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Background This study aimed to investigate the predictive values of serum biomarkers including absolute eosinophil count (AEC), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) with respect to immune-related adverse events (irAEs) during anti-PD-1/PD-L1 inhibitor treatment in patients with advanced malignant tumors. Methods We retrospectively analyzed 95 patients with advanced cancer who were treated with anti-PD-1/PD-L1 inhibitors from January 1, 2017, to May 1, 2020, in our cancer center. We then analyzed associations between irAEs and anti-PD-1/PD-L1 inhibitor responses and evaluated the predictive values of serum biomarkers with respect to the risk of irAEs. Results The incidence of irAEs was 55.8%. There were no statistically significant differences between the irAEs and no-irAEs groups in an objective response rate (ORR) or disease control rate (DCR). However, landmark analysis showed that the irAEs group had better survival after 120 days following the initiation of anti-PD-1/PD-L1 inhibitor treatment, compared with the no-irAEs group. The incidences of irAEs were greater in the high-AEC and low-NLR groups than in the low-AEC and high-NLR groups. Univariate logistic analysis showed that low NLR, ECOG performance status (0–1), and high AEC were risk factors for irAEs. Multivariate logistic analysis showed that high AEC and good ECOG performance status were independent predictors for irAEs. Conclusions irAEs may be associated with a survival benefit. Baseline AEC is a strong predictor of irAEs in patients undergoing treatment with anti-PD-1/PD-L1 inhibitors.

Background Radiotherapy resistance is the main cause of low tumor regression for locally advanced rectum adenocarcinoma (READ). The biomarkers correlated to radiotherapy sensitivity and potential molecular mechanisms have not been completely elucidated. Methods A mRNA expression profile and a gene expression dataset of READ (GSE35452) were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) between radiotherapy responder and non-responder of READ were screened out. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs were performed. Random survival forest analysis was used to identified hub genes by randomForestSRC package. Based on CIBERSORT algorithm, Genomics of Drug Sensitivity in Cancer (GDSC) database, Gene set variation analysis (GSVA), enrichment analysis (GSEA), nomogram, motif enrichment and non-coding RNA network analyses, the associations between hub genes and immune cell infiltration, drug sensitivity, specific signaling pathways, prognosis prediction and TF – miRNA regulatory and ceRNA network were investigated. The expressions of hub genes in clinical samples were displayed with the online Human Protein Atlas (HPA). Results In total, 544 up-regulated and 575 down-regulated DEGs in READ were enrolled. Among that, three hubs including PLAGL2, ZNF337 and ALG10 were identified. These three hub genes were significantly associated with tumor immune infiltration, different immune-related genes and sensitivity of chemotherapeutic drugs. Also, they were correlated with the expression of various disease-related genes. In addition, GSVA and GSEA analysis revealed that different expression levels of PLAGL2, ZNF337 and ALG10 affected various signaling pathways related to disease progression. A nomogram and calibration curves based on three hub genes showed excellent prognosis predictive performance. And then, a regulatory network of transcription factor (ZBTB6) - mRNA (PLAGL2) and a ceRNA network of miRNA (has-miR-133b) - lncRNA were established. Finally, the results from HPA online database demonstrated the protein expression levels of PLAGL2, ZNF337 and ALG10 varied widely in READ patients. Conclusion These findings indicated that up-regulation of PLAGL2, ZNF337 and ALG10 in READ associated with radiotherapy response and involved in multiple process of cellular biology in tumor. They might be potential predictive biomarkers for radiotherapy sensitivity and prognosis for READ.