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Open AccessJournal ArticleDOI

Prognostic and Predictive Molecular Biomarkers for Colorectal Cancer: Updates and Challenges

TLDR
The different molecular markers in clinical use for CRC are described, new markers that might become indispensable over the next years are highlighted, recently developed gene expression-based tests are discussed and the challenges in biomarker research are highlighted.
Abstract
Colorectal cancer (CRC) is a leading cause of death among cancer patients. This heterogeneous disease is characterized by alterations in multiple molecular pathways throughout its development. Mutations in RAS, along with the mismatch repair gene deficiency, are currently routinely tested in clinics. Such biomarkers provide information for patient risk stratification and for the choice of the best treatment options. Nevertheless, reliable and powerful prognostic markers that can identify “high-risk” CRC patients, who might benefit from adjuvant chemotherapy, in early stages, are currently missing. To bridge this gap, genomic information has increasingly gained interest as a potential method for determining the risk of recurrence. However, due to several limitations of gene-based signatures, these have not yet been clinically implemented. In this review, we describe the different molecular markers in clinical use for CRC, highlight new markers that might become indispensable over the next years, discuss recently developed gene expression-based tests and highlight the challenges in biomarker research.

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Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer

TL;DR: Li et al. as mentioned in this paper developed a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS), which is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival.
Journal ArticleDOI

Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer

TL;DR: Li et al. as discussed by the authors developed a machine learning-based integrative procedure for constructing a consensus immune-related lncRNA signature (IRLS), which is an independent risk factor for overall survival and displays stable and powerful performance, but only demonstrates limited predictive value for relapse-free survival.
Journal ArticleDOI

Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer

TL;DR: Wang et al. as mentioned in this paper proposed a consensus machine learning-derived lncRNA signature (CMDLncS) that exhibited best power for predicting recurrence risk from 76 kinds of algorithm combinations.
Journal ArticleDOI

Distant Metastasis in Colorectal Cancer Patients-Do We Have New Predicting Clinicopathological and Molecular Biomarkers? A Comprehensive Review.

TL;DR: In this paper, a review of possible molecular and clinicopathological biomarkers that may provide prognostic and predictive information for patients with distant metastasis is presented, including sideness of the tumor, molecular profile and epigenetic characteristics of the primary tumor and arising metastatic CRC, and early markers reflecting cancer cell resistance in mCRC.
Journal ArticleDOI

Prognostic Cancer Gene Expression Signatures: Current Status and Challenges.

TL;DR: In this article, the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.
References
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Journal ArticleDOI

Cancer Metastasis: Building a Framework

TL;DR: Understanding of the origins and nature of cancer metastasis and the selection of traits that are advantageous to cancer cells is promoted.
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What are the current problem with CRC biomarkers?

The current problem with CRC biomarkers is the lack of reliable and powerful prognostic markers for identifying high-risk patients.