Background Necroptosis is a novel programmed cell death mode independent on caspase. A number of studies have revealed that the induction of necroptosis could act as an alternative therapeutic strategy for drug-resistant tumors as well as affect tumor immune microenvironment. Methods Gene expression profiles and clinical data were downloaded from XENA-UCSC (including The Cancer Genome Atlas and Genotype-Tissue Expression), Gene Expression Omnibus, International Cancer Genome Consortium and Chinese Glioma Genome Atlas. We used non-negative matrix factorization method to conduct tumor classification. The least absolute shrinkage and selection operator regression was applied to establish risk models, whose prognostic effectiveness was examined in both training and testing sets with Kaplan–Meier analysis, time-dependent receiver operating characteristic curves as well as uni- and multi-variate survival analysis. Principal Component Analysis, t-distributed Stochastic Neighbor Embedding and Uniform Manifold Approximation and Projection were conducted to check the risk group distribution. Gene Set Enrichment Analyses, immune infiltration analysis based on CIBERSORT, EPIC, MCPcounter, ssGSEA and ESTIMATE, gene mutation and drug sensitivity between the risk groups were also taken into consideration. Results There were eight types of cancer with at least ten differentially expressed necroptosis-related genes which could influence patients’ prognosis, namely, adrenocortical carcinoma (ACC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), pancreatic adenocarcinoma (PAAD), liver hepatocellular carcinoma (LIHC), skin cutaneous melanoma (SKCM) and thymoma (THYM). Patients could be divided into different clusters with distinct overall survival in all cancers above except for LIHC. The risk models could efficiently predict prognosis of ACC, LAML, LGG, LIHC, SKCM and THYM patients. LGG patients from high-risk group had a higher infiltration level of M2 macrophages and cancer-associated fibroblasts. There were more CD8+ T cells, Th1 cells and M1 macrophages in low-risk SKCM patients’ tumor microenvironment. Gene mutation status and drug sensitivity are also different between low- and high-risk groups in the six cancers. Conclusions Necroptosis-related genes can predict clinical outcomes of ACC, LAML, LGG, LIHC, SKCM and THYM patients and help to distinguish immune infiltration status for LGG and SKCM.

This study aimed to assess the response prediction and prognostic values of different peripheral blood cell biomarkers for advanced lung adenocarcinoma (LUAD) patients receiving first-line therapy. Methods: Patients diagnosed with advanced LUAD as well as healthy controls and patients with benign pulmonary diseases were collected in this retrospective study. Propensity score matching (PSM) was performed in a 1:1 ratio. Survival state was estimated by the Kaplan-Meier method and the Cox proportional hazard model was used to assess the prognostic factors. Results: Compared with the control groups, the level of peripheral blood leucocyte, neutrophil, monocyte, platelet, and neutrophil to lymphocyte ratio, monocyte to lymphocyte ratio, platelet to lymphocyte ratio, and systemic inflammation response index (SIRI) were higher in LUAD patients (all p < 0.001). Some inflammatory markers decreased at the time of optimal response and then increased again as the disease progressed. Multivariate analysis revealed that SIRI and lactate dehydrogenase (LDH) were independent prognostic factors no matter before or after PSM analysis. Area under the curve (AUC) of SIRI and LDH were 0.625 (p < 0.001) and 0.596 (p = 0.008), respectively. When SIRI and LDH were combined, the AUC reached 0.649 (p < 0.001). Conclusions: Pretreatment SIRI was an independent prognostic factor of progression free survival (PFS) in advanced LUAD patients. Dynamic monitoring of inflammatory index changes could help to predict therapeutic efficacy. The combination of SIRI and LDH is expected to be a promising clinically accessible biomarker in the future.

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Background: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a significant health issue closely associated with multiple extrahepatic cancers. The association between MAFLD and clinical outcomes of endometrial cancer (EC) remains unknown. Methods: We retrospectively included 725 EC patients between January 2012 and December 2020. The odds ratios (ORs) were calculated using logistic regression analyses. Kaplan–Meier survival curves were used for survival analysis. Results: Among EC patients, the prevalence of MAFLD was 27.7% (201/725, 95% confidence interval (Cl) = 0.245–0.311). MAFLD was significantly associated with cervical stromal involvement (CSI) (OR = 1.974, 95% confidence interval (Cl) = 1.065–3.659, p = 0.031). There was a significant correlation between overall survival (OS) and CSI (HR = 0.31; 95%CI: 0.12–0.83; p = 0.020), while patients with MAFLD had a similar OS to those without MAFLD (p = 0.952). Moreover, MAFLD was significantly associated with CSI in the type I EC subgroup (OR = 2.092, 95% confidence interval (Cl) = 1.060–4.129, p = 0.033), but not in the type II EC subgroup (p = 0.838). Further logistic regression analysis suggested that the hepatic steatosis index (HSI) was significantly associated with CSI among type I EC patients without type 2 diabetes mellitus (T2DM) (OR = 1.079, 95% confidence interval (Cl) = 1.020–1.139, p = 0.012). Conclusions: About one-quarter of our cohort had MAFLD. MAFLD was associated with the risk of CSI in EC patients, and this association existed in type I EC patients but not in type II EC patients. Furthermore, the HSI can help predict CSI in type I EC patients without T2DM.