Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.

BACKGROUND: Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms. METHODS: Gene expression data for gastric cancer were gathered from TCGA and GEO databases. DEGs associated with immunotherapy response came from ICBatlas. KEGG and GO analyses investigated pathways. Hub genes identification employed multiple machine algorithms. Associations between hub genes and signaling pathways, disease genes, immune cell infiltration, drug sensitivity, and prognostic predictions were explored via multi-omics analysis. Hub gene expression was validated through HPA and CCLE. Multiple algorithms pinpointed Cancer-Associated Fibroblasts genes (CAFs), with ten machine-learning methods generating CAFs scores for prognosis. Model gene expression was validated at the single-cell level using the TISCH database. RESULTS: We identified 201 upregulated and 935 downregulated DEGs. Three hub genes, namely CDH6, EGFLAM, and RASGRF2, were unveiled. These genes are implicated in diverse disease-related signaling pathways. Additionally, they exhibited significant correlations with disease-associated gene expression, immune cell infiltration, and drug sensitivity. Exploration of the HPA and CCLE databases exposed substantial expression variations across patients and cell lines for these genes. Subsequently, we identified CAFs-associated genes and established a robust prognostic model. The analysis in the TISCH database showed that the genes in this model were highly expressed in CAFs. CONCLUSIONS: The results unveil an association between CDH6, EGFLAM, and RASGRF2 and the immunotherapeutic response in gastric cancer. These genes hold potential as predictive biomarkers for gastric cancer immunotherapy resistance and prognostic assessment.

Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration.

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.

Safety and efficacy of immune checkpoint inhibitor rechallenge in advanced non-small cell lung cancer: a retrospective study.

We conducted a retrospective study to evaluate the efficacy of immune checkpoint inhibitor (ICI) rechallenge in patients with advanced non-small cell lung cancer (NSCLC). The study included 111 patients who had previously received ICI therapy and experienced disease progression. The primary endpoints assessed were overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). Our findings revealed that the ICI rechallenge showed promising results in improving patient outcomes. OS (r) is the time from rechallenging with immune checkpoint inhibitors to the last follow-up or death from any cause. The median OS (r) was 14.3 months (95% CI 11.3-17.3 months), with a median PFS (r) of 5.9 months (95% CI 4.1-7.7 months). The ORR was 17.1%; the DCR was 82.3%. Subgroup analysis demonstrated that patients without brain or liver metastases had a longer OS (r) compared to those with metastases (21.6 vs. 13.8 months, χ2 = 3.873, P = 0.046; 20.8 vs. 9.1 months, χ2 = 10.733, P = 0.001, respectively). Moreover, patients without driver gene mutations exhibited significantly longer OS than those with mutations or wild-type patients (22.9 vs. 16.1 vs. 7.5 months, χ2 = 10.710, P = 0.005). Notably, patients who switched to a different ICI during the rechallenge had shorter OS than those who did not change medications (10.4 vs. 21.1 months, χ2 = 9.014, P = 0.003). The incidence of immune-related adverse events did not significantly differ between the two treatment phases. These findings suggest that ICI rechallenge may be a viable therapeutic strategy for select NSCLC patients. Further prospective studies are needed to validate these results and guide treatment decisions for advanced NSCLC.

Cognitive function and cardiovascular health in the elderly: network analysis based on hypertension, diabetes, cerebrovascular disease, and coronary heart disease.

Introduction: Cognitive decline in the elderly population is a growing concern, and vascular factors, such as hypertension, diabetes, cerebrovascular disease, and coronary heart disease, have been associated with cognitive impairments. This study aims to provide deeper insights into the structure of cognitive function networks under these different vascular factors and explore their potential associations with specific cognitive domains. Methods: Cognitive function was assessed using a modified Chinese version of the mini-mental state examination (MMSE) scale, and intensity centrality and side weights were estimated by network modeling. The network structure of cognitive function was compared across subgroups by including vascular factors as subgroup variables while controlling for comorbidities and confounders. Results: The results revealed that cerebrovascular disease and coronary heart disease had a more significant impact on cognitive function. Cerebrovascular disease was associated with weaker centrality in memory and spatial orientation, and a sparser cognitive network structure. Coronary heart disease was associated with weaker centrality in memory, repetition, executive function, recall, attention, and calculation, as well as a sparser cognitive network structure. The NCT analyses further highlighted significant differences between the cerebrovascular disease and coronary heart disease groups compared to controls in terms of overall network structure and connection strength. Conclusion: Our findings suggest that specific cognitive domains may be more vulnerable to impairments in patients with cerebrovascular disease and coronary heart disease. These insights could be used to improve the accuracy and sensitivity of cognitive screening in these patient populations, inform personalized cognitive intervention strategies, and provide a better understanding of the potential mechanisms underlying cognitive decline in patients with vascular diseases.

Application of an angiogenesis-related genes risk model in lung adenocarcinoma prognosis and immunotherapy.

Lung adenocarcinoma (LUAD) is an essential pathological subtype of non-small cell lung cancer and offers a severe problem for worldwide public health. There is mounting proof that angiogenesis is a crucial player in LUAD progression. Consequently, the purpose of this research was to construct a novel LUAD risk assessment model based on genetic markers related to angiogenesis. We accessed The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases for LUAD mRNA sequencing data and clinical information. Based on machine algorithms and bioinformatics, angiogenic gene-related risk scores (RS) were calculated. Patients in the high-risk category had a worse prognosis (p < 0.001) in the discovery TCGA cohort, and the results were confirmed by these three cohorts (validation TCGA cohort, total TCGA cohort, and GSE68465 cohort). Moreover, risk scores for genes involved in angiogenesis were independent risk factors for lung cancer in all four cohorts. The low-risk group was associated with better immune status and lower tumor mutational load. In addition, the somatic mutation study revealed that the low-risk group had a lower mutation frequency than the high-risk group. According to an analysis of tumor stem cell infiltration, HLA expression, and TIDE scores, the low-risk group had higher TIDE scores and HLA expression levels than the high-risk group, and the amount of tumor stem cell infiltration correlated with the risk score. In addition, high-risk groups may benefit from immune checkpoint inhibitors and targeted therapies. In conclusion, we developed an angiogenesis-related gene risk model to predict the prognosis of LUAD patients, which may aid in the classification of patients with LUAD and select medications for LUAD patients.

Adjuvant Therapy With PD1/PDL1 Inhibitors for Human Cancers: A Systematic Review and Meta-Analysis.

Background: Immune checkpoint inhibitors (ICIs) have made a breakthrough in the systemic treatment of patients with advanced tumors. However, little is known about their efficacy and safety in adjuvant settings after the resection of solid tumors. Methods: We performed a meta-analysis on the efficacy and safety of programmed death 1 (PD1)/PD-1 ligand (PDL1) inhibitors in adjuvant therapy after tumor resection using Review Manager 5.3, based on published clinical studies. The outcomes included recurrence-free survival (RFS), disease-free survival (DFS), overall survival (OS), and adverse events (AEs). Results: Eight randomized controlled trials (RCTs) were included in the analysis. The use of PD1/PDL1 inhibitors in adjuvant therapy significantly improved RFS (hazard ratio [HR] = 0.72; 95% confidence interval [CI] 0.67-0.78, p < 0.00001). However, there was no statistically significant difference in OS between PD1/PDL1 inhibitors and placebo (HR = 0.86; 95% CI 0.74-1.00, p = 0.05). Gender, age, and PDL1 status were independent predictors of RFS with PD1/PDL1 inhibitors. As for the safety analysis results, PD1/PDL1 inhibitors had a higher incidence of fatigue (risk ratio [RR] = 1.22; 95% CI 1.01-1.49, p = 0.04), nausea (RR = 1.47; 95% CI 1.11-1.94, p = 0.007), and pruritus (RR = 1.96; 95% CI 1.57-2.44, p < 0.00001). In addition, the incidence of any grade adverse events increased in the PD1/PDL1 inhibitor group (RR = 1.03; 95% CI 1.02-1.05, p < 0.0001). Conclusions: This is the first meta-analysis on the efficacy and safety of PD1/PDL1 inhibitors in adjuvant therapy. The use of PD1/PDL1 inhibitors in adjuvant therapy could significantly reduce the recurrence rate after solid tumor resection. However, the incidence of fatigue, nausea, pruritus, and any grade AEs also increased, which should be monitored with vigilance.

The Prognostic Value of ctDNA and bTMB on Immune Checkpoint Inhibitors in Human Cancer.

BACKGROUND: Circulating tumor DNA (ctDNA) levels and blood tumor mutation burden (bTMB) have a significant impact on the prognosis of tumor patients. However, their prognostic role in immune checkpoint inhibitors (ICIs) in cancer patients is still unclear. METHODS: We used the Review Manager software (version 5.3) to perform a meta-analysis based on the published literature to explore the prognostic value of ctDNA and bTMB in patients receiving immunotherapy. We extracted the hazard ratios (HRs) of progression-free survival (PFS) and overall survival (OS) for each included study and their respective 95% confidence intervals (CIs) and p-values for analysis. RESULTS: Thirteen studies were included in the meta-analysis. Higher ctDNA levels were significantly associated with shorter OS (HR = 3.35, 95%CI = 2.49-4.51, p < 0.00001) and PFS (HR = 3.28, 95%CI = 2.47-4.35, p < 0.00001). The results of ctDNA subgroup analysis showed that high posttreatment ctDNA levels significantly correlated with shorter OS in cancer patients receiving ICIs (HR = 5.09, 95%CI = 1.43-18.07, p = 0.01). Moreover, patients with ctDNA clearance had better OS (HR = 4.94, 95%CI = 2.96-8.26, p < 0.00001). Patients with high posttreatment ctDNA levels had shorter PFS (HR = 3.00, 95%CI = 2.02-4.46, p < 0.00001) and those with ctDNA clearance had longer PFS (HR = 4.61, 95%CI = 2.78-7.65, p < 0.00001). However, there was no statistically significant difference in the OS benefits between a high and a low bTMB after ICI therapy (HR = 0.68, 95%CI = 0.33-1.37, p = 0.28). CONCLUSIONS: The host immune system and tumor burden together determine whether cancer patients can benefit from ICI therapy. Our systematic review and meta-analysis revealed for the first time that the levels of pretreatment and posttreatment ctDNA and the clearance of ctDNA can independently be used as prognostic factors for antitumor immunotherapy, while bTMB cannot. In conclusion, ctDNA levels have great potential as an assistant tool for radiological assessments to make clinical therapeutic decisions. The prognostic utility of bTMB still requires further exploration.