DNAH10 mutation correlates with cisplatin sensitivity and tumor mutation burden in small-cell lung cancer

Li, Man; Lin, Anqi; Luo, Peng; Shen, Weitao; Xiao, Dan; Gou, Lan-ying; Zhang, Jian; Guo, Linlang

doi:10.18632/aging.102683

Cited by 26 publications

(21 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, our previous study demonstrated that cancer cells with high TMB are associated with higher IC50 values (21), which is consistent with our current conclusion, suggesting that high TMB may also be a mechanism of cisplatin resistance.…”

Section: Discussionsupporting

confidence: 93%

See 1 more Smart Citation

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

Gao

Lyu

Zhou

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Lung cancer, mainly including lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer, is the cancer with the highest incidence and cancer-related mortality in the world. Platinum-based chemotherapy plays an important role in the treatment of various lung cancer subtypes, but not all patients can benefit from it, so it is worth identifying lung cancer patients who are resistant or insensitive.Method: The drug response and sequencing data of 170 lung cancer cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database, and support vector machines (SVMs) and beam search were used to select an optimal gene panel that can predict the sensitivity of cell lines to cisplatin. Then, we used the available cell line data to explore the potential mechanisms.Result: In this study, SVMs and beam search were used to screen a 9-gene panel related to lung cancer cell line resistance to cisplatin, with an area under the curve (AUC) of 0.873 ± 0.004. The natural logarithm of the half maximal inhibitory concentration (lnIC50) values of the panel-MT group were significantly higher than those of the panel-WT group, regardless of whether lung cancer subtype was considered. In addition, we found that the differentially expressed pathways between the two groups may explain the difference.Conclusion: In this study, we found that a panel including nine genes (PLXNC1, KIAA0649, SPTBN4, SLC14A2, F13A1, COL5A1, SCN2A, PLEC, and ALMS1) can accurately predict sensitivity to cisplatin, which may provide individualized treatment recommendations to improve the prognosis of patients with lung cancer.

show abstract

Section: Discussionsupporting

confidence: 93%

“…ERBB amplification (18), oncogene EGFR (19) and FLT3 mutations (20) are also sensitive to their target inhibitors. Recent studies have found that new gene mutations are associated with chemotherapy sensitivity and patient prognosis (21,22).…”

Section: Introductionmentioning

confidence: 99%

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

Gao

Lyu

Zhou

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The distributions of the risk score, survival time, and expression profiles were shown in Additional file 3 : Figure S2A–C. Overall tumor mutation burden (TMB) reflects the number of mutations in tumor cells and is a quantifiable biomarker [ 24 ]. We have calculated the TMB of the melanoma genome and explored the correlation between risk score and TMB (Additional file 3 : Figure S2D).…”

Section: Resultsmentioning

confidence: 99%

Effects of RNA methylation N6-methyladenosine regulators on malignant progression and prognosis of melanoma

Liu

Zhou

et al. 2021

Cancer Cell Int

View full text Add to dashboard Cite

Background Melanoma is an extremely aggressive type of skin cancer and experiencing a expeditiously rising mortality in a current year. Exploring new potential prognostic biomarkers and therapeutic targets of melanoma are urgently needed. The ambition of this research was to identify genetic markers and assess prognostic performance of N6-methyladenosine (m6A) regulators in melanoma. Methods Gene expression data and corresponding clinical informations of melanoma patients as well as sequence data of normal controls are collected from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Quantitative real-time PCR (qRT-PCR) analysis was carried out to detect the RNA expression of IGF2BP3 in A375 cell line, melanoma tissues, and normal tissues. Western blot, cell proliferation, and migration assays were performed to assess the ability of IGF2BP3 in A375 cell line. Results Differently expressed m6A regulators between tumor samples and normal samples were analyzed. A three-gene prognostic signature including IGF2BP3, RBM15B, and METTL16 was constructed, and the risk score of this signature was identified to be an independent prognostic indicator for melanoma. In addition, IGF2BP3 was verified to promote melanoma cell proliferation and migration in vitro and associate with lymph node metastasis in clinical samples. Moreover, risk score and the expression of IGF2BP3 were positively associated with the infiltrating immune cells and these hub genes made excellent potential drug targets in melanoma. Conclusion We identified the genetic changes in m6A regulatory genes and constructed a three-gene risk signature with distinct prognostic value in melanoma. This research provided new insights into the epigenetic understanding of m6A regulators and novel therapeutic strategies in melanoma.

show abstract

“…Previous studies on CDDP resistance have mainly focus on drug accumulation, DNA damage repair, key signaling pathways and the tumor microenvironment (TME) (Chen and Chang, 2019). In addition, cancer stem cells play an important role in CDDP resistance and tumor progression (Li et al, 2020;Qiu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Identification of Mutations Related to Cisplatin-Resistance and Prognosis of Patients With Lung Adenocarcinoma

Liu

Fang

et al. 2020

Front. Pharmacol.

View full text Add to dashboard Cite

Background: Lung adenocarcinoma (LUAD) is the most common histologic type of nonsmall cell lung cancer (NSCLC; approximately 60%), and platinum-based chemotherapy is the cornerstone of the treatment for patients with LUAD. However, a considerable number of patients experience tumor recurrence after developing cisplatin (cisdiamminedichloroplatinum(II) or CDDP) resistance. Therefore, it is particularly important to screen primary CDDP-resistant LUAD patient populations, which can maximize the clinical benefits for these patients. Methods: Data for 61 LUAD cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database to screen for mutations related to CDDP susceptibility, and we conducted whole-exome sequencing (WES) of tumors from 45 LUAD patients from Zhujiang Hospital of Southern Medical University. Subsequently, the clinical prognostic value of these mutations was verified by using The Cancer Genome Atlas (TCGA)-LUAD cohort and our cohort (n 45). Results: Based on drug sensitivity data for the GDSC-LUAD cell lines and survival analysis of the cohorts TCGA-LUAD and Local-LUAD, we found only one gene (GREB1) with mutations related to decreased CDDP sensitivity as well as worse overall survival (OS) and progression-free survival (PFS) [OS: log-rank p 0.038, hazard ratio (HR; 95% confidence interval (95% CI)): 2.19 (0.73-6.55); PFS: log-rank p 0.001; HR: 4.65, 95% CI: 1.18-18.37]. The GREB1-mutant (GREB1-MT) group had a higher frequency of gene mutations. Additionally, gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) suggested reduced accumulation of intracellular drugs in the GREB1-MT group, in addition to increased drug efflux and enhanced DNA damage repair and intracellular detoxification. Conclusion: This study found that GREB1 mutations may mediate the primary resistance and clinical prognosis of LUAD patients undergoing treatment with CDDP. Further functional analysis showed that GREB1 mutations are related to the known mechanism of CDDP resistance. These results suggest that GREB1 mutations are potential biomarkers for screening of CDDP resistance among LUAD patients.

show abstract

DNAH10 mutation correlates with cisplatin sensitivity and tumor mutation burden in small-cell lung cancer

Cited by 26 publications

References 41 publications

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer

Effects of RNA methylation N6-methyladenosine regulators on malignant progression and prognosis of melanoma

Identification of Mutations Related to Cisplatin-Resistance and Prognosis of Patients With Lung Adenocarcinoma

Contact Info

Product

Resources

About