Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma

Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities

Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.

IDH mutation, glioma immunogenicity, and therapeutic challenge of primary mismatch repair deficient IDH-mutant astrocytoma PMMRDIA: a systematic review

In 2021, Suwala et al. described Primary Mismatch Repair Deficient IDH-mutant Astrocytoma (PMMRDIA) as a distinct group of gliomas. In unsupervised clustering, PMMRDIA forms distinct cluster, separate from other IDH-mutant gliomas, including IDH-mutant gliomas with secondary mismatch repair (MMR) deficiency. In the published cohort, three patients received treatment with an immune checkpoint blocker (ICB), yet none exhibited a response, which aligns with existing knowledge about the decreased immunogenicity of IDH-mutant gliomas in comparison to IDH-wildtype. In the case of PMMRDIA, the inherent resistance to the standard-of-care temozolomide caused by MMR deficiency is an additional challenge. It is known that a gain-of-function mutation of IDH1/2 genes produces the oncometabolite R-2-hydroxyglutarate (R-2-HG), which increases DNA and histone methylation contributing to the characteristic glioma-associated CpG island methylator phenotype (G-CIMP). While other factors could be involved in remodeling the tumor microenvironment (TME) of IDH-mutant gliomas, this systematic review emphasizes the role of R-2-HG and the subsequent G-CIMP in immune suppression. This highlights a potential actionable pathway to enhance the response of ICB, which might be relevant for addressing the unmet therapeutic challenge of PMMRDIA.

Lipid metabolism-related gene expression in the immune microenvironment predicts prognostic outcomes in renal cell carcinoma

Background

Rates of renal cell carcinoma (RCC) occurrence and mortality are steadily rising. In an effort to address this issue, the present bioinformatics study was developed with the goal of identifying major lipid metabolism biomarkers and immune infiltration characteristics associated with RCC cases.

Methods

The Cancer Genome Atlas (TCGA) and E-MTAB-1980 were used to obtain matched clinical and RNA expression data from patients diagnosed with RCC. A LASSO algorithm and multivariate Cox regression analyses were employed to design a prognostic risk model for these patients. The tumor immune microenvironment (TIME) in RCC patients was further interrogated through ESTIMATE, TIMER, and single-cell gene set enrichment analysis (ssGSEA) analyses. Gene Ontology (GO), KEGG, and GSEA enrichment approaches were further employed to gauge the mechanistic basis for the observed results. Differences in gene expression and associated functional changes were then validated through appropriate molecular biology assays.

Results

Through the approach detailed above, a risk model based on 8 genes associated with RCC patient overall survival and lipid metabolism was ultimately identified that was capable of aiding in the diagnosis of this cancer type. Poorer prognostic outcomes in the analyzed RCC patients were associated with higher immune scores, lower levels of tumor purity, greater immune cell infiltration, and higher relative immune status. In GO and KEGG enrichment analyses, genes that were differentially expressed between risk groups were primarily related to the immune response and substance metabolism. GSEA analyses additionally revealed that the most enriched factors in the high-risk group included the stable internal environment, peroxisomes, and fatty acid metabolism. Subsequent experimental validation in vitro and in vivo revealed that the most significantly differentially expressed gene identified herein, ALOX5, was capable of suppressing RCC tumor cell proliferation, invasivity, and migration.

Conclusion

In summary, a risk model was successfully established that was significantly related to RCC patient prognosis and TIME composition, offering a robust foundation for the development of novel targeted therapeutic agents and individualized treatment regimens. In both immunoassays and functional analyses, dysregulated lipid metabolism was associated with aberrant immunological activity and the reprogramming of fatty acid metabolic activity, contributing to poorer outcomes.

Positive regulators of T cell functions as predictors of prognosis and microenvironment characteristics of low-grade gliomas

Background

Low-grade gliomas (LGG) are one of the most prevalent types of brain cancers. The efficacy of immunotherapy in LGG is limited compared to other cancers. Immunosuppression in the tumor microenvironment (TME) of LGG is one of the main reasons for the low efficacy of immunotherapy. Recent studies have identified 33 positive regulators of T cell functions (TPRs) that play a critical role in promoting the proliferation, activity, and functions of multiple immunocytes. However, their role in the TME of LGG has not been investigated. This study aimed to construct a risk model based on these TPRs and to detect the significance of immunotypes in predicting LGG prognosis and immunotherapy efficacy.

Methods

A total of 688 LGGs and 202 normal brain tissues were extracted from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Genotype-Tissue Expression (GTEx) databases. The NMF R package was used to identify TRP-related subtypes. The TPR prognostic model was established using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival of LGG samples.

Results

The Subtype 2 patients had worse survival outcomes, suppressed immune function, and higher immune cell infiltration. A risk regression model consisting of 14 TPRs was established, and its performance was validated in CGGA325 cohorts. The low-risk group exhibited better overall survival, immune microenvironment, and immunotherapy response, as determined via the TIDE algorithm, indicating that increasing the level of immune infiltration can effectively improve the response to immunotherapy in the low-risk group. The risk score was determined to be an independent hazard factor (p<0.001) although other clinical features (age, sex, grade, IDH status, 1p19q codel status, MGMT status, and accepted radiotherapy) were considered. Lastly, high-risk groups in both cohorts revealed optimal drug responses to rapamycin, paclitaxel, JW-7-52-1, and bortezomib.

Conclusions

Our study identified two distinct TPR subtypes and built a TPR signature to elucidate the characteristics of T cell proliferation in LGG and its association with immune status and prognosis. These findings shed light on possible immunotherapeutic strategies for LGGs.

Identification of Novel m6A-Related Long Non-Coding RNA Signatures for Cholangiocarcinoma Using Integrated Bioinformatics Analyses

Traditional methods used to treat cholangiocarcinoma are less effective, and the identification of new CHOL signature genes can help in the early clinical diagnosis and intervention of cholangiocarcinoma. In this work, we used integrated bioinformatics analysis to find new m6a-associated lncRNA signatures in cholangiocarcinoma. Pearson correlation test was used to identify m6A-lncRNAs by co-expression analysis of m6A-mrna and lncRNAs. we then selected m6A-lncRNAs co-expressed with METTL3 and METTL14 genes and screened for DEm6A-lncRNAs by comparing expression differences. we then used R package of Spearman coefficient correlation analysis to investigate the relevance of m6A-lncrna expression in CHOL. To determine the relative levels of immune cell infiltration, we performed ssGSEA analysis on all samples using the R package, and then we used graphs to illustrate the differences in immune cell infiltration between the CHOL and NC groups. The results of this study will help to identify new CHOL-causing biosignatures, which are important for the early clinical detection and management of CHOL.

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.

Identification of Immune-Related lncRNAs for Predicting Prognosis and Immune Landscape Characteristics of Uveal Melanoma

Immune-related genes and long noncoding RNAs (lncRNAs) have a significant impact on the prognostic value and immunotherapeutic response of uveal melanoma (UM). Therefore, we tried to develop a prognostic model on the basis of irlncRNAs for predicting prognosis and response on immunotherapy of UM patients. We identified 1,664 immune-related genes and 2,216 immune-related lncRNAs (irlncRNAs) and structured a prognostic model with 3 prognostic irlncRNAs by co-expression analysis, univariable Cox, LASSO, and multivariate Cox regression analyses. The Kaplan–Meier analysis indicated that patients in the high-risk group had a shorter survival time than patients in the low-risk group. The ROC curves demonstrated the high sensitivity and specificity of the signature for survival prediction, and the one-, three-, and five-year AUC values, respectively, were 0.974, 0.929, and 0.941 in the entire set. Cox regression analysis, C-index, DCA, PCA analysis, and nomogram were also applied to assess the validity and accuracy of the risk model. The GO and KEGG enrichment analyses indicated that this signature is significantly related to immune-related pathways and molecules. Finally, we investigated the immunological characteristics and immunotherapy of the model and identified various novel potential compounds in the model for UM. In summary, we constructed a new model on the basis of irlncRNAs that can accurately predict prognosis and response on immunotherapy of UM patients, which may provide valuable clinical applications in antitumor immunotherapy.

Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm

This study aimed to investigate the immune landscape in hepatoblastoma (HB) based on deconvolution methods and identify a biomarkers panel for diagnosis based on a machine learning algorithm. Firstly, we identified 277 differentially expressed genes (DEGs) and differentiated and functionally identified the modules in DEGs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GO (gene ontology) were used to annotate these DEGs, and the results suggested that the occurrence of HB was related to DNA adducts, bile secretion, and metabolism of xenobiotics by cytochrome P450. We selected the top 10 genes for our final diagnostic panel based on the random forest tree method. Interestingly, TNFRSF19 and TOP2A were significantly down-regulated in normal samples, while other genes (TRIB1, MAT1A, SAA2-SAA4, NAT2, HABP2, CYP2CB, APOF, and CFHR3) were significantly down-regulated in HB samples. Finally, we constructed a neural network model based on the above hub genes for diagnosis. After cross-validation, the area under the ROC curve was close to 1 (AUC = 0.972), and the AUC of the validation set was 0.870. In addition, the results of single-sample gene-set enrichment analysis (ssGSEA) and deconvolution methods revealed a more active immune responses in the HB tissue. In conclusion, we have developed a robust biomarkers panel for HB patients.

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

Purpose

Mucinous ovarian cancer (MOC) is a rare histological type of EOC. In order to guide the clinical diagnosis and management of MOC patients, we constructed and verified a nomogram for the estimation of overall survival in patients with MOC.

Patients and Methods

We collected 494 patients with MOC diagnosed from 2010 to 2015 in SEER database, and the following main inclusion criteria were used: (1) patients whose MOC was confirmed by pathology; (2) patients without a history of primary other cancer. Subsequently, we performed randomized grouping (6:4) and Cox hazard regression analysis in the training group. Subsequently, the nomogram was established. A variety of indicators were used to validate the prognosis value of nomogram, including the C-index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Moreover, Kaplan–Meier analysis was used to compare the survival results among different risk subgroups.

Results

Cox hazard regression analysis revealed that age, grade, FIGO stage and log odds of positive lymph nodes stage were independent risk factors for patients with MOC. In the training group, the C-index of the nomogram was 0.827 (95% CI: 0.791–0.863) and the areas under the curve (AUC) predicting the 1-, 3- and 5-year survival rate were 0.853 (95% CI: 0.791–0.915), 0.886 (95% CI: 0.852–0.920) and 0.815 (95% CI: 0.766–0.864), respectively. The calibration curve revealed that the nomogram of the 1-, 3- and 5-year survival rate was consistent with the actual fact. Patients with high risk had a poorer prognosis than those with low risk (P < 0.001). DCA revealed that the nomogram had the best clinical value than other classical prognostic markers. Similarly, nomogram had excellent prognostic ability in the testing group.

Conclusion

The nomogram was constructed to predict overall survival in patients with MOC, which had the significance for clinical evaluation.