Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Zengin, Talip; Önal-Süzek, Tuğba

doi:10.3390/jpm11020154

Cited by 26 publications

(18 citation statements)

References 117 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gene mutation, including copy number variation (CNVs) and single-nucleotide polymorphism (SNPs), plays important roles in the development and progression of human cancers [ 44 , 45 ]. However, the effect of CNV and SNP in m6A-related genes remain unknown to us.…”

Section: Discussionmentioning

confidence: 99%

M6A-Related Bioinformatics Analysis Reveals a New Prognostic Risk Signature in Cutaneous Malignant Melanoma

Zhu

Wang

et al. 2022

Disease Markers

View full text Add to dashboard Cite

Cutaneous malignant melanoma (CMM) is the most deadly skin cancer worldwide. Despite advances in the treatments of CMM, its incidence and mortality rates are still increasing. N6-methyladenosine (m6A) is the most common form of RNA modification and has attracted increasing interest in cancer initiation and progression. However, the role of m6A regulators in CMM and their correlation with prognosis remain elusive. Here, we demonstrated that by applying consensus clustering, all CMM patient cases can be divided into two clusters based on overall expression levels of 25 m6A genes. We systematically analyzed the prognostic value of the 25 m6A RNA methylation regulators in CMM and found that ELAVL1, ABCF1, and IGF2BP1 yield the highest scores for predicting the prognosis of CMM. Accordingly, we derived a risk signature consisting of three selected m6A genes as an independent prognostic marker for CMM and validated our findings with data derived from a different CMM cohort. Next, we determined that CNVs in m6A genes had a significant negative impact on patient survival. The mRNA expression levels of m6A genes were correlated with CNV mutation. Moreover, in the selected three risk signature m6A regulators, GSEA analysis showed that they were closely correlated with inflammation and immune pathways. TME analysis proved that m6A gene expressions were negatively correlated with immune cell infiltration. In conclusion, m6A regulators are vital participants in CMM pathology; and ELAVL1, ABCF1, and IGF2BP1 mRNA levels are valuable factors for prognosis prediction and treatment strategy development.

show abstract

Section: Discussionmentioning

confidence: 99%

M6A-Related Bioinformatics Analysis Reveals a New Prognostic Risk Signature in Cutaneous Malignant Melanoma

Zhu

Wang

et al. 2022

Disease Markers

View full text Add to dashboard Cite

show abstract

“…Recently, MGA was found to mutate frequently in multiple types of cancers, thus providing a novel insight into the function of MGA ( De Paoli et al, 2013 ; Cancer Genome, Atlas Research, and Network, 2014 ; Jo et al, 2016 ; Zhang et al, 2018 ). Zengin and Önal-Süzek (2021) reported that MGA was a driver gene in LUAD but not in LUSC. Nonetheless, the specific role of MGA mutation in cancer is unclear, and the role of MGA mutation in ICI treatment outcome was not determined.…”

Section: Discussionmentioning

confidence: 99%

Predictive Value of Max’s Giant Associated Protein Mutation in Outcomes of Lung Adenocarcinoma Patients Treated With Immune Checkpoint Inhibitors

Qü

Wang

Liu

et al. 2021

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Treatment with immune checkpoint inhibitors (ICIs) has considerably improved prognosis in multiple cancers. However, regardless of PD-L1 expression and TMB, better predictive biomarkers are required to identify ICI-responsive patients. We analyzed a pan-cancer cohort as the discovery cohort to identify the role of Max’s giant associated protein (MGA) mutation in the outcome of ICI treatment in different types of cancers. A pooled lung adenocarcinoma (LUAD) cohort was considered as the validation cohort. Another two LUAD cohorts who received conventional treatment were included for prognostic analysis and mechanism exploration. In the discovery cohort, MGA mutation was a favorable survival biomarker for patients with LUAD than in those with other types of cancers. MGA mutation was positively correlated with the TMB score. The results of the validation cohort were consistent with those of the discovery cohort. Patients with MGA mutation in the TMB-low subgroup had longer survival. Two LUAD cohorts who received standard treatment showed that the MGA mutation was not a prognostic biomarker for standard treatment. Mechanically, we found that the co-mutant genes did not affect the prognostic role of MGA mutation. Gene-set enrichment analysis revealed that genes belonging to the immunodeficiency pathway were enriched in the MGA wild-type group in LUAD. Moreover, activated NK cells were more enriched in the MGA mutant LUAD group. In conclusion, our results demonstrated that MGA mutation was an independent predictive biomarker for ICI therapy. These results may provide a novel insight into identifying potential patients with LUAD for ICI therapy.

show abstract

“…Although addition of the top 10 most highly mutated somatic driver genes to the classification model did not improve the performance of LUAD patients, they vastly improved the classification model of LUSC patients (Table 8). Top 10 somatically mutated genes of LUSC patients reported in our previous publication were CDKN2A, CSMD3, FAT1, KEAP1, KMT2C, KMT2D, NF1 NFE2L2, PIK3CA, TP53 [16]. The correlation between the risk of the patient (high vs low) and the given ten genes, the CSMD3 gene, impacted the model most by improving both the Logistic Regression and Random Forest performance in LUSC (Figure 12 and Figure 13).…”

Section: Somatically Mutated Genesmentioning

confidence: 95%

Integration of Clinicopathological And Genomic Features To Predict The Risk Stratification of TCGA Lung Adenocarcinoma And Lung Squamous Cell Carcinoma Patients

Sakman

Zengin

Önal-Süzek

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Predicting lung adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC) risk cohorts is a crucial step in precision oncology. Currently, clinicians and patients are informed about the patient's risk group via staging in the clinic. Several machine learning approaches have been carried out on the stratification of LUAD and LUSC patients, but there is no study assessing the integrated training of both clinical data and genetic data of these two lung cancer types. Methods: We initially implemented five different machine learning algorithms (Support Vector Machine, Logistic Regression, Naive Bayes, Random Forest, and K Neighbors Classifiers) to evaluate the clinical and mutated genes of patients to develop a prognostic relevance model to classify LUAD and LUSC patients into high-risk and low-risk groups. Results: We identified a list of clinical features and somatically mutated genes that may be used to evaluate the prognosis of LUAD and LUSC patients for patient risk stratification in a clinical setting. As a result of this analysis, new genes such as KEAP1 for LUAD and CSMD3 for LUSC with others can be added to clinical decision processes. Conclusions: In current clinical practice, clinicians, and patients are informed about the patient's risk group only with cancer staging. With the feature set we propose, clinicians and patients can assess the risk group of their patients according to the patient-specific clinical and molecular parameters. Our machine learning model may serve as a practical and reliable prognosis predictive tool for LUAD and LUSC and could provide novel insights into the understanding of the underlying clinical and molecular mechanisms of LUAD and LUSC. Keywords: Machine Learning, Lung Adenocarcinoma, Lung Squamous Cell Carcinoma, Prognosis Prediction Model, TCGA, Multi-omics, Data Integration

show abstract

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Cited by 26 publications

References 117 publications

M6A-Related Bioinformatics Analysis Reveals a New Prognostic Risk Signature in Cutaneous Malignant Melanoma

M6A-Related Bioinformatics Analysis Reveals a New Prognostic Risk Signature in Cutaneous Malignant Melanoma

Predictive Value of Max’s Giant Associated Protein Mutation in Outcomes of Lung Adenocarcinoma Patients Treated With Immune Checkpoint Inhibitors

Integration of Clinicopathological And Genomic Features To Predict The Risk Stratification of TCGA Lung Adenocarcinoma And Lung Squamous Cell Carcinoma Patients

Contact Info

Product

Resources

About