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DNA methylome and transcriptome landscapes of cancer-associated fibroblasts reveal a smoking-associated malignancy index
Sheng-Fang Su, … , Chao-Chi Ho, Ker-Chau Li
Sheng-Fang Su, … , Chao-Chi Ho, Ker-Chau Li
Published July 6, 2021
Citation Information: J Clin Invest. 2021;131(16):e139552. https://doi.org/10.1172/JCI139552.
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Research Article Genetics Oncology Article has an altmetric score of 3

DNA methylome and transcriptome landscapes of cancer-associated fibroblasts reveal a smoking-associated malignancy index

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Abstract

Unlike the better-studied aberrant epigenome in the tumor, the clinicopathologic impact of DNA methylation in the tumor microenvironment (TME), especially the contribution from cancer-associated fibroblasts (CAFs), remains elusive. CAFs exhibit profound patient-to-patient tumorigenic heterogeneity. We asked whether such heterogeneity may be exploited to quantify the level of TME malignancy. We developed a robust and efficient methylome/transcriptome co-analytical system for CAFs and paired normal fibroblasts (NFs) from non–small-cell lung cancer patients. We found 14,781 CpG sites of CAF/NF differential methylation, of which 3,707 sites showed higher methylation changes in ever-smokers than in nonsmokers. Concomitant CAF/NF differential gene expression analysis pointed to a subset of 54 smoking-associated CpG sites with strong methylation-regulated gene expression. A methylation index that summarizes the β values of these CpGs was built for NF/CAF discrimination (MIND) with high sensitivity and specificity. The potential of MIND in detecting premalignancy across individual patients was shown. MIND succeeded in predicting tumor recurrence in multiple lung cancer cohorts without reliance on patient survival data, suggesting that the malignancy level of TME may be effectively graded by this index. Precision TME grading may provide additional pathological information to guide cancer prognosis and open up more options in personalized medicine.

Authors

Sheng-Fang Su, Hao Ho, Jia-Hua Li, Ming-Fang Wu, Hsu-Chieh Wang, Hsiang-Yuan Yeh, Shuenn-Wen Kuo, Huei-Wen Chen, Chao-Chi Ho, Ker-Chau Li

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