Identification of Novel Molecular Therapeutic Targets and Their Potential Prognostic Biomarkers Among Kinesin Superfamily of Proteins in Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Kinesin superfamily of proteins (KIFs) has been broadly reported to play an indispensable role in the biological process. Recently, emerging evidence reveals its oncogenic role in various cancers. However, the prognostic, oncological, and immunological values of KIFs have not been comprehensively explored in pancreatic ductal adenocarcinoma (PDAC) patients. We aimed to illustrate the relationship between KIFs and pancreatic ductal adenocarcinoma by using bioinformatical analysis. METHODS: We use GEPIA, Oncomine datasets, cBioPortal, LOGpc, TIMER, and STRING bioinformatics tools and web servers to investigate the aberrant expression, prognostic values, and oncogenic role of KIFs. The two-gene prognostic model and the correlation between KIFs and KRAS and TP53 mutation were performed using an R-based computational framework. RESULTS: Our results demonstrated that KIFC1/2C/4A/11/14/15/18A/18B/20B/23 (we name it prognosis-related KIFs) were upregulated and associated with unfavorable clinical outcome in pancreatic cancer patients. KIF21B overexpression is associated with better clinical outcome. The KIFC1/2C/4A/11/14/15/18A/18B/20B/23 profiles were significantly increased compared to grade 1 and grade 2/3. Besides, KIFC1/2C/4A/11/14/15/18A/18B/20B/23 was significantly associated with the mutation status of KRAS and TP53.Notably, most prognosis-related KIFs have strong correlations with tumor growth and myeloid-derived suppressor cells infiltration (MDSCs). A prognostic signature based on KIF20B and KIF21B showed a reliable predictive performance. Receiver operating characteristic (ROC) curve was employed to assess the predictive power of two-gene signature. Consequently, the gene set enrichment analysis (GSEA) showed that KIF20B and KIF21B's overexpression was associated with the immunological and oncogenic pathway activation in pancreatic cancer. Finally, real-time quantitative PCR (RT-qPCR) was utilized to investigate the expression pattern of KIF20B and KIF21B in pancreatic cancer cell lines and normal pancreatic cell. CONCLUSIONS: Knowledge of the expression level of the KIFs may provide novel therapeutic molecular targets and potential prognostic biomarkers to pancreatic cancer patients.

Identification of biomarkers related to Tumor-Infiltrating Lymphocytes (TILs) infiltration with gene co-expression network in colorectal cancer.

Colorectal cancer (CRC) is one of the most common tumors, ranking second in the global cause of death from cancer. The prognosis of advanced patients is still very poor. In this study, hub modules with the highest association with tumor-infiltrating immune cells were identified by weighted gene co-expression network analysis based on CRC expression data from the Gene Expression Omnibus database. Next, three hub genes (ADAM8, IL-1A, VAV3) related to infiltrating immune cells were identified by co-expression network and prognostic analysis. After analysis and verification of the TIMER database, ADAM8 was selected as a prognostic biomarker. Finally, the result of functional test showed that ADAM8 gene expression down-regulation partially reversed the immune tolerance of CRC cells to TILs. By bioinformatics analysis methods and the experimental techniques, we identified ADAM8 as a prognostic biomarker and clinical therapeutic target related to tumor-infiltrating immune cells in CRC.

ABO blood type, smoking status, other risk factors and prognosis of pancreatic ductal adenocarcinoma.

The aim of this observational study was to test whether ABO blood type was a prognostic factor for pancreatic ductal adenocarcinoma (PDAC) patients and whether other risk factors could influence pancreatic cancer patients' survival. This study included 610 patients who were diagnosed as pancreatic cancer and had undergone radical surgery. Patients' characteristics included age, gender, tumor stage, tumor grade, adenosquamous carcinoma (ASC) status, preoperative serum carbohydrate antigen 19-9 (CA19-9) levels, preoperative serum carcinoembryonic antigen (CEA) levels, ABO blood type, smoking status, and drinking status were analyzed in this study. Cox proportional hazards regression model and Kaplan-Meier method were used to evaluate the role of prognostic factors. For pancreatic cancer patients undergoing radical surgery, the overall survival was worse for ASC patients than PDAC patients (Log-rank = 11.315, P < .001). Compared with ASC patients (Log-rank < 0.001, P = .996), PDAC patients can benefit from chemotherapy (Log-rank = 17.665, P < .001). For PDAC patients, O blood type had better overall survival than non-O blood type (Log-rank = 4.153, P = .042). Moreover, the group with higher serum levels of CA19-9 had poor prognosis compared to another group with low serum CA19-9 (Log-rank = 4.122, P = .042). Higher CEA levels indicated poor prognosis (Log-rank = 13.618, P < .001). In conclusion, ASC status was associated with overall survival of pancreatic cancer patients and cannot benefit from postoperative chemotherapy. Non-O blood type was a prognostic factor for PDAC patients.

Evaluation of the target genes of arsenic trioxide in pancreatic cancer by bioinformatics analysis.

The aim of the present study was to evaluate the potential network of arsenic trioxide (ATO) target genes in pancreatic cancer. The DrugBank, STITCH, cBioPortal, Kaplan-Meier plotter and Oncomine websites were used to analyze the association of ATO and its target genes with pancreatic cancer. Initially, 19 ATO target genes were identified, along with their associated protein-protein interaction networks and Kyoto Encyclopedia of Genes and Genomes pathways. ATO was found to be associated with multiple types of cancer, and the most common solid cancer was pancreatic cancer. A total of 6 ATO target genes (namely AKT1, CCND1, CDKN2A, IKBKB, MAPK1 and MAPK3) were found to be associated with pancreatic cancer. Next, the mutation information of the 6 ATO target genes in pancreatic cancer was collected. A total of 20 ATO interacting genes were identified, which were mainly involved in hepatitis B, prostate cancer, pathways in cancer, glioma and chronic myeloid leukemia. Finally, the genes CCND1 and MAPK1 were detected to be prognostic factors in patients with pancreatic cancer. In conclusion, bioinformatics analysis may help elucidate the molecular mechanisms underlying the involvement of ATO in pancreatic cancer, enabling more effective treatment of this disease.