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Immune checkpoint blockade has vastly changed the landscape of cancer treatment and showed a promising prognosis for cancer patients. However, there is still a large portion of patients who have no response to this therapy. Therefore, it's essential to investigate biomarkers to predict the efficacy of immune checkpoint inhibitors. This article summarizes the predictive value of established biomarkers, including programmed cell death ligand 1(PD-L1) expression level, tumor mutational burden, tumor-infiltrating lymphocytes, and mismatch repair deficiency. It also addresses the predictive value of tumorous mutations, circulation factors, immune-related factors, and gut microbiome with immunotherapy treatment. Furthermore, some of the emerging novel biomarkers, and potential markers for hyper progressive disease are discussed, which should be validated in clinical trials in the future.

Background: Asparaginase (ASP) is the cornerstone drug in the treatment of extranodal NK/T-cell lymphoma (ENKTCL), and the mechanisms of resistance to ASP remain largely unknown. Long non-coding RNAs play important roles in chemotherapy resistance in various cancers. However, the expression of BCYRN1 and its role in ENKTCL still remain unidentified. Methods: Lentivirus-mediated BCYRN1 overexpression and knockdown were performed in SNK-6 cells. Cell autophagy was analyzed by adenovirus expressing GFP-LC3B fusion protein. RNA pull-down and RNA Binding Protein Immunoprecipitation Assay were performed to investigate the relationship between BCYRN1 and p53. Western blot analysis was performed to assess the effect of BCYRN1 on different autophagy pathways. Finally, in vivo xenograft tumor model was constructed to analyze the effect of BCYRN1 on tumor growth and ASP resistance. Results: BCYRN1 was overexpressed in ENKTCL than normal NK cells, and patients with higher expression had significantly inferior progression-free survival (PFS). The IC50 value of ASP was significantly increased in BCYRN1-overexpressed SNK-6 cells and BCYRN1 overexpression could resist the inhibitory effect of ASP on proliferation. ASP could induce concurrent apoptosis and autophagy in ENKTCL, and the latter process was enhanced by overexpression of BCYRN1, mainly through affecting both PI3K/AKT/mTOR and p53/mTOR pathways. BCYRN1 could induce the degradation of p53 via ubiquitination, thus resulting in enhancement of autophagy and ASP resistance, which could be reversed by drug-induced autophagy inhibition. The effect of BCYRN1 on tumor growth and autophagy were confirmed in vivo xenograft model. Conclusions: It was found that BCYRN1 was a valuable prognostic biomarker in ENKTCL. BCYRN1 could promote resistance to ASP by inducing autophagy, which could be reversed by inhibition of autophagy. Our findings highlight the feasibility of combining autophagy inhibition and ASP in the treatment of ENKTCL.

Background. Diffuse large B cell lymphoma (DLBCL) is a life-threatening malignant tumor characterized by heterogeneous clinical, phenotypic, and molecular manifestations. Given the association between immunity and tumors, identifying a suitable immune biomarker could improve DLBCL diagnosis. Methods. We systematically searched for DLBCL gene expression microarray datasets from the GEO database. Immune-related genes (IRGs) were obtained from the ImmPort database, and 318 transcription factor (TF) targets in cancer were retrieved from the Cistrome Cancer database. An immune-related classifier for DLBCL prognosis was constructed using Cox regression and LASSO analysis. To assess differences in overall survival between the low- and high-risk groups, we analyzed the tumor microenvironment (TME) and immune infiltration in DLBCL using the ESTIMATE and CIBERSORT algorithms. WGCNA was applied to study the molecular mechanisms explaining the clinical significance of our immune-related classifier and TFs. Results. Eighteen IRGs were selected to construct the classifier. The multi-IRG classifier showed powerful predictive ability. Patients with a high-risk score had poor survival. Based on the AUC for three- and five-year survival, the classifier exhibited better predictive power than clinical data. Discrepancies in overall survival between the low- and high-risk score groups might be explained by differences in immune infiltration, TME, and transcriptional regulation. Conclusions. Our study describes a novel prognostic IRG classifier with strong predictive power in DLBCL. Our findings provide valuable guidance for further analysis of DLBCL pathogenesis and clinical treatment.