosis and improvement of the survival rate in early-stage BC patients. 38 hub genes significantly positively correlated with tumor grade were identified based on weighted gene coexpression network analysis (WGCNA) by integrating the clinical traits and transcriptome analysis. Six candidate genes were screened from 38 hub genes basing on least absolute shrinkage and selection operator (LASSO)-Cox and random forest. Four upregulated genes (CDC20, CDCA5, TTK and UBE2C) were identified as biomarkers with the log-rank p < 0.05, in which high expression levels of them showed a poor overall survival (OS) and recurrence-free survival (RFS). A risk model was finally constructed using LASSO-Cox regression coefficients and it possessed superior capability to identify high risk patients and predict OS (p < 0.0001, AUC at 1-, 3- and 5-years are 0.81, 0.73 and 0.79, respectively). Decision curve analysis demonstrated risk score was the best prognostic predictor, and low risk represented a longer survival time and lower tumor grade. Importantly, multiple immune cell types and immunotherapy targets were observed increase in expression levels in high-risk group, most of which were significantly correlated with four genes. In summary, the immune-related biomarkers could accurately predict the prognosis and character the immune responses in BC patients. In addition, the risk model is conducive to the tiered diagnosis and treatment of BC patients.

Long Non-Coding RNAs, Cell Cycle, and Human Breast Cancer.

The long non-coding RNAs (lncRNAs) constitute an important class of the human transcriptome. The discovery of lncRNAs provided one of many unexpected results of the post-genomic era and uncovered a huge number of previously ignored transcriptional events. In recent years, lncRNAs are known to be linked with human diseases, with particular focus on cancer. Growing evidence has indicated that dysregulation of lncRNAs in breast cancer (BC) is strongly associated with the occurrence, development, and progress. Increasing numbers of lncRNAs have been found to interact with cell cycle progression and tumorigenesis in BC. The lncRNAs can exert their effect as a tumor suppressor or oncogene and regulate tumor development through direct or indirect regulation of cancer-related modulators and signaling pathways. What is more, lncRNAs are excellent candidates for promising therapeutic targets in BC due to the features of high tissue and cell-type specific expression. However, the underlying mechanisms of lncRNAs in BC still remain largely undefined. Here, we concisely summarize and sort out the current understanding of research progress in relationships of the roles for lncRNA in regulating the cell cycle. We also summarize the evidence for aberrant lncRNA expression in BC, and the potential for lncRNA to improve BC therapy is also discussed. Together, lncRNAs can be considered as exciting therapeutic candidates whose expression can be altered to impede BC progression.

ABO Blood Group and the Risk and Prognosis of Lymphoma.

ABO blood group antigens exhibit alternative phenotypes and genetically derived structures that are located on the red cell surface. The role of ABO blood group in cancer biology has been intensely reported by several studies, and it is now widely recognized that ABO antigens are associated with the risk and prognosis of several types of tumors, namely gastric cancer and pancreatic cancer. However, there have been contentious limited issues with the association between the ABO blood group and lymphoma. In this narrative review, based on literature data, we discuss the role of ABO blood group in the risk and prognosis of lymphoma and summarize the current knowledge of the underlying pathogenic mechanisms of the association. The possible association of ABO blood group with racial disparities and pathological classification in lymphoma patients is also discussed.

Energy metabolism pathways in breast cancer progression: The reprogramming, crosstalk, and potential therapeutic targets.

Breast cancer (BC) is a malignant tumor that seriously endangers health in women. BC, like other cancers, is accompanied by metabolic reprogramming. Among energy metabolism-related pathways, BC exhibits enhanced glycolysis, tricarboxylic acid (TCA) cycle, pentose phosphate pathway (PPP), glutamate metabolism, and fatty acid metabolism activities. These pathways facilitate the proliferation, growth and migration of BC cells. The progression of BC is closely related to the alterations in the activity or expression level of several metabolic enzymes, which are regulated by the intrinsic factors such as the key signaling and transcription factors. The metabolic reprogramming in the progression of BC is attributed to the aberrant expression of the signaling and transcription factors associated with the energy metabolism pathways. Understanding the metabolic mechanisms underlying the development of BC will provide a druggable potential for BC treatment and drug discovery.