Hub Gene Screening and Prognostic Modeling of Lung Cancer: An Integrated Bioinformatics Study.

Background: One of the most frequent malignancies is lung carcinoma which poses heavy burden on the global health. The link among differentially expressed genes (DEGs) and lung cancer patients' clinical outcomes was still missing. In this study, we integrated transcriptome data with clinical data to investigate the relationship between them in lung carcinoma patients. Methods: To begin, DEGs were identified using the Gene Expression Omnibus (GEO) gene expression pattern (GSE180347). Then, these DEGs are being searched in the TCGA database using the DEGs collected in the preceding phase. The Kaplan-Meier plotter was then used to assess the predictive value of these DEGs in patients with lung cancer. Results: Our study revealed a total of 45 DEGs, 15 of which were up-regulated and 30 of which were down-regulated. These DEGs were mostly enriched in cytokine receptor binding and cytokine activity, according to GO enrichment analysis. These DEGs were mostly enriched in cytokine-cytokine receptor interaction, according to KEGG enrichment analysis. Based on the PPI network, which comprises of 12 DEGs, a major module was discovered. They are mostly interested in cytotoxicity mediated by natural killer cells. Among all 45 DEGs, the mutations of NCAM1 account for the most cases in TCGA database with a percentage above 15%. Among the 12 DEGs in the significant module, higher expression of FAS, GPR29, HAVCR2, and NCAM1 exhibits longer survival time with hazard ratio and 95% confident interval of 0.79 (0.69-0.89), 0.80 (0.70-0.90), 0.71 (0.60-0.84), and 0.73 (0.62-0.86), respectively. However, higher expression of FCGR3A and IFNG exhibits shorter survival time with hazard ratio and 95% confident interval of 1.50 (1.32-1.71) and 1.15 (1.02-1.31), respectively. Conclusion: Our results demonstrate significant correlation between some DEGs and the survival outcome in lung adenocarcinomas patients, providing a comprehensive bioinformatics study in anticipation of future molecular mechanisms and biomarker studies.

Predicting Tumor Mutational Burden From Lung Adenocarcinoma Histopathological Images Using Deep Learning.

Tumor mutation burden (TMB) is an important biomarker for tumor immunotherapy. It plays an important role in the clinical treatment process, but the gold standard measurement of TMB is based on whole exome sequencing (WES). WES cannot be done in most hospitals due to its high cost, long turnaround times and operational complexity. To seek out a better method to evaluate TMB, we divided the patients with lung adenocarcinoma (LUAD) in TCGA into two groups according to the TMB value, then analyzed the differences of clinical characteristics and gene expression between the two groups. We further explored the possibility of using histopathological images to predict TMB status, and developed a deep learning model to predict TMB based on histopathological images of LUAD. In the 5-fold cross-validation, the area under the receiver operating characteristic (ROC) curve (AUC) of the model was 0.64. This study showed that it is possible to use deep learning to predict genomic features from histopathological images, though the prediction accuracy was relatively low. The study opens up a new way to explore the relationship between genes and phenotypes.

[Whole-process Management of Treatment of Advanced ALK Positive Non-small Cell Lung Cancer: A Case Report].

BACKGROUND: Anaplastic lymphoma kinase (ALK) is an important therapeutic target for advanced non-small cell lung cancer (NSCLC). In recent years, with the emergence of several ALK tyrosine kinase inhibitors (TKI), the overall survival (OS) of ALK fusion positive patients is gradually extended. This paper reports the treatment of a late stage non-small cell lung cancer (NSCLC) patient with ALK fusion positive for more than 5 years, and analyzes the treatment process and effect evaluation, so as to provide experience for the follow-up treatment of patients. METHODS: The diagnosis and treatment process of a patient with advanced ALK fusion mutation positive lung cancer admitted to the third ward of Department of oncology, Chifeng hospital, Inner Mongolia on July 3, 2015 was retrospectively analyzed. RESULTS: A 42 years old male patient was admitted to our department on July 3, 2015 for "intermittent cough, chest tightness for 2 months, diagnosed with lung adenocarcinoma for 1 day". Imaging examination showed a space occupying lesion in the left lower lobe of the lung, accompanied by mediastinal lymphadenopathy and left encapsulated pleural effusion. Bronchoscopic pathology showed non-small cell carcinoma, and adenocarcinoma was tentatively suggested. DIAGNOSIS: left lower lobe adenocarcinoma T1bN2M1a stage IV. Fluorescence in situ hybridization (FISH) indicated the translocation of ALK (2p23) chromosome. After 2 cycles of docetaxel+cisplatin (DP) regimens chemotherapy, disease progression occurred, so we used 6 cycles of pemetrexed+carboplatin to apply combination chemotherapy, 4 cycles of pemetrexed monotherapy were used after that. The efficacy evaluation: PR. On April 9, 2016, the patient was treated with crizotinib. In August 2019, multiple intracranial metastases were found and whole brain radiotherapy was given. Since September 4, 2019, oral administration of nsatinib has been carried out. As of March 1, 2021, the patients were followed up well. CONCLUSIONS: The advanced ALK fusion positive lung adenocarcinoma patients, though the first-line and the second-line chemotherapy, and the follwing application of ALK-TKI treatment, has procured a total OS has reached 68 months, and the current follow-up is good.