Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease

Abstract Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver

The circadian clock as a potential biomarker and therapeutic target in pancreatic cancer

Pancreatic cancer (PC) has a very high mortality rate globally. Despite ongoing efforts, its prognosis has not improved significantly over the last two decades. Thus, further approaches for optimizing treatment are required. Various biological processes oscillate in a circadian rhythm and are regulated by an endogenous clock. The machinery controlling the circadian cycle is tightly coupled with the cell cycle and can interact with tumor suppressor genes/oncogenes; and can therefore potentially influence cancer progression. Understanding the detailed interactions may lead to the discovery of prognostic and diagnostic biomarkers and new potential targets for treatment. Here, we explain how the circadian system relates to the cell cycle, cancer, and tumor suppressor genes/oncogenes. Furthermore, we propose that circadian clock genes may be potential biomarkers for some cancers and review the current advances in the treatment of PC by targeting the circadian clock. Despite efforts to diagnose pancreatic cancer early, it still remains a cancer with poor prognosis and high mortality rates. While studies have shown the role of molecular clock disruption in tumor initiation, development, and therapy resistance, the role of circadian genes in pancreatic cancer pathogenesis is not yet fully understood and further studies are required to better understand the potential of circadian genes as biomarkers and therapeutic targets

Role of gut bacterial and non-bacterial microbiota in alcohol-associated liver disease: Molecular mechanisms, biomarkers and therapeutic prospective

Alcohol-associated liver disease (ALD) comprises a spectrum of liver diseases that include: steatosis to alcohol-associated hepatitis, cirrhosis, and ultimately hepatocellular carcinoma. The pathophysiology and potential underlying mechanisms for alcohol-associated liver disease are unclear. Moreover, the treatment of ALD remains a challenge. Intestinal microbiota include bacteria, fungi, and viruses, that are now known to be important in the development of ALD. Alcohol consumption can change the gut microbiota and function leading to liver disease. Given the importance of interactions between intestinal microbiota, alcohol, and liver injury, the gut microbiota has emerged as a potential biomarker and therapeutic target. This review focuses on the potential mechanisms by which the gut microbiota may be involved in the pathogenesis of ALD and explains how this can be translated into clinical management. We discuss the potential of utilizing the gut microbiota signature as a biomarker in ALD patients. Additionally, we present an overview of the prospect of modulating the intestinal microbiota for the management of ALD

The potential therapeutic applications of CRISPR/Cas9 in colorectal cancer

The application of the CRISPR-associated nuclease 9 (Cas9) system in tumor studies has led to the discovery of several new treatment strategies for colorectal cancer (CRC), including the recognition of novel target genes, the construction of animal mass models, and the identification of genes related to chemotherapy resistance. CRISPR/Cas9 can be applied to genome therapy for CRC, particularly regarding molecular-targeted medicines and suppressors. This review summarizes some aspects of using CRISPR/Cas9 in treating CRC. Further in-depth and systematic research is required to fully realize the potential of CRISPR/Cas9 in CRC treatment and integrate it into clinical practice

The potential therapeutic applications of CRISPR/Cas9 in colorectal cancer

The application of the CRISPR-associated nuclease 9 (Cas9) system in tumor studies has led to the discovery of several new treatment strategies for colorectal cancer (CRC), including the recognition of novel target genes, the construction of animal mass models, and the identification of genes related to chemotherapy resistance. CRISPR/Cas9 can be applied to genome therapy for CRC, particularly regarding molecular-targeted medicines and suppressors. This review summarizes some aspects of using CRISPR/Cas9 in treating CRC. Further in-depth and systematic research is required to fully realize the potential of CRISPR/Cas9 in CRC treatment and integrate it into clinical practice

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

Abstract 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

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

Extracellular vesicles: Emerging mediators of cell communication in gastrointestinal cancers exhibiting metabolic abnormalities

There is a complex interaction between pro-tumoural and anti-tumoural networks in the tumour microenvironment (TME). Throughout tumourigenesis, communication between malignant cells and various cells of the TME contributes to metabolic reprogramming. Tumour Dysregulation of metabolic pathways offer an evolutional advantage in the TME and enhance the tumour progression, invasiveness, and metastasis. Therefore, understanding these interactions within the TME is crucial for the development of innovative cancer treatments. Extracellular vesicles (EVs) serve as carriers of various materials that include microRNAs, proteins, and lipids that play a vital role in the communication between tumour cells and non-tumour cells. EVs are actively involved in the metabolic reprogramming process. This review summarized recent findings regarding the involvement of EVs in the metabolic reprogramming of various cells in the TME of gastrointestinal cancers. Additionally, we highlight identified microRNAs involved in the reprogramming process in this group of cancers and explained the abnormal tumour metabolism targeted by exosomal cargos as well as the novel potential therapeutic approaches.</p

The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach

Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan–Meier analysis. The STRING database was used to construct a protein–protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants—the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1—as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes—ASPHD1 and ZBTB12—and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.</p

Extracellular vesicles: emerging mediators of cell communication in gastrointestinal cancers exhibiting metabolic abnormalities

There is a complex interaction between pro-tumoural and anti-tumoural networks in the tumour microenvironment (TME). Throughout tumourigenesis, communication between malignant cells and various cells of the TME contributes to metabolic reprogramming. Tumour Dysregulation of metabolic pathways offer an evolutional advantage in the TME and enhance the tumour progression, invasiveness, and metastasis. Therefore, understanding these interactions within the TME is crucial for the development of innovative cancer treatments. Extracellular vesicles (EVs) serve as carriers of various materials that include microRNAs, proteins, and lipids that play a vital role in the communication between tumour cells and non-tumour cells. EVs are actively involved in the metabolic reprogramming process. This review summarized recent findings regarding the involvement of EVs in the metabolic reprogramming of various cells in the TME of gastrointestinal cancers. Additionally, we highlight identified microRNAs involved in the reprogramming process in this group of cancers and explained the abnormal tumour metabolism targeted by exosomal cargos as well as the novel potential therapeutic approaches.</p