Circulating tumor cells and cell-free nucleic acids as biomarkers in colorectal cancer

Diagnostic, Prognostic, and Predictive Biomarkers in Gastric Cancer: from Conventional to Novel Biomarkers

Gastric cancer is a major health concern worldwide. The survival rate of Gastric cancer greatly depends on the stage at which it is diagnosed. Early diagnosis is critical for improving survival outcomes. To improve the chances of early diagnosis, regular screening tests, such as an upper endoscopy or barium swallow, are recommended for individuals at a higher risk due to factors like family history or a previous diagnosis of gastric conditions. Biomarkers can be detected and measured using non-invasive methods such as blood tests, urine tests, breath analysis, or imaging techniques. These non-invasive approaches offer many advantages, including convenience, safety, and cost-effectiveness, making them valuable tools for disease diagnosis, monitoring, and research. Biomarker-based tests have emerged as a useful tool for identifying gastric cancer early, monitoring treatment response, assessing the recurrence risk, and personalizing treatment plans. In this current review, we have explored both classical and novel biomarkers for gastric cancer. We have centralized their potential clinical application and discussed the challenges in Gastric cancer research.

Microbiome as a biomarker and therapeutic target in pancreatic cancer

Studying the effects of the microbiome on the development of different types of cancer has recently received increasing research attention. In this context, the microbial content of organs of the gastrointestinal tract has been proposed to play a potential role in the development of pancreatic cancer (PC). Proposed mechanisms for the pathogenesis of PC include persistent inflammation caused by microbiota leading to an impairment of antitumor immune surveillance and altered cellular processes in the tumor microenvironment. The limited available diagnostic markers that can currently be used for screening suggest the importance of microbial composition as a non-invasive biomarker that can be used in clinical settings. Samples including saliva, stool, and blood can be analyzed by 16 s rRNA sequencing to determine the relative abundance of specific bacteria. Studies have shown the potentially beneficial effects of prebiotics, probiotics, antibiotics, fecal microbial transplantation, and bacteriophage therapy in altering microbial diversity, and subsequently improving treatment outcomes. In this review, we summarize the potential impact of the microbiome in the pathogenesis of PC, and the role these microorganisms might play as biomarkers in the diagnosis and determining the prognosis of patients. We also discuss novel treatment methods being used to minimize or prevent the progression of dysbiosis by modulating the microbial composition. Emerging evidence is supportive of applying these findings to improve current therapeutic strategies employed in the treatment of PC.

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.

The Promise of Epigenetics Research in the Treatment of Appendiceal Neoplasms

Appendiceal cancers (AC) are a rare and heterogeneous group of malignancies. Historically, appendiceal neoplasms have been grouped with colorectal cancers (CRC), and treatment strategies have been modeled after CRC management guidelines due to their structural similarities and anatomical proximity. However, the two have marked differences in biological behavior and treatment response, and evidence suggests significant discrepancies in their respective genetic profiles. In addition, while the WHO classification for appendiceal cancers is currently based on traditional histopathological criteria, studies have demonstrated that histomorphology does not correlate with survival or treatment response in AC. Due to their rarity, appendiceal cancers have not been studied as extensively as other gastrointestinal cancers. However, their incidence has been increasing steadily over the past decade, making it crucial to identify new and more effective strategies for detection and treatment. Recent efforts to map and understand the molecular landscape of appendiceal cancers have unearthed a wealth of information that has made it evident that appendiceal cancers possess a unique molecular profile, distinct from other gastrointestinal cancers. This review focuses on the epigenetic landscape of epithelial appendiceal cancers and aims to provide a comprehensive overview of the current state of knowledge of epigenetic changes across different appendiceal cancer subtypes, highlighting the challenges as well as the promise of employing epigenetics in the quest for the detection of biomarkers, therapeutic targets, surveillance markers, and predictors of treatment response and survival in epithelial appendiceal neoplasms.