Background: Although immunosenescence-induced difference on overall immune function and immune cell subsets between younger and older populations has been well characterized, the potential effect of patients' age on the efficacy of immune checkpoint inhibitors (ICIs) remains little known. We performed a meta-analysis to investigate whether age differences play a role in cancer immunotherapy efficacy based on a large amount of clinical data.Methods: We conducted a systematic search of PubMed, Embase and MEDLINE for relevant randomized controlled trials. The primary outcome was overall survival (OS) and progression-free survival (PFS) was secondary outcome. The interaction test was used to assess the heterogeneity of HR between younger and older groups.Results: In total, 19 clinical randomized trials involving 11157 patients were included. The pooled HR for OS was 0.73 (95%CI 0.69-0.78) and 0.63 (95%CI 0.52-0.73) for PFS in younger patients receiving ICIs treatments, when compared with younger patients treated with controls. For older patients treated with ICIs, the pooled HR for OS compared with controls was 0.64 (95%CI 0.59-0.69) and 0.66 (95% CI 0.58-0.74) for PFS. The difference on OS efficacy between younger and older patients treated with ICIs was significant (P-heterogeneity=0.025).Conclusions: Immune checkpoint inhibitors significantly improved OS and PFS in both younger and older patients compared with controls, but the magnitude of benefit was clinically age-dependent. Patients 65y can benefit more from immunotherapy than younger patients. Future research should take age difference into consideration in trials and focus on tolerance and toxicity of ICIs in older patients.

Background: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC.Methods: Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes.The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC.Results: A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity (ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression.Conclusions: This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.

Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC. Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein–protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database. Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 down-regulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression. Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.