Immunological Characteristics of Alternative Splicing Profiles Related to Prognosis in Bladder Cancer

Single-cell combined with bulk-RNA data reveal a pattern related to angiogenesis in breast cancer patients: Individualized medicine

Angiogenesis contributes to tumor progression, aggressive behavior, and metastasis. Although several endothelial dysfunction genes (angiogenesis-related genes [ARGs]) have been identified as diagnostic biomarkers of breast cancer in a few studies, the mixed effects of ARGs have not been thoroughly investigated. The RNA sequencing data and patient survival datasets of breast cancer were obtained for further analysis. MSigDB website includes angiogenesis-related mechanisms. The consensus clustering analysis identifies 1082 breast cancer patients as three clusters. differential expression genes (DEGs) were identified by limma package. GO combined with gene set enrichment analysis (GSEA) to identify cytogenetic functions between two predefined clusters. Then Serpin Family F Member 1 (SERPINF1), angiomotin (AMOT), promyelocytic leukemia (PML), and BTG anti-proliferation factor 1 (BTG) were selected to construct prediction models using random forest survival analysis. External validation was performed using the GSE58812 triple-negative breast cancer cohort as the validation set. The median scoring system was used to discern the high- and low-risk groups, and there was a significant difference in their diagnostic results. Immunological infiltration scores were calculated using single sample gene set enrichment analysis (ssGSEA) and xCell algorithms, and consciousness scores were calculated using the R package "oncoPredict" for drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. In addition, the single-cell analysis of seven triple-negative breast cancers using scRNA-seq information from GSE118389 demonstrated the interpretation of SERPINF1, AMOT, PML, and BTG1. In conclusion, this investigation engineered ARG-centric disease paradigms that not only prognosticated prospective therapeutic compounds, but also projected their mechanistic trajectories, thereby facilitating the proposition of tailored treatments within diverse patient cohorts diagnosed with breast cancer.

Pathogenomics model for personalized medicine in cervical cancer: Cross-talk of gene expressions and pathological images related to oxidative stress

An increasing number of studies have shown that oxidative stress plays an important role in the development and progression of cancer. Cervical cancer (CC) is a disease of unique complexity that tends to exhibit high heterogeneity in molecular phenotypes. We aim here to characterize molecular features of cervical cancer by developing a classification system based on oxidative stress-related gene expression profiles. In this study, we obtained gene expression profiling data for cervical cancer from the TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) (GSE44001) databases. Oxidative stress-related genes used for clustering were obtained from GeneCards. Patients with cervical cancer were divided into two subtypes (C1 and C2) by non-negative matrix factorization (NMF) classification. By performing Kaplan-Meier survival analysis, differential expression analysis, and gene set enrichment analysis (GSEA) between the two subtypes, we found that subtype C2 had a worse prognosis and was highly enriched for immune-related pathways as well as epithelial-mesenchymal transition (EMT) pathways. Subsequently, we performed metabolic pathway analysis, gene mutation landscape analysis, immune microenvironment analysis, immunotherapy response analysis, and drug sensitivity analysis of the two isoforms. The results showed that the isoforms were significantly different between metabolic pathway enrichment and the immune microenvironment, and the chromosomes of subtype C1 were more unstable. In addition, we found that subtype C2 tends to respond to treatment with anti-CTLA4 agents, a conclusion that coincides with high chromosomal variation in C1, as well as C2 enrichment of immune-related pathways. Then, we screened 10 agents that were significantly susceptible to C2 subtype. Finally, we constructed pathogenomics models based on pathological features and linked them to molecular subtypes. This study establishes a novel CC classification based on gene expression profiles of oxidative stress-related genes and elucidates differences between immune microenvironments between CC subtypes, contributing to subtype-specific immunotherapy and drug therapy.

Prognostic evaluation of the novel blueprint of DNA methylation sites by integrating bulk RNA-sequencing and methylation modification data in endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is a prevalent malignancy affecting the female population, with an increasing incidence among younger age groups. DNA methylation, a common epigenetic modification, is well-established to play a key role in cancer progression. We suspected whether DNA methylation could be used as biomarkers for EC prognosis.

METHODS

In the present study, we analyzed bulk RNA-sequencing data from 544 EC patients and DNA methylation data from 430 EC patients in the TCGA-UCEC cohort. We applied weighted correlation network analysis to select a key gene set associated with panoptosis. We conducted correlation analysis between transcriptomic data of the selected key genes and DNA methylation data to identify valuable DNA methylation sites. These sites were further screened by Cox regression and least absolute shrinkage and selection operator analysis. Immune microenvironment differences between high-risk and low-risk groups were assessed using single-sample gene set enrichment analysi, xCell and MCPcounter algorithms.

RESULTS

Our results identified five DNA methylation sites (cg03906681, cg04549977, cg06029846, cg10043253 and cg15658376) with significant prognostic value in EC. We constructed a prognostic model using these sites, demonstrating satisfactory predictive performance. The low-risk group showed higher immune cell infiltration. Notably, methylation of site cg03906681 was negatively related to CD8 T cell infiltration, whereas cg04549977 exhibited positive correlations with immune infiltration, particularly in macrophages, activated B cells, dendritic cells and myeloid-derived suppressor cells. PD0325901_1060 was strongly correlated with risk scores, indicating a potential therapeutic response for high-risk EC patients.

CONCLUSION

We have developed a robust DNA methylation-based prognostic model for EC, which holds promise for improving prognosis prediction and personalized treatment approaches. These findings may contribute to better management of EC patients, particularly in identifying those at higher risk who may benefit from tailored interventions.

Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.

METHODS

We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).

RESULTS

The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.

CONCLUSIONS

In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.

Preoperative Systemic Inflammatory Markers as a Significant Prognostic Factor After TURBT in Patients with Non-Muscle-Invasive Bladder Cancer

Introduction

Neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and lymphocyte/monocyte ratio (LMR) have been widely proposed to have predictive value for the patient prognosis of many malignancies, including bladder cancer. However, the predictive value of their combination in non-muscle-invasive bladder cancer (NMIBC) is unclear.

Methods

Cases of NMIBC patients who underwent transurethral resection of the bladder tumor were recruited from two tertiary public medical centers. A systemic inflammatory marker (SIM) score was calculated based on comprehensive consideration of NLR, PLR, and LMR. Recurrence-free survival (RFS) and progression-free survival (PFS) were estimated by Kaplan-Meier analysis. The Log rank test was used to compare differences between the groups. Cox regression was used to screen risk factors affecting RFS and PFS. Nomogram models were established and validated, and patients were stratified based on the model scores.

Results

The study dataset was grouped according to a 7:3 randomization, with the training cohort consisting of 292 cases and the validation cohort consisting of 124 cases. Cox regression analysis showed that SIM score is an independent predictor of RFS and PFS in NMIBC patients. The novel models were established based on the SIM score and other statistically significant clinicopathological features. The area under the curve (AUC) for predicting 1-, 2-, and 3-year RFS was 0.667, 0.689, and 0.713, respectively. The AUC for predicting 1-, 2-, and 3-year PFS was 0.807, 0.775, and 0.862, respectively. Based on the risk stratification, patients at high risk of recurrence and progression could be accurately identified. The established models were applied to the patient evaluation of the validation cohort, which proved the great performance of the novel models.

Conclusion

The novel models based on the SIM score and clinicopathological characteristics can accurately predict the survival prognosis of NMIBC patients, and the models can be used by clinicians for individualized patient assessment and to assist in clinical decision-making.

Identification and Validation of NK Marker Genes in Ovarian Cancer by scRNA-seq Combined with WGCNA Algorithm

Background

As an innate immune system effector, natural killer cells (NK cells) play a significant role in tumor immunotherapy response and clinical outcomes.

Methods

In our investigation, we collected ovarian cancer samples from TCGA and GEO cohorts, and a total of 1793 samples were included. In addition, four high-grade serous ovarian cancer scRNA-seq data were included for screening NK cell marker genes. Weighted gene coexpression network analysis (WGCNA) identified core modules and central genes associated with NK cells. The "TIMER," "CIBERSORT," "MCPcounter," "xCell," and "EPIC" algorithms were performed to predict the infiltration characteristics of different immune cell types in each sample. The LASSO-COX algorithm was employed to build risk models to predict prognosis. Finally, drug sensitivity screening was performed.

Results

We first scored the NK cell infiltration of each sample and found that the level of NK cell infiltration affected the clinical outcome of ovarian cancer patients. Therefore, we analyzed four high-grade serous ovarian cancer scRNA-seq data, screening NK cell marker genes at the single-cell level. The WGCNA algorithm screens NK cell marker genes based on bulk RNA transcriptome patterns. Finally, a total of 42 NK cell marker genes were included in our investigation. Among which, 14 NK cell marker genes were then used to develop a 14-gene prognostic model for the meta-GPL570 cohort, dividing patients into high-risk and low-risk subgroups. The predictive performance of this model has been well-verified in different external cohorts. Tumor immune microenvironment analysis showed that the high-risk score of the prognostic model was positively correlated with M2 macrophages, cancer-associated fibroblast, hematopoietic stem cell, stromal score, and negatively correlated with NK cell, cytotoxicity score, B cell, and T cell CD4+Th1. In addition, we found that bleomycin, cisplatin, docetaxel, doxorubicin, gemcitabine, and etoposide were more effective in the high-risk group, while paclitaxel had a better therapeutic effect on patients in the low-risk group.

Conclusion

By utilizing NK cell marker genes in our investigation, we developed a new feature that is capable of predicting patients' clinical outcomes and treatment strategies.

The role of hypoxia-related genes in TACE-refractory hepatocellular carcinoma: Exploration of prognosis, immunological characteristics and drug resistance based on onco-multi-OMICS approach

Transcatheter arterial chemoembolization (TACE) is an effective treatment for hepatocellular carcinoma (HCC). During TACE, chemotherapeutic agents are locally infused into the tumor and simultaneously cause hypoxia in tumor cells. Importantly, the poor effect of TACE in some HCC patients has been shown to be related to dysregulated expression of hypoxia-related genes (HRGs). Therefore, we identified 33 HRGs associated with TACE (HRGTs) by differential analysis and characterized the mutational landscape of HRGTs. Among 586 HCC patients, two molecular subtypes reflecting survival status were identified by consistent clustering analysis based on 24 prognosis-associated HRGs. Comparing the transcriptomic difference of the above molecular subtypes, three molecular subtypes that could reflect changes in the immune microenvironment were then identified. Ultimately, four HRGTs (CTSO, MMP1, SPP1, TPX2) were identified based on machine learning approachs. Importantly, risk assessment can be performed for each patient by these genes. Based on the parameters of the risk model, we determined that high-risk patients have a more active immune microenvironment, indicating “hot tumor” status. And the Tumor Immune Dysfunction and Exclusion (TIDE), the Cancer Immunome Atlas (TCIA), and Genome of Drug Sensitivity in Cancer (GDSC) databases further demonstrated that high-risk patients have a positive response to immunotherapy and have lower IC50 values for drugs targeting cell cycle, PI3K/mTOR, WNT, and RTK related signaling pathways. Finally, single-cell level analysis revealed significant overexpression of CTSO, MMP1, SPP1, and TPX2 in malignant cell after PD-L1/CTLA-4 treatment. In conclusion, Onco-Multi-OMICS analysis showed that HRGs are potential biomarkers for patients with refractory TACE, and it provides a novel immunological perspective for developing personalized therapies.