Identification of necroptosis-related features in diabetic nephropathy and analysis of their immune microenvironent and inflammatory response

Proteomic Analysis of the Effects of Shenzhu Tiaopi Granules on Model Rats with Type 2 Diabetes Mellitus

Background

Shenzhu Tiaopi granule (STG) has antidiabetic functions. Data-independent acquisition proteomic technology is an integral part of systems biology. Herein, proteomics was used to analyse the effects of STG on type 2 diabetes mellitus (T2DM) and the mechanism by which STG normalizes glucose metabolism.

Methods

Goto-Kakizaki (GK) T2DM model (Mod) rats, aged 15-16 weeks and with a fasting blood glucose (FBG) level of ≥11.1 mmol/L, were treated with metformin or STG for 12 weeks. Wistar rats aged 15-16 weeks were included in the control (Con) group. Body weight, FBG, total cholesterol (TC), total triglyceride (TG) levels and low-density lipoprotein (LDL-C) levels were measured, and pathological observation, Western blot analysis and data-independent acquisition proteomics of the liver were performed.

Results

Significant differences in FBG, TC, TG, LDL-C (p < 0.01) and pathological liver morphology were observed between the Mod group and Con group, whereas both metformin and STG normalized the glucose and lipid metabolism indicators (p < 0.05 or p < 0.01). In total, 5856 proteins were identified via proteomic analysis, 97 of which were significantly differentially expressed in the liver and affected fatty acid metabolism, unsaturated fatty acid biosynthesis, the peroxisome proliferator-activated receptor (PPAR) signalling pathway, pyruvate metabolism, and terpenoid backbone biosynthesis. Screening identified 10 target proteins, including perilipin-2 (Plin2), pyruvate dehydrogenase kinase 4, farnesyl diphosphate synthase (Fdps) and farnesyl-diphosphate farnesyltransferase 1. Among these proteins, the key proteins were Plin2 and Fdps, which were found to be associated with the PPAR signalling pathway and terpenoid backbone biosynthesis via relationship networks. Plin2 and Fdps are closely related to hyperglycaemia. STG can downregulate Plin2 and upregulate Fdps (p < 0.01).

Conclusion

STG ameliorated hyperglycaemia by significantly altering the expression of different proteins, especially Fdps and Plin2, in the livers of GK rats. These findings may reveal the potential of traditional Chinese medicine for treating T2DM.

Comprehensive exploration of programmed cell death landscape in lung adenocarcinoma combining multi-omic analysis and experimental verification

The mortality and therapeutic failure in lung adenocarcinoma (LUAD) are mainly resulted from the wide metastasis and chemotherapy resistance. Up to now, accurate and stable predictive prognostic indicator for revealing the progress and novel therapeutic strategies of LUAD is infrequent, nonetheless. Diversified programmed cell death (PCD) has been widely confirmed that participated in the occurrence and development of various malignant tumors, respectively. In this research, we integrated fourteen types of PCD, bulk multi-omic data from TCGA-LUAD and other cohorts in gene expression omnibus (GEO) and clinical LUAD patients to develop our analysis. Consequently, pivotal fourteen PCD genes, especially CAMP, CDK5R1, CTSW, DAPK2, GAB2, GAPDH, GATA2, HGF, MAPT, NAPSA, NUPR1, PIK3CG, PLA2G3, and SLC7A11, were utilized to establish the prognostic signature, namely cell death index (CDI). The validation in several external cohorts indicated that CDI can be regarded as a potential risk factor of LUAD patients. Combined with other common clinical information, a nomogram with potential predictive ability was constructed. Besides, according to the CDI signature, the tumor microenvironment (TME) and sensitivity to some potential chemotherapeutic drugs were further and deeply explored. Notably, verification and functional experiments further demonstrated the remarkable correlation between CDI and unfold protein response. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of LUAD patients.

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning

The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently available methods and biomarkers in predicting the response of immunotherapy and prognosis of CM is limited. Programmed cell death (PCD) plays a significant role in the occurrence, development, and therapy of various malignant tumors. In this research, we integrated fourteen types of PCD, multi-omics data from TCGA-SKCM and other cohorts in GEO, and clinical CM patients to develop our analysis. Based on significant PCD patterns, two PCD-related CM clusters with different prognosis, tumor microenvironment (TME), and response to immunotherapy were identified. Subsequently, seven PCD-related features, especially CD28, CYP1B1, JAK3, LAMP3, SFN, STAT4, and TRAF1, were utilized to establish the prognostic signature, namely cell death index (CDI). CDI accurately predicted the response to immunotherapy in both CM and other cancers. A nomogram with potential superior predictive ability was constructed, and potential drugs targeting CM patients with specific CDI have also been identified. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of CM patients, providing unique opportunities for clinical intelligence and new management methods for the therapy of CM.