Impact of varied immunosuppressive agents and post-transplant diabetes mellitus on prognosis among diverse transplant recipients (experimental studies).

The success of solid organ transplantation (SOT) and the use of immunosuppressive agents offer hope to patients with end-stage diseases. However, the impact of post-transplant diabetes mellitus (PTDM) on SOT patients has become increasingly evident. In our study, we utilized the Scientific Registry of Transplant Recipients (SRTR) database to investigate the association between PTDM and patient survival in various types of organ transplantations, including liver, kidney, intestinal, heart, lung, and combined heart-lung transplantations (all P <0.001). Our findings revealed a negative effect of PTDM on the survival of these patients. Furthermore, we examined the effects of both generic and innovator immunosuppressive agents on the development of PTDM and the overall survival of different SOT populations. Interestingly, the results were inconsistent, indicating that the impact of these agents may vary depending on the specific type of transplantation and patient population. Overall, our study provides a comprehensive and systematic assessment of the effects of different immunosuppressive agents on prognosis, as well as the impact of PTDM on the survival of patients undergoing various types of SOT. These findings emphasize the need for further research and highlight the importance of optimizing immunosuppressive regimens and managing PTDM in SOT patients to improve their long-term outcomes.

The macrophage-associated prognostic gene ANXA5 promotes immunotherapy resistance in gastric cancer through angiogenesis.

Gastric cancer (GC) remains a predominant form of malignant tumor globally, necessitating innovative non-surgical therapeutic approaches. This investigation aimed to delineate the expression landscape of macrophage-associated genes in GC and to evaluate their prognostic significance and influence on immunotherapeutic responsiveness. Utilizing the CellMarker2.0 database, we identified 69 immune cell markers with prognostic relevance in GC, including 12 macrophage-specific genes. A Weighted Gene Co-Expression Network Analysis (WGCNA) isolated 3,181 genes correlated with these macrophage markers. The Cancer Genome Atlas (TCGA-STAD) dataset was employed as the training set, while data from the GSE62254 served as the validation cohort. 13 genes were shortlisted through LASSO-Cox regression to formulate a prognostic model. Multivariable Cox regression substantiated that the calculated risk score serves as an imperative independent predictor of overall survival (OS). Distinct macrophage infiltration profiles, pathway associations, treatment susceptibilities, and drug sensitivities were observed between high- and low-risk groups. The preliminary validation of ANXA5 in predicting the survival rates of GC patients at 1 year, 3 years, and 5 years, as well as its expression levels were higher and role in promoting tumor angiogenesis in GC through immunohistochemistry and angiogenesis experiments. In summary, macrophage-related genes were potentially a novel crosstalk mechanism between macrophages and endothelial cells in the tumor microenvironment, and the interplay between inflammation and angiogenesis might have also offered new therapeutic targets, providing a new avenue for personalized treatment interventions.

Optimized risk stratification strategy for glioma patients based on the feature genes of poor immune cell infiltration patterns.

BACKGROUND: Gliomas, originating from glial cells within the brain or spinal cord, are common central nervous system tumors with varying degrees of malignancy that influence the complexity and difficulty of treatment. The current strategies, including traditional surgery, radiotherapy, chemotherapy, and emerging immunotherapies, have yielded limited results. As such, our study aims to optimize risk stratification for a more precise treatment approach. We primarily identify feature genes associated with poor immune cell infiltration patterns through various omics algorithms and categorize glioma patients based on these genes to enhance the accuracy of patient prognosis assessment. This approach can underpin individualized treatment strategies and facilitate the discovery of new therapeutic targets. METHODS: We procured datasets of gliomas and normal brain tissues from TCGA, CGGA, and GTEx databases. Clustering was conducted using the input of 287 immune cell feature genes. Hub genes linked with the poor prognosis subtype (C1) were filtered through WGCNA. The TCGA dataset served as the discovery cohort and the CGGA dataset as the external validation cohort. We constructed a prognostic model related to feature genes from poor immune cell infiltration patterns utilizing LASSO-Cox regression. Comprehensive analyses of genomic heterogeneity, tumor stemness, pathway relevance, immune infiltration patterns, treatment response, and potential drugs were conducted for different risk groups. Gene expression validation was performed using immunohistochemistry (IHC) on 98 glioma samples and 11 normal brain tissue samples. RESULTS: Using the filtered immune cell-related genes, glioma patients were stratified into C1 and C2 subtypes through clustering. The C1 subtype exhibited a worse prognosis, with upregulated genes primarily enriched in immune response, extracellular matrix, etc., and downregulated genes predominantly enriched in neural signal transduction and neural pathway-related aspects. Seven advanced algorithms were used to elucidate immune cell infiltration patterns of different subtypes. In addition, WGCNA identified hub genes from poor immune infiltration patterns, and a prognostic model was constructed accordingly. High-risk patients demonstrated shorter survival times and higher risk scores as compared to low-risk patients. Multivariate Cox regression analysis revealed that, after adjusting for confounding clinical factors, risk score was a vital independent predictor of overall survival (OS) (P < 0.001). The established nomogram, which combined risk scores with WHO grade and age, accurately predicted glioma patient survival rates at 1, 3, and 5 years, with AUCs of 0.908, 0.890, and 0.812, respectively. This risk score enhanced the nomogram's reliability and informed clinical decision-making. We also comprehensively analyzed genomic heterogeneity, tumor stemness, pathway relevance, immune infiltration patterns, treatment response, and potential drugs for different risk groups. In addition, we conducted preliminary validation of the potential PLSCR1 gene using IHC with a large sample of gliomas and normal brain tissues. CONCLUSION: Our optimized risk stratification strategy for glioma patients has the potential to improve the accuracy of prognosis assessment. The findings from our omics research not only enhance the understanding of the functions of feature genes related to poor immune cell infiltration patterns but also offer valuable insights for the study of glioma prognostic biomarkers and the development of individualized treatment strategies.

The prognostic value of the GPAT/AGPAT gene family in hepatocellular carcinoma and its role in the tumor immune microenvironment.

Background: Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related death worldwide. Hepatocellular carcinoma accounts for an estimated 90% of all liver cancers. Many enzymes of the GPAT/AGPAT family are required for the synthesis of triacylglycerol. Expression of AGPAT isoenzymes has been reported to be associated with an increased risk of tumorigenesis or development of aggressive phenotypes in a variety of cancers. However, whether members of the GPAT/AGPAT gene family also influence the pathophysiology of HCC is unknown. Methods: Hepatocellular carcinoma datasets were obtained from the TCGA and ICGC databases. Predictive models related to the GPAT/AGPAT gene family were constructed based on LASSO-Cox regression using the ICGC-LIRI dataset as an external validation cohort. Seven immune cell infiltration algorithms were used to analyze immune cell infiltration patterns in different risk groups. IHC, CCK-8, Transwell assay, and Western blotting were used for in vitro validation. Results: Compared with low-risk patients, high-risk patients had shorter survival and higher risk scores. Multivariate Cox regression analysis showed that risk score was a significant independent predictor of overall survival (OS) after adjustment for confounding clinical factors (p < 0.001). The established nomogram combined risk score and TNM staging to accurately predict survival at 1, 3, and 5 years in patients with HCC with AUC values of 0.807, 0.806, and 0.795, respectively. This risk score improved the reliability of the nomogram and guided clinical decision-making. In addition, we comprehensively analyzed immune cell infiltration (using seven algorithms), response to immune checkpoint blockade, clinical relevance, survival, mutations, mRNA expression-based stemness index, signaling pathways, and interacting proteins related to the three core genes of the prognostic model (AGPAT5, LCLAT1, and LPCAT1). We also performed preliminary validation of the differential expression, oncological phenotype, and potential downstream pathways of the three core genes by IHC, CCK-8, Transwell assay, and Western blotting. Conclusion: These results improve our understanding of the function of GPAT/AGPAT gene family members and provide a reference for prognostic biomarker research and individualized treatment of HCC.

Genotype-guided model significantly improves accuracy of tacrolimus initial dosing after liver transplantation.

Background: The initial dose of tacrolimus after liver transplantation (LT) is critical for rapidly achieving the steady state of the drug concentration, minimizing the potential adverse reactions and warranting long-term patient prognosis. We aimed to develop and validate a genotype-guided model for determining personalized initial dose of tacrolimus. Methods: By combining pharmacokinetic modeling, pharmacogenomic analysis and multiple statistical methods, we developed a genotype-guided model to predict individualized tacrolimus initial dose after LT in the discovery (n = 150) and validation cohorts (n = 97) respectively. This model was further validated in a prospective, randomized and single-blind clinical trial from August, 2021 to February, 2022 (n = 40, ChiCTR2100050288). Findings: Our model included donor's and recipient's genotypes, recipient's weight and total bilirubin, which achieved an area under the curve of receiver operating characteristic curve (AUC of ROC) of 0.88 and 0.79 in the discovery and validation cohorts, respectively. We found that patients who were given tacrolimus within the recommended concentration range (RCR) (4-10 ng/mL), the new-onset metabolic syndromes are lower, especially for new-onset diabetes (p = 0.043). In the clinical trial, compared to those in experience-based (EB) group, patients in the model-based (MB) group were more likely to achieving the RCR (75% vs 40%, p = 0.025) with a more variable individualized dose (0.023-0.096 mg/kg/day vs 0.045-0.057 mg/kg/day). Moreover, significantly fewer medication adjustments were required for the MB group than the EB group (2.75 ± 2.01 vs 6.05 ± 3.35, p = 0.001). Interpretation: Our genotype-based model significantly improved the initial dosing accuracy of tacrolimus and reduced the number of medication adjustments, which are critical for improving the prognosis of LT patients. Funding: National Natural Science Foundation of China, Shanghai three-year action plan, National Science and Technology Major Project of China.

Genetic factors underlying tacrolimus intolerance after liver transplantation.

Background: Tacrolimus (FK506) is the cornerstone of immunosuppression after liver transplantation (LT), however, clinically, switching from FK506 to cyclosporine (SFTC) is common in LT patients with tacrolimus intolerance. The aim of this study was to investigate the genetic risk of patients with tacrolimus intolerance. Methods: A total of 114 LT patients were enrolled in this retrospective study. SNPs were genotyped using Infinium Human Exome-12 v1.2 BeadChip, and genome-wide gene expression levels were profiled using Agilent G4112F array. Results: SFTC was a potential risk factor of dyslipidemia (OR=4.774[1.122-20.311], p = 0.034) and insulin resistance (IR) (OR=6.25[1.451-26.916], p = 0.014), but did not affect the survival of LT patients. Differential expression analysis showed donor CYP3A5, CYP2C9, CFTR, and GSTP1, four important pharmacogenetic genes were significantly up-regulated in the tacrolimus intolerance group. Twelve SNPs of these four genes were screened to investigate the effects on tacrolimus intolerance. Regression analysis showed donor rs4646450 (OR=3.23 [1.22-8.60] per each A allele, p = 0.01), donor rs6977165 (OR=6.44 [1.09-37.87] per each C allele, p = 0.02), and donor rs776746 (OR=3.31 [1.25-8.81] per each A allele, p = 0.01) were independent risk factors of tacrolimus intolerance. Conclusions: These results suggested that SFTC was a potential risk factor for dyslipidemia and IR after LT. Besides, rs4646450, rs6977165, and rs776746 of CYP3A5 might be the underlying genetic risks of tacrolimus intolerance. This might help transplant surgeons make earlier clinical decisions about the use of immunosuppression.