The role of PIK3CA gene mutations in colorectal cancer and the selection of treatment strategies

Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease

INTRODUCTION

Gastrointestinal disorders (GIDs) affect nearly 40% of the global population, with gut microbiome-metabolome interactions playing a crucial role in gastric cancer (GC), colorectal cancer (CRC), and inflammatory bowel disease (IBD). This study aims to investigate how microbial and metabolic alterations contribute to disease development and assess whether biomarkers identified in one disease could potentially be used to predict another, highlighting cross-disease applicability.

METHODS

Microbiome and metabolome datasets from Erawijantari et al. (GC: n = 42, Healthy: n = 54), Franzosa et al. (IBD: n = 164, Healthy: n = 56), and Yachida et al. (CRC: n = 150, Healthy: n = 127) were subjected to three machine learning algorithms, eXtreme gradient boosting (XGBoost), Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO). Feature selection identified microbial and metabolite biomarkers unique to each disease and shared across conditions. A microbial community (MICOM) model simulated gut microbial growth and metabolite fluxes, revealing metabolic differences between healthy and diseased states. Finally, network analysis uncovered metabolite clusters associated with disease traits.

RESULTS

Combined machine learning models demonstrated strong predictive performance, with Random Forest achieving the highest Area Under the Curve(AUC) scores for GC(0.94[0.83-1.00]), CRC (0.75[0.62-0.86]), and IBD (0.93[0.86-0.98]). These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores.

CONCLUSION

These findings emphasize the potential of microbial and metabolic profiling in cross-disease characterization particularly for GIDs, advancing biomarker discovery for improved diagnostics and targeted therapies.

Advances in personalized medicine: translating genomic insights into targeted therapies for cancer treatment

Background

Personalized medicine has revolutionized cancer treatment by utilizing genomic insights to tailor therapies based on individual molecular profiles. This approach enhances therapeutic efficacy, minimizes adverse effects, and addresses tumor heterogeneity through precision-targeted interventions.

Methods

A scoping review was conducted through a comprehensive literature search in PubMed, using MeSH terms and keywords related to genomic profiling and targeted cancer therapies. Eligible studies included original research involving cancer patients who underwent genomic profiling and targeted therapies from January 1, 1950, to February 9, 2025.

Results

Advances in next-generation sequencing (NGS) and bioinformatics have accelerated the identification of clinically relevant mutations-such as epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) and BRAF V600E in melanoma-enabling the development of effective targeted therapies. Emerging technologies like clustered regularly interspaced short palindromic repeats (CRISPR) gene editing and artificial intelligence (AI) are further refining treatment selection by enabling more precise and adaptive therapeutic strategies. Despite these innovations, challenges persist regarding data interpretation, equitable access, costs, regulatory frameworks, and integration into routine clinical workflows.

Conclusions

Genomic profiling is central to the advancement of precision oncology. The convergence of genomics, gene editing, and AI is paving the way toward more personalized, efficient, and inclusive cancer care. Realizing the full potential of personalized medicine will require interdisciplinary collaboration, investment in infrastructure, and ethical oversight to ensure broad, equitable, and responsible implementation in clinical practice.

Decoding colorectal cancer targeted therapy: a bibliometric journey of the last decade (2015-2024)

Colorectal cancer remains one of the most commonly diagnosed cancers globally, with a significant impact on public health. Targeted therapies have revolutionized the treatment landscape for colorectal cancer by offering increased specificity and reduced systemic toxicity compared to conventional chemotherapy. This study provides a comprehensive bibliometric analysis of global research on targeted therapy for colorectal cancer, focusing on publications from 2015 to 2024. A total of 3213 publications were retrieved from the Web of Science Core Collection and analyzed using bibliometric tools to construct knowledge maps and visualize research trends. The regression analysis shows a strong upward trend in publications from 2015 to 2024 (P < 0.001, R2 = 0.889). China leads in publication output, with the University of Texas MD Anderson Cancer Center contributing the highest number of studies. Tabernero and Kopetz are the core authors in the field. Research in this domain has primarily concentrated on the development and clinical assessment of drugs targeting the EGFR, RAS, VEGF, and BRAF signaling pathways, as well as investigating the pathogenesis, drug resistance, and metastatic mechanisms of colorectal cancer. Current advancements emphasize Artificial Intelligence-driven multi-omics integration, the creation of novel therapeutics targeting established molecular pathways, and the execution of global clinical trials to validate personalized treatment strategies.