The HOXA gene family is associated with various cancer types. However, the role of HOXA genes in acute myeloid leukemia (AML) have not been comprehensively studied. We compared the transcriptional expression, survival data, and network analysis of HOXA-associated signaling pathways in patients with AML using the ONCOMINE, GEPIA, LinkedOmics, cBioPortal, and Metascape databases. We observed that HOXA2-10 mRNA expression levels were significantly upregulated in AML and that high HOXA1-10 expression was associated with poor AML patient prognosis. The HOXA genes were altered in ~18% of the AML samples, either in terms of amplification, deep deletion, or elevated mRNA expression. The following pathways were modulated by HOXA gene upregulation: GO:0048706: embryonic skeletal system development; R-HSA-5617472: activation of HOX genes in anterior hindbrain development during early embryogenesis; GO:0060216: definitive hemopoiesis; hsa05202: transcriptional mis-regulation in cancer; and GO:0045638: negative regulation of myeloid cell differentiation, and they were significantly regulated due to alterations affecting the HOXA genes. This study identified HOXA3-10 genes as potential AML therapeutic targets and prognostic markers.

Purpose: We aimed to assess the prognostic value of pretreatment inflammatory and nutritional parameters for predicting overall survival (OS) in patients with newly diagnosed multiple myeloma (NDMM), and to build a new scoring system using the most important variables. Methods: We retrospectively analyzed baseline clinical and laboratory data for patients with NDMM, who were randomly grouped into training and validation cohorts at a ratio of 8:2. The Inflammatory Nutritional Score (INS) was developed based on the least absolute shrinkage and selection operator (LASSO) Cox regression. The INS and other independent prognostic factors were entered into a multivariate Cox model and merged to generate a nomogram model for predictive optimization. Performance and predictive accuracy were assessed using the concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves. Results: In total, 442 eligible patients were enrolled. Six inflammatory/nutritional variables, including the Nutritional Risk Index (NRI), body mass index (BMI), neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet-lymphocyte ratio (PLR), and albumin-alkaline phosphatase ratio (AAPR), were integrated to construct the INS using the LASSO Cox model. The predictive nomogram constructed following the multivariate Cox analysis included INS, performance status, lactate dehydrogenase, age, and C-reactive protein. The model exhibited good predictive performance, with a C-index of 0.708 in the training cohort and 0.749 in the validation cohort. Moreover, the calibration curves also demonstrated excellent consistency between predicted and observed survival in both cohorts. In the time-dependent ROC analysis, our nomogram model exhibited better performance than other staging systems for multiple myeloma. Conclusion: The INS represents an independent prognostic signature in patients with NDMM. Our novel nomogram based on INS may aid in predicting survival probability and stratifying risk.

Objectives. Recent trials have shown an overall survival (OS) benefit in 10-40% advanced cancer patients treated with programmed cell death 1 (PD-1) or programmed death-ligand 1 (PD-L1) inhibitors. Here, we aimed to evaluate the relationship between PD-L1 expression and the therapeutic efficacy of PD-1 or PD-L1 inhibitors in patients with cancer with recurrent or metastatic disease, compared with control treatments. Methods. We systematically searched Medline (PubMed), Embase, and Cochrane Library databases up to Jan 2019 and pooled the treatment effects (hazard ratio or relative ratio) of PD-1/PD-L1 inhibitors in patients with different PD-L1 expression. Results. Overall, twenty-four qualifying trials with over 14,860 subjects were eligible in this study. Compared with conventional agents, anti-PD/PD-L1 drugs significantly reduced the risk of death (hazard ratio 0.72, 95% CI 0.66 to 0.78), irrespective of the tumor type. Additionally, when PD-L1 expression ≥1% was defined as positive, anti-PD-1/PD-L1 monotherapy correlated with prolonged overall survival in patients with nonsmall cell lung cancer (NSCLC) (0.72, 0.61 to 0.86) and other cancer types (0.66, 0.57 to 0.76) patients with PD-L1 positive, rather than those with PD-L1 negative (hazard ratio for NSCLC and other cancer types: 0.84 and 0.87, respectively; all ). The subgroup analyses to experimental agents, PD-1/PD-L1 inhibitors, PD-L1 antibody clone, and type of IHC scoring method validated the robustness of these findings. However, anti-PD-1/PD-L1 combination therapies can reduce the risk of death for patients with different cancer types, regardless of PD-L1 expression ( for all PD-L1 expression status). Conclusions. We recommend PD-L1 expression as a predictive biomarker in patient selection for anti-PD-1/PD-L1 monotherapy, but not for combination therapies.

Despite that immune checkpoint inhibitors (ICIs) had tremendous improved the survival of multiple solid tumors, only a limited proportion of patients are responsive to ICIs. Therefore, effective variables are urgently needed to predict the probability of response to ICIs. Systematic searches were conducted from inception up to May, 2020. Prospective or retrospective studies of ICIs that investigated the association between body mass index (BMI) and survival outcomes, including overall survival (OS) and/or progression-free survival (PFS), were selected. The association between each BMI category and survival outcomes was calculated using Cox proportional hazard regression models and quantified as hazard ratio (HR) with corresponding 95% confidence interval. Seven clinical studies involving data from 3768 individual patients were included. The median OS was 15.5 months (95% confidence interval: 14.7–16.2 mo) and the median PFS was 5.7 months (5.2–6.3 mo). The median OS was significantly longer in overweight/obese patients than in nonoverweight patients (20.7 vs. 11.3 mo; P<0.001). The difference in OS between overweight and obese patients was not statistically significant (HR: 1.14, P=0.098). Similar results were observed for PFS outcomes. Subgroup analysis demonstrated improved OS in overweight/obese patients with nonsmall-cell lung cancer (HR: 0.81, P=0.002), melanoma (HR: 0.66, P<0.001), renal cell carcinoma (HR: 0.53, P<0.001), and multiple cancer type (HR: 0.34, P<0.001), with parallel results noted regarding PFS outcomes. Results of the present study suggested that BMI may be a satisfactory prognostic factor for patients treated with ICIs.