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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by

Cemil Colak1, Fatma Hilal Yagin1, Abdulmohsen Algarni2

  • 1Department of Biostatistics, and Medical Informatics, Faculty of Medicine, Inonu University, 44280 Malatya, Turkey.

Medicina (Kaunas, Lithuania)
|March 27, 2025
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Explainable AI and lipidomics successfully identified biomarkers for early liver cancer detection. This approach reveals critical lipid metabolism changes in cancer progression, aiding precision oncology efforts.

Keywords:
SHAPbiomarkerslipidomicsliver cancermachine learningprecision medicine

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Area of Science:

  • Biochemistry
  • Artificial Intelligence
  • Oncology

Background:

  • Liver cancer is a leading cause of cancer mortality.
  • Altered lipid metabolism is a key feature of liver cancer (hepatocarcinogenesis).
  • Novel diagnostic biomarkers are crucial for early detection.

Purpose of the Study:

  • To utilize explainable artificial intelligence (XAI) for identifying lipidomic biomarkers for liver cancer.
  • To develop a robust machine learning model for early liver cancer diagnosis.
  • To enhance the interpretability of predictive models in liver cancer detection.

Main Methods:

  • Untargeted lipidomic analysis of serum samples from 219 liver cancer patients and 219 controls using LC-QTOF-MS.
  • Statistical analyses including fold change, t-tests, PLS-DA, and Elastic Network for feature selection.
  • Development and evaluation of machine learning models (AdaBoost, Random Forest, Gradient Boosting) with SHAP for interpretability.

Main Results:

  • Significant alterations in lipid profiles, including decreased sphingomyelins and increased fatty acids and phosphatidylcholines.
  • The AdaBoost model achieved a high classification performance with an AUC of 0.875.
  • Phosphatidylcholine (PC 40:4) was identified as a key predictive lipid by SHAP analysis.

Conclusions:

  • Untargeted lipidomics combined with XAI and machine learning effectively identifies early liver cancer biomarkers.
  • Lipid metabolism alterations are integral to liver cancer progression.
  • This approach offers valuable insights for integrating lipidomics into precision oncology strategies.