Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape

Pei, Dongchen; Zong, Yuan; Fan, Yanting; Wei, Shuai; Xing, Zhisong; Song, Shasha; Wang, Xin; Gao, Xingcai

doi:10.3389/fonc.2023.1199608

Cited by 6 publications

(1 citation statement)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is well known that the immune system plays a role in the development of lung cancer and the prognostic process. In the study by Zhu et al (2023) , the immune infiltration of LUAD was investigated. It was found that 12 hub genes, including HBA2 , may be involved in LUAD progression via immune-related signaling pathways.…”

Section: Discussionmentioning

confidence: 99%

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

ÖZER,

ÖZBEK SARICA,

ARĞA

2023

Turkish Journal of Biology

View full text Add to dashboard Cite

Background/aim The complicated nature of tumor formation makes it difficult to identify discriminatory genes. Recently, transcriptome-based supervised classification methods using support vector machines (SVMs) have become popular in this field. However, the inclusion of less significant variables in the construction of classification models can lead to misclassification. To improve model performance, feature selection methods such as enrichment analysis can be used to extract useful variable sets. The detection of genes that can discriminate between normal and tumor samples in the association of cancer and disease remains an area of limited information. We therefore aimed to discover novel and practical sets of discriminatory biomarkers by utilizing the association of cancer and disease. Materials and methods In this study, we employed an SVM classification method for differentially expressed genes enriched by Disease Ontology and filtered nondiscriminatory features using Wilk’s lambda criterion prior to classification. Our approach uses the discovery of disease-associated genes as a viable strategy to identify gene sets that discriminate between tumor and normal states. We analyzed the performance of our algorithm using comprehensive RNA-Seq data for adenocarcinoma of the colon, squamous cell carcinoma of the lung, and adenocarcinoma of the lung. The classification performance of the obtained gene sets was analyzed by comparison with different expression datasets and previous studies using the same datasets. Results It was found that our algorithm extracts stable small gene sets that provide high accuracy in predicting cancer status. In addition, the gene sets generated by our method perform well in survival analyses, indicating their potential for prognosis. Conclusion By combining gene sets for both diagnosis and prognosis, our method can improve clinical applications in cancer research. Our algorithm is available as an R package with a graphical user interface in Bioconductor ( https://doi.org/10.18129/B9.bioc.SVMDO ) and GitHub ( https://github.com/robogeno/SVMDO ).

show abstract

Section: Discussionmentioning

confidence: 99%

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

ÖZER,

ÖZBEK SARICA,

ARĞA

2023

Turkish Journal of Biology

View full text Add to dashboard Cite

show abstract

OCIAD2 promotes pancreatic cancer progression through the AKT signaling pathway

Yin,

Jia,

Yao

et al. 2024

Gene

View full text Add to dashboard Cite

Development of a disulfidptosis-related lncRNA prognostic signature for enhanced prognostic assessment and therapeutic strategies in lung squamous cell carcinoma

Zhu,

Zong,

Gao

2024

Sci Rep

View full text Add to dashboard Cite

Limited treatment options and poor prognosis present significant challenges in the treatment of lung squamous cell carcinoma (LUSC). Disulfidptosis impacts cancer progression and prognosis. We developed a prognostic signature using disulfidptosis-related long non-coding RNAs (lncRNAs) to predict the prognosis of LUSC patients. Gene expression matrices and clinical information for LUSC were downloaded from the TCGA database. Co-expression analysis identified 209 disulfidptosis-related lncRNAs. LASSO-Cox regression analysis identified nine key lncRNAs, forming the basis for establishing a prognostic model. The model’s validity was confirmed by Kaplan–Meier and ROC curves. Cox regression analysis identified the risk score (RS) as an independent prognostic factor inversely correlated with overall survival. A nomogram based on the RS demonstrated good predictive performance for LUSC patient prognosis. The relationship between RS and immune function was explored using ESTIMATE, CIBERSORT, and ssGSEA algorithms. According to the TIDE database, a negative correlation was found between RS and immune therapy responsiveness. The GDSC database revealed that 49 drugs were beneficial for the low-risk group and 25 drugs for the high-risk group. Silencing C10orf55 expression in SW900 cells reduced invasiveness and migration potential. In summary, this lncRNA model based on TCGA-LUSC data effectively predicts prognosis and assists clinical decision-making.

show abstract

Comprehensive analysis to identify a novel diagnostic marker of lung adenocarcinoma and its immune infiltration landscape

Cited by 6 publications

References 49 publications

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

SVM-DO: identification of tumor-discriminating mRNA signatures via support vectormachines supported by disease ontology

OCIAD2 promotes pancreatic cancer progression through the AKT signaling pathway

Development of a disulfidptosis-related lncRNA prognostic signature for enhanced prognostic assessment and therapeutic strategies in lung squamous cell carcinoma

Contact Info

Product

Resources

About