Background Currently, immune checkpoint blockers (ICB) and radiotherapy (RT) combination therapy is broadly applied in non‐small cell lung cancer (NSCLC) patients. However, meta‐analysis about safety and efficacy of RT + ICB versus ICB has not yet been reported. To evaluate safety and efficacy of the combination therapy of ICB and RT in patients with recurrent or metastatic NSCLC and explore factors related to higher response rates, longer lifetime, and lower toxicity, meta‐analysis of previous clinical data will be presented in this article. Methods A literature search on patients with recurrent or metastatic NSCLC treated with RT + ICB versus ICB was performed using the Cochrane Library, Embase and PubMed up to December 10, 2022. Suitable quality assessment checklists were selected corresponding to various types of research studies. Comparative and single‐arm studies were analyzed using Stata 14.0. Results 10 comparative studies and 15 arms of combination therapy were included for this meta‐analysis. RT significantly improved objective response rate (ORR), disease control rate (DCR), and overall survival (OS) and progression‐free survival (PFS) of ICB (I‐square value (I2) = 0.00%, odds ratio (OR) 1.28, 95% confidence interval (CI) 1.09–1.49, I2 = 0.00%, OR 1.12, 95% CI 1.00–1.25, I2 = 42.1%, OR 0.81, 95% CI 0.72–0.92, I2 = 34.5%, OR 0.80, and 95% CI 0.71–0.89, respectively). Toxicity between combination therapy and ICB monotherapy did not significantly differ in any grade or in ≥3 grade of tr‐AEs (I2 = 0.00%, OR 1.05, 95% CI 0.91–1.22, I2 = 0.00%, OR 1.46, 95% CI 0.90–2.37, respectively). Subgroup analyses based on single‐arm studies showed that applications of SRS/SBRT, PD‐1 inhibitor, and administration of ICB after RT were conducive to a better DCR, longer OS and mild adverse events (heterogeneity between groups (HBG) all p < 0.05). Conclusion RT can significantly improve ORR, DCR, OS, and PFS of ICB in patients with recurrent or metastatic NSCLC without increasing toxicity. PD‐1 inhibitor following SRS/SBRT could be the best option to maximally benefit the patients.

Background: Long non-coding RNA (LncRNA) is a prognostic factor for malignancies, and N7-Methylguanosine (m7G) is crucial in the occurrence and progression of tumors. However, it has not been documented how well m7G-related LncRNAs predict the development of breast cancer (BC). This study aims to develop a predictive signature based on long non-coding RNAs (LncRNAs) associated with m7G to predict the prognosis of breast cancer patients.Methods: The Cancer Genome Atlas (TCGA) database provided us with the RNA-seq data and matching clinical information of individuals with breast cancer. To identify the signature of N7-Methylguanosine-Related LncRNAs and create a prognostic model, we employed co-expression network analysis, least absolute shrinkage selection operator (LASSO) regression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. The signature was assessed using the Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) curve. A nomogram and principal component analysis (PCA) were employed to confirm the predictive signature’s usefulness. Then, we examined the drug sensitivity between the two risk groups and utilized single-sample gene set enrichment analysis (ssGSEA) to investigate the association between predictive factors and the tumor immune microenvironment in high-risk and low-risk groups.Results: Nine m7G-related LncRNAs (LINC01871, AP003469.4, Z68871.1, AC245297.3, EGOT, TFAP2A-AS1, AL136531.1, SEMA3B-AS1, AL606834.2) that are independently associated with the overall survival time (OS) of BC patients make up the signature we developed. For predicting 1-, 3-, and 5-year survival rates, the areas under the ROC curve (AUC) were 0.715, 0.724, and 0.726, respectively. The Kaplan-Meier analysis revealed that the prognosis of BC patients in the high-risk group was worse than that of those in the low-risk group. When compared to clinicopathological variables, multiple regression analysis demonstrated that risk score was a significant independent predictive factor for BC patients. The results of the ssGSEA study revealed a substantial correlation between the predictive traits and the BC patients’ immunological status, low-risk BC patients had more active immune systems, and they responded better to PD1/L1 immunotherapy.Conclusion: The prognostic signature, which is based on m7G-related LncRNAs, can be utilized to inform patients’ customized treatment plans by independently predicting their prognosis and how well they would respond to immunotherapy.

Background: Programmed cell death-1 (PD-1) blockade has been shown to confer clinical benefit in cancer patients. Here, we assessed the level of serum interleukin 14α (IL14α) in patients receiving anti-PD-1 treatment. Methods: This prospective study recruited 30 patients with advanced solid cancer who received pembrolizumab treatment in Northern Jiangsu People’s Hospital between April 2016 and June 2018. The western blot analysis was used to assess the expression level of serum IL14α in patients at baseline and after 2 cycles of treatment. Interleukin 14α was performed using the unpaired 2-tailed Student test. The progression-free survival (PFS) and overall survival (OS) were calculated using the Kaplan-Meier method and compared by the log-rank test. Results: The early change of IL14α after 2 cycles of anti-PD-1 therapy was calculated as delta IL14α % change = (IL14α level after 2 cycles − IL14α level before treatment)/IL14α level before treatment × 100%. Receiver operating characteristic (ROC) was analyzed to get a cutoff point of delta IL14α % change as 2.46% (sensitivity = 85.71%, specificity = 62.5%; area under the ROC curve [AUC] = 0.7277, P = .034). Using this cutoff to subgroup the patients, an improved objective response rate was observed in patients with a delta IL14α change higher than 2.46% ( P = .0072). A delta IL14α change over 2.46% was associated with a superior PFS ( P = .0039). Conclusions: Early changes of serum IL14α levels may be a promising biomarker to predict outcomes in patients with solid cancer following anti-PD-1 treatment.