Identification of glioblastoma immune subtypes and immune landscape based on a large cohort

Immunological Roles of CCL18 in Pan‑Cancer and Its Potential Value in Endometrial Cancer

Endometrial cancer (EC) is one of the most prevalent malignancies in the female reproductive system. However, the potential functions and mechanisms of immune-related genes in the onset and progression of EC remain unclear. The immune-related gene CCL18 has been implicated in apoptosis, proliferation, invasion, metastasis, and drug resistance in various types of tumors. Nevertheless, its role in pan-cancer has been poorly investigated, and its expression value and prognostic significance in endometrial cancer (EC) have not been explored. Therefore, the objective of this study was to identify potential immune-related prognostic biomarkers for EC by utilizing the cancer genome atlas (TCGA), immunology database and analysis portal (ImmPort) database, and Gene Expression Omnibus (GEO). Immunohistochemistry staining results from EC tissue chips demonstrated elevated expression levels of inflammatory chemokine protein 18 (CCL18) in EC compared to normal endometrium. This study offers a potential therapeutic strategy for EC treatment by identifying regulatory targets through microRNA sequencing data. Additionally, drug prediction was based on CCL18 targets. Furthermore, an analysis of CCL18 expression in pan-cancer was conducted, and the results revealed its high expression in various types of cancer, including EC and bladder cancer. Through analysis of the ATAC-seq data, we found that SIX1, SOX3, and TWIST2 may regulate CCL18 transcription by binding to the gene promoter of CCL18 in EC. This study indicated that CCL18 could be a potential biomarker in pan-cancer and EC.

Therapeutic Hypothermia in Treating Glioblastoma: A Review

Glioblastoma (GBM) is the most commonly occurring of all malignant central nervous system (CNS) tumors in adults. Considering the low median survival of only ∼15 months and poor prognosis in GBM patients, despite surgical resection with adjuvant radiation and chemotherapy, it is vital to seek brand new and innovative treatment in combination with already existing methods. Hypothermia participates in many metabolic pathways, inflammatory responses, and apoptotic processes, while also promoting the integrity of neurons. Following the successful application of therapeutic hypothermia across a spectrum of disorders such as traumatic CNS injury, cardiac arrest, and epilepsy, several clinical trials have set to evaluate the potency of hypothermia in treating a variety of cancers, including breast and ovaries cancer. In regard to primary neoplasms and more specifically, GBM, hypothermia has recently shown promising results as an auxiliary treatment, reinforcing chemotherapy's efficacy. In this review, we discuss the recent advances in utilizing hypothermia as treatment for GBM and other cancers.

Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies

Background

Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma.

Methods

A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis.

Results

Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed.

Conclusions

Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.

Immune Subtypes and Immune Landscape Analysis of Endometrial Carcinoma

Some patients with endometrial cancer (EC) suffer from limited survival benefits after immunotherapy, suggesting that there may be a specific pattern associated with immunotherapy. Immune-related genes were extracted from The Cancer Genome Atlas databases. We analyzed the differences among immune subtypes (ISs) in the distribution of the tumor mutational burden, chemotherapy-induced immune response markers, immune checkpoint-related genes, immunotherapy, and chemotherapy. We applied dimensionality reduction and defined the immune landscape of EC. Then, we used the Weighted Gene Co-Expression Network Analysis package to identify the coexpression modules of these immune genes. Finally, hub genes were selected and detected by quantitative PCR and immunohistochemistry. We obtained three ISs. There were differences in the distribution of the tumor mutational burden, chemotherapy-induced immune response markers, and immune checkpoint-related genes among the ISs. Regarding immunotherapy and chemotherapy, the IS2 subtypes were more sensitive to programmed cell death protein 1 inhibitors. In addition, different positions in the immune landscape map exhibited different prognostic characteristics, providing further evidence of the ISs. The IS2 subtypes were significantly positively correlated with yellow module gene list, indicating a good prognosis with high score. SIRPG and SLAMF1 were identified as the final characteristic genes. The quantitative PCR and immunohistochemistry results showed that the expression levels of SIRPG and SLAMF1 were low in human EC tissue. In this study, we identified three reproducible ISs of EC. The immune landscape analysis further revealed the intraclass heterogeneity of the ISs. SIRPG and SLAMF1 were identified to be associated with progression, suggesting that they may be novel immune-related biomarkers of EC.

A 13-gene signature to predict the prognosis and immunotherapy responses of lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) comprises 20–30% of all lung cancers. Immunotherapy has significantly improved the prognosis of LUSC patients; however, only a small subset of patients responds to the treatment. Therefore, we aimed to develop a novel multi-gene signature associated with the immune phenotype of the tumor microenvironment for LUSC prognosis prediction. We stratified the LUSC patients from The Cancer Genome Atlas dataset into hot and cold tumor according to a combination of infiltration status of immune cells and PD-L1 expression level. Kaplan–Meier analysis showed that hot tumors were associated with shorter overall survival (OS). Enrichment analyses of differentially expressed genes (DEGs) between the hot and cold tumors suggested that hot tumors potentially have a higher immune response ratio to immunotherapy than cold tumors. Subsequently, hub genes based on the DEGs were identified and protein–protein interactions were constructed. Finally, we established an immune-related 13-gene signature based on the hub genes using the least absolute shrinkage and selection operator feature selection and multivariate cox regression analysis. This gene signature divided LUSC patients into high-risk and low-risk groups and the former inclined worse OS than the latter. Multivariate cox proportional hazard regression analysis showed that the risk model constructed by the 13 prognostic genes was an independent risk factor for prognosis. Receiver operating characteristic curve analysis showed a moderate predictive accuracy for 1-, 3- and 5-year OS. The 13-gene signature also performed well in four external cohorts (three LUSC and one melanoma cohorts) from Gene Expression Omnibus. Overall, in this study, we established a reliable immune-related 13-gene signature that can stratify and predict the prognosis of LUSC patients, which might serve clinical use of immunotherapy.

Emerging Biomarkers for Immunotherapy in Glioblastoma

Simple Summary

Immunotherapy has shown clinical benefits in several solid cancers; still, glioblastoma remains very challenging to treat. Glioblastoma is the most frequent brain cancer and displays great heterogeneity. The standard of care has remained the same for over fifteen years, and to overcome the therapeutic limitations, emerging immune correlates of therapy responses and improved prognosis should be further developed for a more personalized therapy approach and increased clinical responses.

Abstract

Immunotherapy has shown clinical benefits in several solid malignancies—in particular, melanoma and non-small cell lung cancer. However, in other solid tumours such as glioblastoma (GBM), the response to immunotherapy has been more variable, and except for anti-PD-1 for patients with microsatellite instable (MSI)+ cancers, no immunotherapy is currently approved for GBM patients. GBM is the most common and most aggressive brain cancer with a very poor prognosis and a median overall survival of 15 months. A few prognostic biomarkers have been identified and are used to some extent, but apart from MSI, no biomarkers are used for patient stratification for treatments other than the standard of care, which was established 15 years ago. Around 25% of new treatments investigated in GBM are immunotherapies. Recent studies indicate that the use of integrated and validated immune correlates predicting the response and guiding treatments could improve the efficacy of immunotherapy in GBM. In this review, we will give an overview of the current status of immunotherapy and biomarkers in use in GBM with the main challenges of treatment in this disease. We will also discuss emerging biomarkers that could be used in future immunotherapy strategies for patient stratification and potentially improved treatment efficacy.

Epithelial-Mesenchymal Transition-Based Gene Signature and Distinct Molecular Subtypes for Predicting Clinical Outcomes in Breast Cancer

Purpose

Regulation of inducers and transcription factor families influence epithelial–mesenchymal transition (EMT), a contributing factor to breast cancer invasion and progression.

Methods

Molecular subtypes were classified based on EMT-related mRNAs using ConsensusClusterPlus package. Differences in tumor immune microenvironment and prognosis were assessed among subtypes. Based on EMT genes, a gene signature for prognosis was built using TCGA training set by performing multivariate and univariate Cox regression analyses. Prediction accuracy of the signature was validated by receiver operating characteristic (ROC) curves and overall survival analysis on internal and external datasets. By conducting univariate and multivariate Cox regression analyses, the risk signature as an independent prognostic indicator was assessed. A nomogram was constructed and validated by calibration analysis and decision curve analysis (DCA).

Results

Five molecular subtypes were characterized based on EMT genes. Patients in Cluster 2 exhibited an activated immune state and a better prognosis. An 11-EMT gene-signature was built to predict breast cancer prognosis. After validation, the signature showed independence and robustness in predicting clinical outcomes of patients. A nomogram combining the RiskScore and pTNM_stage accurately predicted 1-, 2-, 3-, and 5-year survival chance. In comparison with published model, the current model showed a higher area under the curve (AUC).

Conclusion

We characterized five breast cancer subtypes with distinct clinical outcomes and immune status. The study developed an 11-EMT gene-signature as an independent prognostic factor for predicting clinical outcomes of breast cancer.