Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

Background: Sepsis remains a complex condition with incomplete understanding of its pathogenesis. Further research is needed to identify prognostic factors, risk stratification tools, and effective diagnostic and therapeutic targets.Methods: Three GEO datasets (GSE54514, GSE65682, and GSE95233) were used to explore the potential role of mitochondria-related genes (MiRGs) in sepsis. WGCNA and two machine learning algorithms (RF and LASSO) were used to identify the feature of MiRGs. Consensus clustering was subsequently carried out to determine the molecular subtypes for sepsis. CIBERSORT algorithm was conducted to assess the immune cell infiltration of samples. A nomogram was also established to evaluate the diagnostic ability of feature biomarkers via “rms” package. Results: Three different expressed MiRGs (DE-MiRGs) were identified as sepsis biomarkers. A significant difference in the immune microenvironment landscape was observed between healthy controls and sepsis patients. Among the DE-MiRGs, NDUFB3 was selected to be a potential therapeutic target and its significant elevated expression level was confirmed in sepsis using in vitro experiments and confocal microscopy, indicating its significant contribution to the mitochondrial quality imbalance in the LPS-simulated sepsis model. Conclusion: By digging the role of these pivotal genes in immune cell infiltration, we gained a better understanding of the molecular immune mechanism in sepsis and identified potential intervention and treatment strategies.

The interplay between the immune system and cancer underscores the central role of immunotherapy in cancer treatment. In this context, the innate immune system plays a critical role in preventing tumor invasion. Myeloid differentiation factor 88 (MyD88) is crucial for innate immunity, and activation of MyD88 promotes the production of inflammatory cytokines and induces infiltration, polarization, and immune escape of immune cells in the tumor microenvironment. Additionally, abnormal MyD88 signaling induces tumor cell proliferation and metastasis, which are closely associated with poor prognosis. Therefore, MyD88 could serve as a novel tumor biomarker and is a promising target for cancer therapy. Current strategies targeting MyD88 including inhibition of signaling pathways and protein multimerization, have made substantial progress, especially in inflammatory diseases and chronic inflammation-induced cancers. However, the specific role of MyD88 in regulating tumor immunity and tumorigenic mechanisms remains unclear. Therefore, this review describes the involvement of MyD88 in tumor immune escape and disease therapy. In addition, classical and non-classical MyD88 inhibitors were collated to provide insights into potential cancer treatment strategies. Despite several challenges and complexities, targeting MyD88 is a promising avenue for improving cancer treatment and has the potential to revolutionize patient outcomes.

BACKGROUND The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases. MATERIAL AND METHODS IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein-protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively. RESULTS A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes. CONCLUSIONS The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.

Objectives Cetuximab plus leucovorin, fluorouracil and oxaliplatin (FOLFOX-4) is superior to FOLFOX-4 alone as a first-line treatment for patients with metastatic colorectal cancer with RAS wild-type (RAS wt mCRC), with significantly improved survival benefit by TAILOR, an open-label, randomised, multicentre, phase III trial. Nevertheless, the cost-effectiveness of these two regimens remains uncertain. The following study aims to determine whether cetuximab combined with FOLFOX-4 is a cost-effective regimen for patients with specific RAS wt mCRC in China. Design A cost-effectiveness model combined decision tree and Markov model was built to simulate pateints with RAS wt mCRC based on health states of dead, progressive and stable. The health outcomes from the TAILOR trial and utilities from published data were used respectively. Costs were calculated with reference to the Chinese societal perspective. The robustness of the results was evaluated by univariate and probabilistic sensitivity analyses. Participants The included patients were newly diagnosed Chinese patients with fully RAS wt mCRC. Interventions First-line treatment with either cetuximab plus FOLFOX-4 or FOLFOX-4. Main outcome measures The primary outcomes are costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs). Results Baseline analysis disclosed that the QALYs was increased by 0.383 caused by additional cetuximab, while an increase of US$62 947 was observed in relation to FOLFOX-4 chemotherapy. The ICER was US$164 044 per QALY, which exceeded the willingness-to-pay threshold of US$28 106 per QALY. Conclusions Despite the survival benefit, cetuximab combined with FOLFOX-4 is not a cost-effective treatment for the first-line regime of patients with RAS wt mCRC in China. Trial registration number TAILOR trial (NCT01228734); Post-results.