Dataset Information

Recognition and combination of multiple cell-death features showed good predictive value in lung adenocarcinoma.

ABSTRACT:

Background

Cell death is a key regulatory process in organisms and its study has become increasingly important in the field of cancer. While prior research has primarily centered on the individual pathways of cell death in cancer, there has been a lack of comprehensive investigation into the synergistic effects of multiple cell death pathways.

Methods

Genes related to autophagy, apoptosis, necroptosis, pyroptosis, and cuproptosis was selected, and patients' data was collected from The Cancer Genome Atlas (TCGA)project. Cell death features were identified using principal component analysis and combined to create a composite score. A scalable prediction model was then created using LASSO regression after a thorough assessment of the composite scores. The model was subsequently validated across multiple external datasets to establish its robustness and reliability.

Results

The cell death features effectively represented the gene expression patterns in the samples. The composite score well predicted prognosis, clinical stage, mutation, tumor microenvironment, and immunotherapy effectiveness. The model built on composite scores accurately predicted prognosis and immunotherapy effectiveness across multiple datasets. GJB2 was identified as a potential biomarker.

Conclusion

Models based on multiple cell death pathways have significant predictive power for prognosis and immunotherapy effectiveness in lung adenocarcinoma. This highlights the synergistic role of multiple cell death pathways in cancer development and offers a new perspective for cancer research.

SUBMITTER: Meng F

PROVIDER: S-EPMC10709390 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Recognition and combination of multiple cell-death features showed good predictive value in lung adenocarcinoma.

Meng Fanmao F Sun Xin X Guo Wei W Shi Yong Y Cheng Wenhui W Zhao Liang L

Heliyon 20231117 12

<h4>Background</h4>Cell death is a key regulatory process in organisms and its study has become increasingly important in the field of cancer. While prior research has primarily centered on the individual pathways of cell death in cancer, there has been a lack of comprehensive investigation into the synergistic effects of multiple cell death pathways.<h4>Methods</h4>Genes related to autophagy, apoptosis, necroptosis, pyroptosis, and cuproptosis was selected, and patients' data was collected from ...[more]

PMID: 38076144

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Project description:BackgroundSpread through air space (STAS) is a risk factor for disease recurrence in patients with stage IA lung adenocarcinoma (LUAD) who undergo limited resection. Preoperative prediction of STAS could help intraoperative surgical decision-making in small LUAD patients. The aim of the study was to evaluate the predictive value of radiological features on STAS in stage cIA LUAD.MethodsA case-control study was designed through retrospective analysis of the radiological features of patients who underwent curative surgery for LUAD with a clinical tumor size ≤3 cm. Univariable and multivariable analyses were used to identify the independent risk factors for STAS. The predicted probability of STAS was calculated by a specific formula. Receiver operating characteristic (ROC) curves were used to determine a cut-off value with appropriate specificity while maintaining high sensitivity for STAS positivity.ResultsSTAS was frequently observed in acinar predominant (P<0.001), micropapillary predominant (P<0.001) and solid predominant (P<0.001) tumors and was significantly associated with larger pT size (P<0.001), presence of micropapillary component (P<0.001), lymphovascular invasion (LVI) (P<0.001), visceral pleura invasion (VPI) (P<0.001), both N1 and N2 lymph node metastasis (P<0.001) and ALK rearrangement (P<0.001). STAS-positivity was significantly associated with the presence of cavitation (P=0.047), lobulation (P=0.009), air bronchogram (P<0.001), and vascular convergence (P=0.016) and was also associated with greater maximum tumor diameter (P<0.001), maximum solid component diameter (P<0.001), maximum tumor area (P<0.001), consolidation/tumor ratio (CTR) (P<0.001), tumor disappearance ratio (TDR) (P<0.001) and computed tomography (CT) value (P<0.001). Multivariable analysis showed that STAS was associated with air bronchogram (P=0.042), maximum tumor diameter (P=0.015), maximum solid component diameter (P=0.022) and CTR (P<0.001). The ROC curve showed that the area under the curve (AUC) was 0.726 in the model for predicting STAS, with a sensitivity and specificity of 95.2% and 36.8%, respectively.ConclusionsSTAS-positive LUAD was associated with air bronchogram, maximum tumor diameter, maximum solid component diameter and CTR. These radiological features could predict STAS with excellent sensitivity but inferior specificity.

Dataset Information

Recognition and combination of multiple cell-death features showed good predictive value in lung adenocarcinoma.

Background

Methods

Results

Conclusion

Publications

Recognition and combination of multiple cell-death features showed good predictive value in lung adenocarcinoma.

Similar Datasets

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