Background: Membranous nephropathy (MN) is an autoimmune disease. It is an important cause of end-stage renal disease in primary glomerulonephritis. Significant breakthroughs in its diagnosis have been made in previous studies, however, the pathogenesis of MN has still remained elusive. In recent years, bioinformatics has provided new research strategies to investigate the mechanisms of kidney disease. This study aimed to explore potential biomarkers of MN through bioinformatics analysis. Methods: Differentially expressed genes (DEGs) were identified by performing a differential expression analysis with the "limma" R package, and then, the weighted gene co-expression network analysis (WGCNA) was applied to obtain the most MN-related genes. After intersecting these genes, the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) algorithms were utilized to identify hub genes. To assess the diagnostic value of hub genes, the receiver operating characteristic (ROC) curve analysis was performed. Finally, the relationship between hub genes and the immune microenvironment (IME) was analyzed. Results: The differential expression analysis yielded 1,466 DEGs, and using the WGCNA, 442 genes, which were the most MN-related genes, were obtained. From the intersection of these genes, 130 genes were identified. Subsequently, two hub genes (ECM1 and ATP8B1) were detected by the LASSO and SVM-REF algorithms. It was found that they were associated with components of the IME (natural killer T cells, gamma delta T cells, macrophages, etc.). Conclusion: Two hub genes (ECM1 and ATP8B1) were identified by machine learning, and their diagnostic value was evaluated. It was revealed that these two genes were associated with the components of the IME. Our findings may provide new ideas for developing new biomarkers for MN.

Background: Anoikis, a mechanism of programmed apoptosis, plays an important role in growth and metastasis of tumors. However, there are still few available comprehensive reports on the impact of anoikis on colorectal cancer. Method: A clustering analysis was done on 133 anoikis-related genes in GSE39582, and we compared clinical features between clusters, the tumor microenvironment was analyzed with algorithms such as “Cibersort” and “ssGSEA”. We investigated risk scores of clinical feature groups and anoikis-associated gene mutations after creating a predictive model. Lastly, we incorporated clinical traits to build a nomogram. Result: We identified two anoikis-related clusters with distinct prognoses, clinical characteristics, and biological functions. One of the clusters was associated with anoikis resistance, which activated multiple pathways encouraging tumor metastasis. In our prognostic model, oxaliplatin may be a sensitive drug for low-risk patients. The nomogram showed good ability to predict survival time. Conclusion: Our study identified two distinct modes of anoikis in colorectal cancer, with active metastasis-promoting pathways inducing an anti-anoikis subtype, which has a stronger propensity for metastasis and a worse prognosis than an anoikis-activated subtype. Massive immune cell infiltration may be an indicator of anoikis resistance. Anoikis' role in the colorectal cancer remains to be investigated.

Background: Diffuse large B-cell lymphoma (DLBCL), which is considered to be the most common subtype of lymphoma, is an aggressive tumor. Necroptosis, a novel type of programmed cell death, plays a bidirectional role in tumors and participates in the tumor microenvironment to influence tumor development. Targeting necroptosis is an intriguing direction, whereas its role in DLBCL needs to be further discussed.Methods: We obtained 17 DLBCL-associated necroptosis-related genes by univariate cox regression screening. We clustered in GSE31312 depending on their expressions of these 17 genes and analyzed the differences in clinical characteristics between different clusters. To investigate the differences in prognosis across distinct clusters, the Kaplan-Meier method was utilized. The variations in the tumor immune microenvironment (TME) between distinct necroptosis-related clusters were investigated via “ESTIMATE”, “Cibersort” and single-sample geneset enrichment analysis (ssGSEA). Finally, we constructed a 6-gene prognostic model by lasso-cox regression and subsequently integrated clinical features to construct a prognostic nomogram.Results: Our analysis indicated stable but distinct mechanism of action of necroptosis in DLBCL. Based on necroptosis-related genes and cluster-associated genes, we identified three groups of patients with significant differences in prognosis, TME, and chemotherapy drug sensitivity. Analysis of immune infiltration in the TME showed that cluster 1, which displayed the best prognosis, was significantly infiltrated by natural killer T cells, dendritic cells, CD8+ T cells, and M1 macrophages. Cluster 3 presented M2 macrophage infiltration and the worst prognosis. Importantly, the prognostic model successfully differentiated high-risk from low-risk patients, and could forecast the survival of DLBCL patients. And the constructed nomogram demonstrated a remarkable capacity to forecast the survival time of DLBCL patients after incorporating predictive clinical characteristics.Conclusion: The different patterns of necroptosis explain its role in regulating the immune microenvironment of DLBCL and the response to R-CHOP treatment. Systematic assessment of necroptosis patterns in patients with DLBCL will help us understand the characteristics of tumor microenvironment cell infiltration and aid in the development of tailored therapy regimens.