Background. More and more evidence has shown that immune-related long noncoding ribonucleic acid (irlncRNAs) is a potential prognostic factor for colon cancer. The relevant gene pair pattern can improve the sensitivity of the prognostic model. Therefore, our present study aimed to identify irlncRNA Pairs and construct and validate a new prognostic signature in colon cancer. Methods. We downloaded the expression matrix of mRNA and lncRNA of patients with colon cancer and their clinical information from the public TCGA database. We obtained immune genes from the ImmPort database. Coexpression analysis was performed to identify irlncRNAs. We built an irlncRNA pair matrix by comparing the expression levels of each lncRNA pair in a cycle. Univariate Cox regression analysis, LASSO penalized regression analysis, and multivariate Cox regression analysis were performed to determine the final variables to construct the prognostic risk score model (a new signature). We draw the receiver operating characteristic (ROC) curves of the signature and clinical characteristics and determine the optimal cutoff value by the optimal Akaike Information Criterion (AIC) value. Based on the optimal cutoff value of the ROC curve of the signature, colon cancer patients were divided into the high- and low-risk groups. Then, the signature was evaluated by clinicopathological features, tumor-infiltrating immune cells, checkpoint-related biomarkers, targeted therapy, and chemotherapy. Results. We identified 8 lncRNA pairs including AC103740.1|LEF1-AS1, LINC02391|AC053503.5, WWC2-AS2|AL355916.2, AC104090.1|NEURL1-AS1, AC099524.1|AL161908.1, AC074011.1|AL078601.2, AL355916.2|LINC01723, and AP003392.4|LINC00598 from 71 differently expressed irlncRNAs. We constructed a prognostic risk score model (a new signature) using these optimal eight irlncRNA pairs. ROC curve analysis revealed that the highest AUC value of the signature was 0.776 at 1 year, with the optimal cutoff value of 1.283. Our present study also showed that the constructed signature could accurately identify adverse survival outcomes, prognostic clinicopathological features, and specify tumor invasion status. The expression of immune checkpoint-related genes and chemical drug sensitivity were related to different risk groups. Conclusion. In our present study, we constructed a new irlncRNA signature of colon cancer based on the irlncRNA pairs instead of the special expression level of lncRNA. We found this signature had not only good prognostic value but also certain clinical value, which might provide a new insight into the treatment and prognosis of colon cancer.

Background: Immunotherapy has significantly altered the treatment landscape for non-small cell lung cancer (NSCLC).However, there is no report on the prediction of overall survival (OS) of lung adenocarcinoma (LDAC) based on immune score. In this study,we aim to investigate the immune scores of the LADC and the prognosis-related factors and construct a nomogram for prognosis prediction.Methods:A total of 407 cases were included in the study.And the clinicopathological characteristics of patients with LADC and immune scores were download from TCGA database.We used Cox proportional hazards regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).Nomograms were framed from Cox models and internally validated by use of 1000 bootstrap.Model discrimination was assessed by using the concordance index (c-index) and the calibration curve.Results：Patients were divided into groups with low, moderate, or high Subgroups based on immune scores.This study shows that compared with patients with low and intermediate immune scores, only those with high immune scores had significantly improved OS (HR and 95% confidence interval[CI]:0.488 [0.327‐0.730]).The C‐index for OS prediction was 0.707(95% CI, 0.664‐0.750) .The calibration curves for prediction of 3-years and 5-years OS probabilities demonstrated good calibration and discrimination.Conclusions：High immune scores Subgroup is very significantly correlated with better OS in patients with LADC. Moreover, the nomograms for predicting prognosis may help to assess the survival of patients with LADC.