Integrated Analysis of Stemness-Related LncRNAs Helps Predict the Immunotherapy Responsiveness of Gastric Cancer Patients

Overcoming immunotherapy resistance in gastric cancer: insights into mechanisms and emerging strategies

Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, with limited treatment options in advanced stages. Immunotherapy, particularly immune checkpoint inhibitors (ICIs) targeting PD1/PD-L1, has emerged as a promising therapeutic approach. However, a significant proportion of patients exhibit primary or acquired resistance, limiting the overall efficacy of immunotherapy. This review provides a comprehensive analysis of the mechanisms underlying immunotherapy resistance in GC, including the role of the tumor immune microenvironment, dynamic PD-L1 expression, compensatory activation of other immune checkpoints, and tumor genomic instability. Furthermore, the review explores GC-specific factors such as molecular subtypes, unique immune evasion mechanisms, and the impact of Helicobacter pylori infection. We also discuss emerging strategies to overcome resistance, including combination therapies, novel immunotherapeutic approaches, and personalized treatment strategies based on tumor genomics and the immune microenvironment. By highlighting these key areas, this review aims to inform future research directions and clinical practice, ultimately improving outcomes for GC patients undergoing immunotherapy.

Mitochondrial cholesterol metabolism related gene model predicts prognosis and treatment response in hepatocellular carcinoma

Background

The persistently high mortality and morbidity rates of hepatocellular carcinoma (HCC) remain a global concern. Notably, the disruptions in mitochondrial cholesterol metabolism (MCM) play a pivotal role in the progression and development of HCC, underscoring the significance of this metabolic pathway in the disease's etiology. The purpose of this research was to investigate genes associated with MCM and develop a model for predicting the prognostic features of patients with HCC.

Methods

MCM-related genes (MCMGs) were identified through The Cancer Genome Atlas (TCGA), The Molecular Signatures Database (MsigDB), and the Mitocarta3.0 databases. Differential gene expression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed using R software to construct a MCM-related model. This model underwent further analysis for somatic mutations, single sample gene set enrichment analysis (ssGSEA), stromal and immune cell estimation, immune checkpoint evaluation, and drug susceptibility prediction to assess the tumor microenvironment (TME) and therapeutic responses. The mRNA expression levels of the genes associated with the model were quantified using real-time fluorescence quantitative polymerase chain reaction (RT-qPCR).

Results

The model, which included six MCMGs (ACADL, ACLY, TXNRD1, DTYMK, ACAT1, and FLAD1), divided all patients (age ≤65 vs. >65 years, P<0.001; male vs. female, ns) into a high-risk group and a low-risk group. The high-risk group showed a higher mortality rate and lower survival rate with AUC of 0.785, 0.752, 0.756, 0.774 and 0.759 for the 1-, 2-, 3-, 4-, and 5-year respectively. A nomogram based on risk score, stage, T, and M had a better prognostic accuracy, with AUC of 0.808, 0.796, 0.811, 0.824 and 0.795 for the 1-, 2-, 3-, 4-, and 5-year respectively. The high-risk group showed enrichment in cell cycle, cell division, and chromosome processes, and a significantly higher tumor mutation burden (TMB) value compared to the low-risk group. Further immune infiltration analysis indicated a significantly reduction in the abundances of some immune cells (activated CD4 T cells, type 2 helper T cells, and neutrophils) and significantly higher expression levels of some immune checkpoint (CD80, CTLA4, HAVCR2, and TNFRSF4) in the high-risk group. Moreover, the risk score was associated with the response to immune checkpoint inhibitors (ICIs) therapy and efficiencies of multiple chemotherapy drugs.

Conclusions

This study developed a prognostic model based on MCMGs, which can predict the prognosis of liver cancer patients and their response to immunotherapy and chemotherapy. The model may provide new strategies to enhance the prognosis and treatment of HCC.

Long Non-Coding RNAs in Drug Resistance of Gastric Cancer: Complex Mechanisms and Potential Clinical Applications

Gastric cancer (GC) ranks as the third most prevalent malignancy and a leading cause of cancer-related mortality worldwide. However, the majority of patients with GC are diagnosed at an advanced stage, highlighting the urgent need for effective perioperative and postoperative chemotherapy to prevent relapse and metastasis. The current treatment strategies have limited overall efficacy because of intrinsic or acquired drug resistance. Recent evidence suggests that dysregulated long non-coding RNAs (lncRNAs) play a significant role in mediating drug resistance in GC. Therefore, there is an imperative to explore novel molecular mechanisms underlying drug resistance in order to overcome this challenging issue. With advancements in deep transcriptome sequencing technology, lncRNAs-once considered transcriptional noise-have garnered widespread attention as potential regulators of carcinogenesis, including tumor cell proliferation, metastasis, and sensitivity to chemo- or radiotherapy through multiple regulatory mechanisms. In light of these findings, we aim to review the mechanisms by which lncRNAs contribute to drug therapy resistance in GC with the goal of providing new insights and breakthroughs toward overcoming this formidable obstacle.

Development and verification of a novel immunogenic cell death-related signature for predicting the prognosis and immune infiltration in triple-negative breast cancer

BACKGROUND

Insufficient understanding of the pathogenesis and tumor immunology of triple-negative breast cancer (TNBC) has limited the development of immunotherapy. The importance of tumor microenvironment (TME) in immunotyping, prognostic assessment and immunotherapy efficacy of cancer has been emphasized, however, potential immunogenic cell death (ICD) related genes function in TME of TNBC has been rarely investigated.

AIMS

To initially explore the role and related mechanisms of ICD in TNBC, especially the role played in the TME of TNBC, and to identify different relevant subtypes based on ICD, and then develop an ICD-related risk score to predict each TNBC patient TME status, prognosis and immunotherapy response.

METHODS AND RESULTS

In this study, we identified distinct ICD-related modification patterns based on 158 TNBC cases in the TCGA-TNBC cohort. We then investigated the possible correlation between ICD-related modification patterns and TME cell infiltration characteristics in TNBC. By using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis, we created a risk scoring system (ICD score) to quantifiably evaluate the impact of ICD-related modification patterns in individual TNBC patient. Two different ICD-related modification patterns were found with significant differences in immune infiltration. Lower ICD score was correlated with higher immune infiltration, tumor mutational burden and significantly enriched in immune-related pathways, indicating a strong ability to activate immune response, which might account for relatively favorable prognosis of TNBC patients and could serve as a predictor to select suitable candidates for immunotherapy. We used two independent cohorts, GSE58812 cohort and Metabric cohort to validate prognosis and immunohistochemistry for preliminary in vitro validation.

CONCLUSION

This study evidenced that the ICD-related modification patterns might exert pivotal roles in the immune infiltration landscape of TNBC and ICD score might act as potential predictors of prognostic assessment and immunotherapy response. This research provides unique insights for individualize immune treatment strategies and promising immunotherapy candidates screening.

Comprehensive Molecular Analyses of a Macrophage-Related Gene Signature With Regard to Prognosis, Immune Features, and Biomarkers for Immunotherapy in Hepatocellular Carcinoma Based on WGCNA and the LASSO Algorithm

Macrophages have been reported to exert a crucial role in hepatocellular carcinoma (HCC). This study aimed to explore the macrophage-related genes and establish a macrophage-related signature (MRS) model to predict the overall survival (OS) of patients with HCC based on these genes’ expression. We screened the macrophage-related gene module by weighted gene coexpression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was utilized for further selection, and the selected genes were entered into stepwise regression to develop the MRS model, which was further validated in the Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) datasets. We analyzed the biological phenotypes associated with macrophages in terms of functional enrichment, tumor immune signature, and tumor mutational signature. The patient’s response to immunotherapy was inferred by the tumor immune dysfunction and exclusion (TIDE) score, the immunophenotype score (IPS), and the IMvigor210 dataset. A novel MRS model was established based on the LASSO regression coefficients of the genes PON1, IL15RA, NEIL3, HILPDA, PFN2, HAVCR1, ANXA10, CDCA8, EPO, S100A9, TTK, KLRB1, SPP1, STC2, CYP26B1, GPC1, G6PD, and CBX2. In either dataset, MRS was identified as an independent risk factor for OS in HCC patients. Additionally, our research indicated that a high-risk score in the MRS model was significantly correlated with tumor staging, pathological grade, tumor–node–metastasis (TNM) stage, and survival. Several genes of the human leukocyte antigen (HLA) family and immune checkpoints were highly expressed in the high-risk group. In addition, the frequency of tumor mutations was also higher in the high-risk group. According to our analyses, a higher risk score in the MRS model may predict a better response to immunotherapy.