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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Chromatin Position Affects Gene Expression02:35

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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mRNA Stability and Gene Expression02:51

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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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Related Experiment Video

Updated: Jan 29, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Gene Expression-Based Colorectal Cancer Prediction Using Machine Learning and SHAP Analysis.

Yulai Yin1, Zhen Yang1, Xueqing Li1,2

  • 1School of Medicine, Nankai University, Tianjin 300071, China.

Genes
|January 28, 2026
PubMed
Summary

A novel genetic diagnostic model for colorectal cancer (CRC) was developed using machine learning. This ten-gene model shows high predictive performance, offering potential for early CRC detection and intervention.

Keywords:
colorectal cancerdifferential genesmachine learningmendelian randomizationpredictive model

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Area of Science:

  • Genomics
  • Bioinformatics
  • Oncology

Background:

  • Colorectal cancer (CRC) poses a significant global health challenge.
  • Accurate and early diagnosis is crucial for effective treatment and improved patient outcomes.
  • Genetic markers offer a promising avenue for developing precise diagnostic tools.

Purpose of the Study:

  • To develop and validate a robust genetic diagnostic model for colorectal cancer (CRC).
  • To identify key genes associated with CRC through integrated bioinformatics analyses.
  • To leverage machine learning for predicting CRC risk and diagnosis.

Main Methods:

  • Differential gene expression analysis using TCGA database.
  • Mendelian randomization analysis with eQTL and CRC outcome data.
  • Development and validation of a diagnostic model using nine machine learning algorithms, including XGBoost.
  • Gene selection based on differential expression, randomization analysis, and machine learning model importance.

Main Results:

  • Identified 3716 differentially expressed genes (DEGs) and 121 CRC-associated genes via Mendelian randomization.
  • A final ten-gene signature (RIF1, GDPD5, DBNDD1, RCCD1, CLDN5, ASCL2, IFITM3, IFITM1, SMPDL3A, SUCLG2) was established.
  • The XGBoost model achieved an AUC of 0.990, with the final model showing AUCs of 0.9875 (training) and 0.9601 (validation).
  • IFITM1 and DBNDD1 were identified as the most influential genes.

Conclusions:

  • The gene expression profile in CRC reflects enhanced cell proliferation, metabolism, and immune evasion.
  • The developed ten-gene genetic diagnostic model demonstrates strong predictive performance for CRC.
  • This model has significant potential for early CRC diagnosis, intervention, and third-tier prevention strategies.