A correlation between BCL-2 modifying factor, p53 and livin gene expressions in cancer colon patients

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

Livin promotes Th2-type immune response in airway allergic diseases

Objectives

To investigate the effects of livin on the Th2 immune response in airway allergic diseases (AAD) and explore the interaction among livin, GATA3, IL-4 in peripheral blood CD4⁺ T cells of AAD patients.

Methods

WT mice and livin KO mice were developed for model of AAD. Th2 cell levels in the lung tissues and spleen were assessed by flow cytometry. Also, it was assessed in the culture after exposing to livin inhibitor (Lp-15); the protein and mRNA levels of livin, GATA3 and IL-4 in peripheral blood CD4⁺ T cells isolated from patients with or without AAD were measured by real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting, respectively. Finally, Co-immunoprecipitation (Co-IP) was employed to identify the interaction between livin and GATA3.

Results

Compared with WT mouse, Th2 cell frequency in lung tissues and spleen was significantly decreased in livin KO mouse; after adding Lp-15, the differentiation from Naive CD4⁺T cells in spleen to Th2 cells was blocked; the protein and mRNA levels of livin, GATA3 and IL-4 in AAD group were higher than that in control group. The levels of livin were positively correlated with IL-4, and GATA3 was also positively correlated with IL-4 and livin. GATA3 was detected in the protein complex co-precipitated with livin antibody, and livin was also detected in the protein complex co-precipitated by GATA3 antibody.

Conclusion

Livin increases the expression of IL-4 and facilitates naive CD4⁺ T cells to differentiate into Th2 cells, which triggers airway allergy.

Breast Cancer Prognosis Prediction and Immune Pathway Molecular Analysis Based on Mitochondria-Related Genes

Background

Mitochondria play an important role in breast cancer (BRCA). We aimed to build a prognostic model based on mitochondria-related genes.

Method

Univariate Cox regression analysis, random forest, and the LASSO method were performed in sequence on pretreated TCGA BRCA datasets to screen out genes from a Gene Set Enrichment Analysis, Gene Ontology: biological process gene set to build a prognosis risk score model. Survival analyses and ROC curves were performed to verify the model by using the GSE103091 dataset. The BRCA datasets were equally divided into high- and low-risk score groups. Comparisons between clinical features and immune infiltration related to different risk scores and gene mutation analysis and drug sensitivity prediction were performed for different groups.

Result

Four genes, MRPL36, FEZ1, BMF, and AFG1L, were screened to construct our risk score model in which the higher the risk score, the poorer the prognosis. Univariate and multivariate analyses showed that the risk score was significantly associated with age, M stage, and N stage. The gene mutation probability in the high-risk score group was significantly higher than that in the low-risk score group. Patients with higher risk scores were more likely to die. Drug sensitivity prediction in different groups indicated that PF-562271 and AS601245 might be new inhibitors of BRCA.

Conclusion

We developed a new workable risk score model based on mitochondria-related genes for BRCA prognosis and identified new targets and drugs for BRCA research.

Tumor Suppressor Protein p53 and Inhibitor of Apoptosis Proteins in Colorectal Cancer-A Promising Signaling Network for Therapeutic Interventions

Simple Summary

Tumor suppressor 53 (p53) is a multifunctional protein that regulates cell cycle, DNA repair, apoptosis and metabolic pathways. In colorectal cancer (CRC), mutations of the gene occur in 60% of patients and are associated with a more aggressive tumor phenotype and resistance to anti-cancer therapy. In addition, inhibitor of apoptosis (IAP) proteins are distinguished biomarkers overexpressed in CRC that impact on a diverse set of signaling pathways associated with the regulation of apoptosis/autophagy, cell migration, cell cycle and DNA damage response. As these mechanisms are further firmly controlled by p53, a transcriptional and post-translational regulation of IAPs by p53 is expected to occur in cancer cells. Here, we aim to review the molecular regulatory mechanisms between IAPs and p53 and discuss the therapeutic potential of targeting their interrelationship by multimodal treatment options.

Abstract

Despite recent advances in the treatment of colorectal cancer (CRC), patient’s individual response and clinical follow-up vary considerably with tumor intrinsic factors to contribute to an enhanced malignancy and therapy resistance. Among these markers, upregulation of members of the inhibitor of apoptosis protein (IAP) family effects on tumorigenesis and radiation- and chemo-resistance by multiple pathways, covering a hampered induction of apoptosis/autophagy, regulation of cell cycle progression and DNA damage response. These mechanisms are tightly controlled by the tumor suppressor p53 and thus transcriptional and post-translational regulation of IAPs by p53 is expected to occur in malignant cells. By this, cellular IAP1/2, X-linked IAP, Survivin, BRUCE and LIVIN expression/activity, as well as their intracellular localization is controlled by p53 in a direct or indirect manner via modulating a multitude of mechanisms. These cover, among others, transcriptional repression and the signal transducer and activator of transcription (STAT)3 pathway. In addition, p53 mutations contribute to deregulated IAP expression and resistance to therapy. This review aims at highlighting the mechanistic and clinical importance of IAP regulation by p53 in CRC and describing potential therapeutic strategies based on this interrelationship.