Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma

Novel insights into the pathogenesis of thyroid eye disease through ferroptosis-related gene signature and immune infiltration analysis

Thyroid eye disease (TED) has brought great physical and mental trauma to patients worldwide. Although a few potential signaling pathways have been reported, knowledge of TED remains limited. Our objective is to explore the fundamental mechanism of TED and identify potential therapeutic targets using diverse approaches. To perform a range of bioinformatic analyses, such as identifying differentially expressed genes (DEGs), conducting enrichment analysis, establishing nomograms, analyzing weighted gene correlation network analysis (WGCNA), and studying immune infiltration, the datasets GSE58331, GSE105149, and GSE9340 were integrated. Further validation was conducted using qPCR, western blot, and immunohistochemistry techniques. Eleven ferroptosis-related DEGs derived from the lacrimal gland were originally screened. Their high diagnostic value was proven, and diagnostic prediction nomogram models with high accuracy and robustness were established by using machine learning. A total of 15 hub gene-related DEGs were identified by WGCNA. Through CIBERSORTx, we uncovered five immune cells highly correlated with TED and found several special associations between these immune cells and the above DEGs. Furthermore, EGR2 from the thyroid sample was revealed to be closely negatively correlated with most DEGs from the lacrimal gland. High expression of APOD, COPB2, MYH11, and MYCN, as well as CD4/CD8 T cells and B cells, was verified in the periorbital adipose tissues of TED patients. To summarize, we discovered a new gene signature associated with ferroptosis that has a critical impact on the development of TED and provides valuable insights into immune infiltration. These findings might highlight the new direction and therapeutic strategies of TED.

Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer

Pancreatic cancer (PC) is a digestive malignancy with worse overall survival. Tumor immune environment (TIME) alters the progression and proliferation of various solid tumors. Hence, we aimed to detect the TIME-related classifier to facilitate the personalized treatment of PC. Based on the 1612 immune-related genes (IRGs), we classified patients into Immune_rich and Immune_desert subgroups via consensus clustering. Patients in distinct subtypes exhibited a difference in sensitivity to immune checkpoint blockers (ICB). Next, the immune-related signature (IRS) model was established based on 8 IRGs (SYT12, TNNT1, TRIM46, SMPD3, ANLN, AFF3, CXCL9 and RP1L1) and validated its predictive efficiency in multiple cohorts. RT-qPCR experiments demonstrated the differential expression of 8 IRGs between tumor and normal cell lines. Patients who gained lower IRS score tended to be more sensitive to chemotherapy and immunotherapy, and obtained better overall survival compared to those with higher IRS scores. Moreover, scRNA-seq analysis revealed that fibroblast and ductal cells might affect malignant tumor cells via MIF-(CD74+CD44) and SPP1-CD44 axis. Eventually, we identified eight therapeutic targets and one agent for IRS high patients. Our study screened out the specific regulation pattern of TIME in PC, and shed light on the precise treatment of PC.

Focus on Therapeutic Options for Surgically Resectable Pancreatic Adenocarcinoma Based on Novel Biomarkers

Pancreatic ductal adenocarcinoma remains associated with a poor prognosis, even when diagnosed at an early stage. Consequently, it is imperative to carefully consider the available therapeutic options and tailor them based on clinically relevant biomarkers. In our comprehensive review, we specifically concentrated on the identification of novel predictive and prognostic markers that have the potential to be integrated into multiparametric scoring systems. These scoring systems aim to accurately predict the efficacy of neoadjuvant chemotherapy in surgically resectable pancreatic cancer cases. By identifying robust predictive markers, we can enhance our ability to select patients who are most likely to benefit from neoadjuvant chemotherapy. Furthermore, the identification of prognostic markers can provide valuable insights into the overall disease trajectory and inform treatment decisions. The development of multiparametric scoring systems that incorporate these markers holds great promise for optimizing the selection of patients for neoadjuvant chemotherapy, leading to improved outcomes in resectable pancreatic neoplasia. Continued research efforts are needed to validate and refine these markers and scoring systems, ultimately advancing the field of personalized medicine in pancreatic adenocarcinoma management.