ABSTRACT
Objective·To explore the immune-related characteristics of non-small cell lung cancer(NSCLC),discover potential tumor markers in V-J genes,and lay the foundation for establishing a TCR-antigen recognition prediction model.Methods·A total of 704 NSCLC samples were collected to establish a comprehensive T-cell receptor(TCR)repertoire analysis workflow.The upstream analysis included steps such as raw data processing,quality control,filtering,TCR sequence identification,and extraction.The downstream analysis included repertoire clone distribution,clone typing,V-J gene sharing,CDR3 distribution characteristics,and clone tracking.The sample clone distribution was analyzed by using indices such as Shannon-Weiner index and Chaol index.Clone typing was performed based on the number of clone amplifications to explore differences among different types.The degree of V-J gene segment sharing was analyzed,and the sharing of low-frequency clone types was determined through clone amplification weight analysis of V-J genes by using two samples of papillary thyroid carcinoma.Finally,analysis of the distribution characteristics of V genes and high-frequency clone type CDR3,and clone tracking analysis were conducted to monitor changes in tumor immune clone frequencies before and after analysis,aiming to identify potential tumor markers.Results·① Significant differences were observed in clone distribution and clone typing among different NSCLC tissues,as well as among different ages and genders.② Specific highly-shared V-J genes were identified in the analysis of V-J gene sharing,and non-normal distribution of high-clone V genes and amino acid high-frequency clone types were found in the CDR3 distribution analysis.③ In the analysis of high-frequency clone type clone tracking,highly expressed or newly expressed high-frequency clone types were observed in NSCLC,suggesting that these clone types could serve as potential tumor-associated antigens or bind with CDR3 reference sequences of new antigens.④ It was found that the expression frequency of TRBJ2-5 gene,originally low-expressed,significantly increased,indicating its potential role as a key low-frequency gene in tumor immune response.Conclusion·The TRAV21 and TRBV6.5 genes show high clone amplification in NSCLC and could serve as potential tumor biomarkers.
ABSTRACT
Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer (NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.
Subject(s)
Humans , Algorithms , Carcinoma, Non-Small-Cell Lung , Diagnosis , Lung Neoplasms , Diagnosis , Machine Learning , PrognosisABSTRACT
Objective@#To explore the value of CT texture analysis (CTTA) in differentiating the pathological grade of urothelial carcinoma of the bladder (UCB).@*Methods@#A total of 53 lesions from 43 patients with bladder cancer confirmed by postoperative pathology were retrospectively analyzed, including 27 cases of high-grade urothelial carcinoma (HGUC) and 26 cases of low-grade urothelial carcinoma (LGUC). All the patients took pelvic CT and enhanced scanning in the same CT scanner with same scanning parameters. Lesions on both plain and enhanced CT images were delineated on software by two radiologists to extract the corresponding volumes of interest (VOI) and then 92 parameters based on feature classes were generated. The average values of two radiologists were obtained. The difference parameters between HGUC group and LGUC group were screened by nonparametric test, and the receiver operating characteristic (ROC) was drawn. The corresponding optimal thresholds were determined and diagnostic effect was assessed.@*Results@#Nine difference texture parameters between HGUC group and LGUC group were selected, including 5 parameters on unenhanced images, namely, skewness, root mean squared, cluster shade, zone percentage and large area high gray level emphasis. There were 4 parameters on enhanced images, namely, skewness, kurtosis, cluster shade and zone percentage. The largest area under curve of 0.840±0.058 (95% CI 0.726-0.955) was obtained from skewness generated by VOI of unenhanced images. The cut-off value of skewness was 0.186 5, which permitted the diagnosis of HGUC with sensitivity of 92.59%, specificity of 73.08%, positive predictive value of 78.13%, negative predictive value of 90.48% and accuracy of 83.02%.@*Conclusion@#CTTA can effectively distinguish between LGUC and HGUC. Skewness from unenhanced CT images had the optimal diagnostic performance.