| Literature DB >> 34758859 |
Zhihui Zhang1, Chaoqi Zhang1, Zhaoyang Yang2, Guochao Zhang1, Peng Wu1, Yuejun Luo1, Qingpeng Zeng1, Lide Wang1, Qi Xue1, Yi Zhang3, Nan Sun4, Jie He5.
Abstract
Small-cell lung cancer (SCLC) is a devastating subtype of lung cancer with few therapeutic options. Despite the advent of immunotherapy, platinum-based chemotherapy is still the irreplaceable first-line therapy for SCLCs. However, drug resistance will invariably occur in most patients and the outcomes are heterogeneous. Therefore, clinically feasible classification strategies and potential therapeutic targets for overcoming chemotherapy resistance are urgently needed. N6-methyladenosine (m6A) is a novel epigenetic decisive factor that is involved in tumor progression and drug resistance. However, almost nothing is known about m6A modification in SCLC. Here, we assessed 200 SCLC samples from patients who underwent chemotherapy from three different cohorts, including a validation cohort containing 71 cases with qPCR data and an independent cohort containing 79 cases with immunohistochemistry data (quantified as H-score). We systematically characterized the predictive landscape of m6A regulators in SCLC patients following with chemotherapy. Using the LASSO Cox model, we built a seven-regulator-based (ZCCHC4, IGF2BP3, ALKBH5, YTHDF3, METTL5, G3BP1, and RBMX) chemotherapy benefit predictive classifier (m6A score) and subsequently validated the classifier in two other cohorts. Time-dependent ROC and C-index analyses showed that the m6A score to possessed superior predictive power for chemotherapy benefit in comparison with other clinicopathological parameters. A multicohort multivariate analysis revealed that the m6A score is an independent factor that affects survival benefit across multiple cohorts. Our in vitro experimental results revealed that three regulators-ZCCHC4, G3BP1, and RBMX-may serve as promising novel therapeutic targets for overcoming chemoresistance in SCLCs. Our results, for the first time, demonstrate the predictive significance of m6A regulators for chemotherapy benefit, as well as their potential as therapeutic targets for overcoming chemotherapy resistance in SCLC patients. The m6A score was found to be a reliable prognostic tool that may help guide chemotherapy decisions for patients with SCLC.Entities:
Keywords: Chemotherapy resistance; Epigenetic modification; Individualized medicine; Small-cell lung cancer; m6A regulators
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Year: 2021 PMID: 34758859 PMCID: PMC8579518 DOI: 10.1186/s13045-021-01173-4
Source DB: PubMed Journal: J Hematol Oncol ISSN: 1756-8722 Impact factor: 17.388
Fig. 1Identification of m6A regulators as predictive biomarkers and potential therapeutic targets in small-cell lung cancer with chemotherapy. a The work flow of this study. Thirty m6A regulators were selected from several recently published studies. The predictive regulators were filtered out through Kaplan–Meier curve analysis. The m6A score was constructed using the LASSO Cox regression model and the training cohort. The m6A score was validated in two different cohorts with qPCR data and immunohistochemistry data. Finally, the therapeutic potential of several regulators was explored through in vitro experiments. b A forest plot of the optimum cutoff survival analysis of the m6A regulators in SCLC patients from the training cohort who underwent chemotherapy. c The LASSO model was selected to determine the partial likelihood deviance of different numbers of variables, and 100-fold cross-validation was chosen. d Distribution of the LASSO coefficients of 15 significant regulators. e Distribution of the seven regulators comprising the m6A score, the corresponding m6A score, and survival status in the training cohort. f Survival curve of OS for patients from the training cohort. g Time-dependent ROC curves comparing the prognostic accuracy of the m6A score with other clinicopathological parameters at 5 years in the training cohort. h Distribution of the seven regulators comprising the m6A score, the corresponding m6A score, and survival status in the validation cohort with qPCR data. i Survival curve of OS for patients from the validation cohort. j Survival curve of RFS for patients from the validation cohort. k Time-dependent ROC curves comparing the prognostic accuracy of the m6A score with other clinicopathological parameters at 5 years in the validation cohort. l Representative immunohistochemistry images of the seven regulators comprising the m6A score from the tissue microarray; (+) high expression, (−) low expression (40 ×). m Distribution of the seven regulators comprising the m6A score, the corresponding m6A score, and survival status in the independent cohort. n Survival curve of OS for patients from the independent cohort. o Survival curve of RFS for patients from the independent cohort. p Time-dependent ROC curves comparing the prognostic accuracy of the m6A score with other clinicopathological parameters at 5 years in the independent cohort. q Univariate Cox regression analysis of clinicopathological factors and the m6A score for OS in patients across multiple cohorts. r Multivariate Cox regression analysis of clinicopathological factors and the m6A score for OS in patients across multiple cohorts. s Distribution of the four selected regulators in normal lung and SCLC tissues from GSE40275. t, x Knocking down the expression of G3BP1 significantly increased the sensitivity of SCLC cells to cisplatin. u, y Knocking down the expression of ZCCH4 significantly increased the sensitivity of SCLC cells to cisplatin. v, z Knocking down the expression of METTL5 had no effect on the sensitivity of SCLC cells to cisplatin. w, aa Knocking down the expression of RBMX significantly increased the sensitivity of SCLC cells to cisplatin