Disulfidptosis­related lncRNA prognosis model to predict survival therapeutic response prediction in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, and disulfidptosis is a newly discovered mechanism of programmed cell death. However, the effects of disulfidptosis-related lncRNAs (DR-lncRNAs) in LUAD have yet to be fully elucidated. The aim of the present study was to identify and validate a novel lncRNA-based prognostic marker that was associated with disulfidptosis. RNA-sequencing and associated clinical data were obtained from The Cancer Genome Atlas database. Univariate Cox regression and lasso algorithm analyses were used to identify DR-lncRNAs and to establish a prognostic model. Kaplan-Meier curves, receiver operating characteristic curves, principal component analysis, Cox regression, nomograms and calibration curves were used to assess the reliability of the prognostic model. Functional enrichment analysis, immune infiltration analysis, somatic mutation analysis, tumor microenvironment and drug predictions were applied to the risk model. Reverse transcription-quantitative PCR was subsequently performed to validate the mRNA expression levels of the lncRNAs in normal cells and tumor cells. These analyses enabled a DR-lncRNA prognosis signature to be constructed, consisting of nine lncRNAs; U91328.1, LINC00426, MIR1915HG, TMPO-AS1, TDRKH-AS1, AL157895.1, AL512363.1, AC010615.2 and GCC2-AS1. This risk model could serve as an independent prognostic tool for patients with LUAD. Numerous immune evaluation algorithms indicated that the low-risk group may exhibit a more robust and active immune response against the tumor. Moreover, the tumor immune dysfunction exclusion algorithm suggested that immunotherapy would be more effective in patients in the low-risk group. The drug-sensitivity results showed that patients in the high-risk group were more sensitive to treatment with crizotinib, erlotinib or savolitinib. Finally, the expression levels of AL157895.1 were found to be lower in A549. In summary, a novel DR-lncRNA signature was constructed, which provided a new index to predict the efficacy of therapeutic interventions and the prognosis of patients with LUAD.

Clinical Study of Artificial Intelligence in Imaging Diagnosis of False Positive Lesions of Pulmonary Nodules.

OBJECTIVE: To determine the accuracy of diagnosis of pulmonary nodules using artificial intelligence method. STUDY DESIGN: Observational study. Place and Duration of the Study: Department of Thoracic Surgery, Jinan Central Hospital, Jinan, China, from January 2020 to May 2021. METHODOLOGY: An analysis of clinical characteristics exhibited by 32 patients initially diagnosed with malignant tumours through imaging (LDCT) and artificial intelligence (AI), was reclassified as having benign lesions following surgical intervention. Quantitative parameters were assessed, including CT mean value, kurtosis, skewness, solid ratio, and the ratio of length to short diameter, within a cohort of 32 benign patients juxtaposed with 58 patients diagnosed with lung cancer during the same time frame. The AI-derived parameters were subjected to Mann-Whitney U non-parametric test. RESULTS: A total of 32 benign pulmonary lesions were evaluated that were initially misdiagnosed as malignant prior to surgery. These lesions displayed an average length of (18.56 ± 12.16) mm, with the majority characterised as solid (68.8%). Notably, a substantial proportion of these lesions exhibited imaging features akin to malignant growths. The AI-derived quantitative parameters of the 32 benign cases and the 58 malignant cases revealed statistical significance in average CT value and solid ratio. However, statistical significance was not established for kurtosis, skewness, or the ratio of length to short diameter. The area under the Receiver Operating Characteristic (ROC) curve for average CT value and solid ratio stood at 0.71 and 0.705, respectively. CONCLUSION: Among the cases initially misdiagnosed as malignant yet subsequently identified as benign, a notable number of these instances were solid nodules, often resembling malignant lesions in imaging characteristics. There was moderate discriminatory capacity for average CT value and solid ratio, rendering them valuable tools for distinguishing between benign and malignant lesions within this particular cohort. This underscores their high diagnostic significance. KEY WORDS: Artificial intelligence, Benign lesions of lung, Lung cancer, Quantitative parameters, Postoperative.

Establishment and Validation of a Novel Nomogram for Predicting Distant Metastasis in Patients with Invasive Lung Adenocarcinoma.

OBJECTIVE: To establish and verify a nomogram for predicting distant metastasis in invasive lung adenocarcinoma (IAC). STUDY DESIGN: Observational study. PLACE AND DURATION OF STUDY: Department of Thoracic Surgery, Jinan Central Hospital, Jinan, China, from December 2021 to May 2022. METHODOLOGY: To create a nomogram, univariate and multivariate logistic regression analyses were used to identify the independent predictors of distant metastasis. The calibration, discrimination, and clinical performance of the nomogram were tested by calibration plots, area under receiver operating characteristic curve (AUC), and decision curve analysis (DCA). RESULTS: Age at diagnosis (<70 years), histological type (invasive mucinous adenocarcinoma), T stage, N stage, surgical approach, and lymph node dissection were independent predictors for the development of nomogram. Compared with the American Joint Committee on Cancer-8th edition staging system, AUC showed that this prediction model has a higher predictive performance (training set: 0.922 vs. 0.790; verification set: 0.919 vs. 0.779). In addition, the overall survival time (OS) of IAC patients was meaningfully different among the three groups of different risks stratified based on model score (p <0.001). CONCLUSION: The prediction model constructed according to factors such as histological type and surgical approach in this study can accurately predict distant metastasis in IAC patients and define high-risk patients according to nomogram score. KEY WORDS: Invasive adenocarcinoma IAC, Distant metastasis, Nomogram, Surveillance, Epidemiology and end results SEER.