Objective: Breakthroughs in omics technology have led to a deeper understanding of the fundamental molecular changes that play a critical role in the development and progression of cancer. This review delves into the hidden molecular drivers of colorectal cancer (CRC), offering potential for clinical translation through novel biomarkers and personalized therapies.
Methods: We summarizes recent studies utilizing various omics approaches, including genomics, transcriptomics, proteomics, epigenomics, metabolomics and data integration with computational algorithms, to investigate CRC.
Results: Integrating multi-omics data in colorectal cancer research unlocks hidden biological insights, revealing new pathways and mechanisms. This powerful approach not only identifies potential biomarkers for personalized prognosis, diagnosis, and treatment, but also predicts patient response to specific therapies, while computational tools illuminate the landscape by deciphering complex datasets.
Conclusions: Future research should prioritize validating promising biomarkers and seamlessly translating them into clinical practice, ultimately propelling personalized CRC management to new heights.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s12029-023-00990-9 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
THBS2 + cancer-associated fibroblasts promote EMT leading to oxaliplatin resistance via COL8A1-mediated PI3K/AKT activation in colorectal cancer.
Mol Cancer
December 2024
Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
Cancer-associated fibroblasts (CAFs) exert multiple tumor-promoting functions and are key contributors to drug resistance. The mechanisms by which specific subsets of CAFs facilitate oxaliplatin resistance in colorectal cancer (CRC) have not been fully explored. This study found that THBS2 is positively associated with CAF activation, epithelial-mesenchymal transition (EMT), and chemoresistance at the pan-cancer level.
View Article and Find Full Text PDFCorrection: Long non-coding RNA CCL14-AS suppresses invasiveness and lymph node metastasis of colorectal cancer cells by regulating MEP1A.
Cancer Cell Int
December 2024
State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
KRAS inhibitors: resistance drivers and combinatorial strategies.
Trends Cancer
December 2024
Charité - Universitätsmedizin Berlin, Institute of Pathology, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany; Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. Electronic address:
In 1982, the RAS genes HRAS and KRAS were discovered as the first human cancer genes, with KRAS later identified as one of the most frequently mutated oncogenes. Yet, it took nearly 40 years to develop clinically effective inhibitors for RAS-mutant cancers. The discovery in 2013 by Shokat and colleagues of a druggable pocket in KRAS paved the way to FDA approval of the first covalently binding KRAS inhibitors, sotorasib and adagrasib, in 2021 and 2022, respectively.
View Article and Find Full Text PDFShould endoscopic submucosal dissection be offered to patients with early colorectal cancer?
Surgery
December 2024
Department of Colorectal Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, OH. Electronic address:
Background: Endoscopic submucosal dissection is increasingly used to treat early-stage colorectal cancer. This study evaluated the feasibility of endoscopic submucosal dissection in this setting and the determinants of lymph node metastasis.
Methods: We reviewed patients who underwent colorectal endoscopic submucosal dissection for early-stage colorectal cancer at a tertiary center between 2011 and 2023.
Development and validation of machine learning models for predicting venous thromboembolism in colorectal cancer patients: A cohort study in China.
Int J Med Inform
December 2024
Chongqing Cancer Multiomics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing 400030, China. Electronic address:
Background: With advancements in healthcare, traditional VTE risk assessment tools are increasingly insufficient to meet the demands of high-quality care, underscoring the need for innovative and specialized assessment methods.
Objective: Owing to the remarkable success of machine learning in supervised learning and disease prediction, our objective is to develop a reliable and efficient model for assessing VTE risk by leveraging the fundamental data and clinical characteristics of colorectal cancer patients within our medical facility.
Methods: Six commonly used machine learning algorithms were utilized in our study to predict the occurrence of VTE in patients with rectal cancer.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!