大腸がんを検出する新しいバイオマーカーを開発(New biomarkers to detect colorectal cancer)

2025-01-22

2025-01-20 バーミンガム大学

バーミンガム大学の研究チームは、機械学習と人工知能(AI)技術を活用し、結腸直腸癌(CRC)の診断に有望な3つの新しいタンパク質バイオマーカー(TFF3、LCN2、CEACAM5)を特定しました。これらのタンパク質は、細胞接着や炎症など、癌の発生と密接に関連するプロセスに関与しています。この発見は、CRCの早期診断ツールの開発に貢献する可能性がありますが、さらなる大規模な検証研究が必要とされています。CRCは英国で4番目に一般的な癌であり、年間約44,100人が診断されています。早期発見は治療効果を高めるため、信頼性の高い診断ツールの開発が急務とされています。

<関連情報>

UKバイオバンクのデータを用いた大腸がんにおけるプロテオームマーカーの機械学習による同定
Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data

Swarnima Kollampallath Radhakrishnan,Dipanwita Nath^1,ominic Russ,Laura Bravo Merodio,Priyani Lad,Folakemi Kola Daisi,Animesh Acharjee
Frontiers in Oncology Published:07 January 2025
DOI:https://doi.org/10.3389/fonc.2024.1505675

Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. This leads to the requirement for prognostic and diagnostic biomarker development. Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. Our methodology leverages an extensive collection of proteome profiles from both healthy and colorectal cancer individuals. To identify a potential biomarker with high predictive ability, we used three machine learning models. To enhance the interpretability of our models, we quantify each protein’s contribution to the model’s predictions using SHapley Additive exPlanations values. Three classifiers—LASSO, XGBoost, and LightGBM were evaluated for predictive performance along with hyperparameter tuning of each model using grid search, with LASSO achieving the highest AUC of 75% in the UK Biobank dataset and the AUCs for LightGBM and XGBoost are 69.61% and 71.42%, respectively. Using SHapley Additive exPlanations values, TFF3, LCN2, and CEACAM5 were found to be key biomarkers associated with cell adhesion and inflammation. Protein quantitative trait loci analyze studies provided further evidence for the involvement of TFF1, CEACAM5, and SELE in colorectal cancer, with possible connections to the PI3K/Akt and MAPK signaling pathways. By offering insights into colorectal cancer diagnostics and targeted therapeutics, our findings set the stage for further biomarker validation.