Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma

Therapeutic and Prognostic Potential of G Protein-Coupled Receptors in Lung Adenocarcinoma: Evidence From Transcriptome Data and In Vitro Experiments

BACKGROUND

G protein-coupled receptors (GPCRs), the largest family of cell-surface molecules involve in various signal transduction, have recently been recognized as important drivers of cancer. However, few studies have reported on the potential of GPCRs as therapeutic targets or biomarkers in lung adenocarcinoma (LUAD).

METHODS

The expression profiles and clinical data of LUAD in the GSE30219 and GSE18842 datasets of the Cancer Genome Atlas were analyzed. LUAD-associated module genes were screened utilizing weighted gene co-expression network analysis (WGCNA). Prognostic signature genes were identified by univariate Cox survival analysis, LASSO regression, and multivariate Cox regression analyses. The immune status was evaluated and drug sensitivity was determined, conducting in vitro experiments for validation.

RESULTS

Patients with LUAD exhibited lower GPCR score than the controls, and 38 dysregulated GPCRs were identified by screening with differential analysis and WGCNA module genes. An optimal prognostic signature was identified, including OR51E1, LGR4, ADRB1, ADGRD1, and ADGRE3. The model established based on these five genes harbored moderate predictive performance for the survival of patients with LUAD. The risk score was negatively correlated with the infiltrating levels of multiple immune cells, including M2 macrophages, myeloid dendritic cells, and neutrophils, but positively correlated with fewer immune cells, such as Th1/Th2 CD4 + T cell. ADGRE3 and OR51E1 expression was positively correlated with drug sensitivity, including to cisplatin, ribociclib, and pevonedistat. Silencing OR51E1 inhibited the malignant cytological behaviors of LUAD cells.

CONCLUSION

GPCRs demonstrated prognostic potential in LUAD, with five genes identified as potential therapeutic targets and prognostic biomarkers for LUAD.

Targeting tumor-associated macrophages in colon cancer: mechanisms and therapeutic strategies

Colon cancer (CC) remains a primary contributor to cancer-related fatalities worldwide, driven by difficulties in early diagnosis and constrained therapeutic options. Recent studies underscore the importance of the tumor microenvironment (TME), notably tumor-associated macrophages (TAMs), in fostering malignancy progression and therapy resistance. Through their inherent plasticity, TAMs facilitate immunosuppression, angiogenic processes, metastatic spread, and drug tolerance. In contrast to M1 macrophages, which promote inflammatory and tumoricidal responses, M2 macrophages support tumor expansion and dissemination by exerting immunosuppressive and pro-angiogenic influences. Consequently, manipulating TAMs has emerged as a potential avenue to enhance treatment effectiveness. This review outlines the origins, polarization states, and functions of TAMs in CC, highlights their role in driving tumor advancement, and surveys ongoing efforts to target these cells for better patient outcomes. Emerging therapeutic strategies aimed at modulating TAM functions - including depletion strategies, reprogramming approaches that shift M2-polarized TAMs toward an M1 phenotype, and inhibition of key signaling pathways sustaining TAM-mediated immunosuppression-are currently under active investigation. These approaches hold promise in overcoming TAM - induced resistance and improving immunotherapeutic efficacy in CC.

Leveraging Single-Cell Multi-Omics to Decode Tumor Microenvironment Diversity and Therapeutic Resistance

Recent developments in single-cell multi-omics technologies have provided the ability to identify diverse cell types and decipher key components of the tumor microenvironment (TME), leading to important advancements toward a much deeper understanding of how tumor microenvironment heterogeneity contributes to cancer progression and therapeutic resistance. These technologies are able to integrate data from molecular genomic, transcriptomic, proteomics, and metabolomics studies of cells at a single-cell resolution scale that give rise to the full cellular and molecular complexity in the TME. Understanding the complex and sometimes reciprocal relationships among cancer cells, CAFs, immune cells, and ECs has led to novel insights into their immense heterogeneity in functions, which can have important consequences on tumor behavior. In-depth studies have uncovered immune evasion mechanisms, including the exhaustion of T cells and metabolic reprogramming in response to hypoxia from cancer cells. Single-cell multi-omics also revealed resistance mechanisms, such as stromal cell-secreted factors and physical barriers in the extracellular matrix. Future studies examining specific metabolic pathways and targeting approaches to reduce the heterogeneity in the TME will likely lead to better outcomes with immunotherapies, drug delivery, etc., for cancer treatments. Future studies will incorporate multi-omics data, spatial relationships in tumor micro-environments, and their translation into personalized cancer therapies. This review emphasizes how single-cell multi-omics can provide insights into the cellular and molecular heterogeneity of the TME, revealing immune evasion mechanisms, metabolic reprogramming, and stromal cell influences. These insights aim to guide the development of personalized and targeted cancer therapies, highlighting the role of TME diversity in shaping tumor behavior and treatment outcomes.

Exploring tumor microenvironment in molecular subtyping and prognostic signatures in ovarian cancer and identification of SH2D1A as a key regulator of ovarian cancer carcinogenesis

Introduction

A deadly gynecological cancer, ovarian cancer (OV), has a poor prognosis because of late-stage diagnosis and few targeted therapies. Addressing the tumor microenvironment (TME) in solid tumors has shown promise since it is crucial in promoting cancer progression.

Methods

We obtained bulk RNA-seq data from TCGA-OV, GSE26712, GSE102073, and ICGC cohorts, as well as scRNA-seq data from EMTAB8107, GSE118828, GSE130000, and GSE154600 cohorts using the TISCH2 database. The ConsensusClusterPlus package was used to cluster the OV tumor tissues hierarchically to determine two molecularly different groups (C1 and C2). A total of ten different types of machine learning techniques with 101 combinations were used for prognostic model construction. Using eight TME algorithms integrated into the IOBR R package, the bulk RNA-seq dataset was analyzed. For in vitro experiments, OVCAR3 and SKOV3, two OV cell lines, were used. The migratory potential of the ovarian cancer cells was assessed using Transwell assay, while proliferation was assessed using CCK8 assay.

Results

Based on TME-related gene set expression, two distinct molecular subgroups (C1 and C2) were identified through consensus clustering, with C1 showing higher TME activity. Further analysis indicated that C1 had increased cancer-associated fibroblasts (CAFs), M1 macrophages, and CD8+ T cells, suggesting a more activated and pro-inflammatory TME. Drug sensitivity analysis revealed that 5-Fluorouracil might be beneficial to C1 patients. Functional differences between C1 and C2 were identified, including cell adhesion, mononuclear cell differentiation, and leukocyte migration. A machine learning model was developed to create a TME-related prognostic signature, demonstrating strong prognostic capabilities across multiple datasets. High-risk patients showed a more immune-suppressive TME and higher tumor stemness. ScRNA-seq disclosed a highly activated TME-related signature in OV. Cancer cell lines had significantly higher SH2D1A mRNA expression than normal ovarian epithelial cells. We observed that SH2D1A knockdown in 2 ovarian cancer cell lines (OVCAR3 and SKOV3) reduced migration and proliferation through a series of in-vitro experiments.

Conclusion

TME-associated genes were efficient in ovarian cancer molecular subtyping. A TME-based prognosis model was constructed for vigorous prognostic stratification efficacy across multiple datasets. Moreover, we identified a pivotal role of SH2D1A in promoting proliferation and migration in ovarian cancer.

Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition

Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.

Comprehensive single-cell transcriptomic profiling reveals molecular subtypes and prognostic biomarkers with implications for targeted therapy in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a genetically heterogeneous disease with poor clinical outcomes. Identification of biomarkers linked to DNA replication stress may enable improved prognostic risk stratification and guide therapeutic decision making. We performed integrated single-cell RNA sequencing and computational analyses to define the molecular determinants and subtypes underlying ESCC heterogeneity.

METHODS

Single-cell RNA sequencing was performed on ESCC samples and analyzed using Seurat. Differential gene expression analysis was used to identify esophageal cell phenotypes. DNA replication stress-related genes were intersected with single-cell differential expression data to identify potential prognostic genes, which were used to generate a DNA replication stress (DRS) score. This score and associated genes were evaluated in survival analysis. Putative prognostic biomarkers were evaluated by Cox regression and consensus clustering. Mendelian randomization analyses assessed the causal role of PRKCB.

RESULTS

High DRS score associated with poor survival. Four genes (CDKN2A, NUP155, PPP2R2A, PRKCB) displayed prognostic utility. Three molecular subtypes were identified with discrete survival and immune properties. A 12-gene signature displayed robust prognostic performance. PRKCB was overexpressed in ESCC, while PRKCB knockdown reduced ESCC cell migration.

CONCLUSIONS

This integrated single-cell sequencing analysis provides new insights into the molecular heterogeneity and prognostic determinants underlying ESCC. The findings identify potential prognostic biomarkers and a gene expression signature that may enable improved patient risk stratification in ESCC. Experimental validation of the role of PRKCB substantiates the potential clinical utility of our results.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

Cancer remains a leading cause of mortality worldwide and calls for novel therapeutic targets. Membrane proteins are key players in various cancer types but present unique challenges compared to soluble proteins. The advent of computational drug discovery tools offers a promising approach to address these challenges, allowing for the prioritization of "wet-lab" experiments. In this review, we explore the applications of computational approaches in membrane protein oncological characterization, particularly focusing on three prominent membrane protein families: receptor tyrosine kinases (RTKs), G protein-coupled receptors (GPCRs), and solute carrier proteins (SLCs). We chose these families due to their varying levels of understanding and research data availability, which leads to distinct challenges and opportunities for computational analysis. We discuss the utilization of multi-omics data, machine learning, and structure-based methods to investigate aberrant protein functionalities associated with cancer progression within each family. Moreover, we highlight the importance of considering the broader cellular context and, in particular, cross-talk between proteins. Despite existing challenges, computational tools hold promise in dissecting membrane protein dysregulation in cancer. With advancing computational capabilities and data resources, these tools are poised to play a pivotal role in identifying and prioritizing membrane proteins as personalized anticancer targets.

Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index

Introduction

Melanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction.

Methods

In this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment.

Results

Notably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature.

Discussion

Our findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.

Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma

Background

We explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked to aggressive behavior in various cancers, including SKCM. However, the exact role and mechanism of sphingolipid metabolism in melanoma remain partially understood.

Methods

We integrated scRNA-seq data from melanoma patients sourced from the GEO database. Through the utilization of the Seurat R package, we successfully identified distinct gene clusters associated with patient survival in the scRNA-seq data. Key prognostic genes were identified through single-factor Cox analysis and used to develop a prognostic model using LASSO and stepwise regression algorithms. Additionally, we evaluated the predictive potential of these genes within the immune microenvironment and their relevance to immunotherapy. Finally, we validated the functional significance of the high-risk gene IRX3 through in vitro experiments.

Results

Analysis of scRNA-seq data identified distinct expression patterns of 4 specific genes (SRGs) in diverse cell subpopulations. Re-clustering cells based on increased SRG expression revealed 7 subgroups with significant prognostic implications. Using marker genes, lasso, and Cox regression, we selected 11 genes to construct a risk signature. This signature demonstrated a strong correlation with immune cell infiltration and stromal scores, highlighting its relevance in the tumor microenvironment. Functional studies involving IRX3 knockdown in A375 and WM-115 cells showed significant reductions in cell viability, proliferation, and invasiveness.

Conclusion

SRG-based risk signature holds promise for precise melanoma prognosis. An in-depth exploration of SRG characteristics offers insights into immunotherapy response. Therapeutic targeting of the IRX3 gene may benefit melanoma patients.

Decoding the metastatic potential and optimal postoperative adjuvant therapy of melanoma based on metastasis score

Metastasis is a formidable challenge in the prognosis of melanoma. Accurately predicting the metastatic potential of non-metastatic melanoma (NMM) and determining effective postoperative adjuvant treatments for inhibiting metastasis remain uncertain. In this study, we conducted comprehensive analyses of melanoma metastases using bulk and single-cell RNA sequencing data, enabling the construction of a metastasis score (MET score) through diverse machine-learning algorithms. The reliability and robustness of the MET score were validated using various in vitro assays and in vivo models. Our findings revealed a distinct molecular landscape in metastatic melanoma characterized by the enrichment of metastasis-related pathways, intricate cell-cell communication, and heightened infiltration of pro-angiogenic tumor-associated macrophages compared to NMM. Importantly, patients in the high MET score group exhibited poorer prognoses and an immunosuppressive microenvironment, featuring increased infiltration of regulatory T cells and decreased infiltration of CD8+ T cells, compared to the low MET score patient group. Expression of PD-1 was markedly higher in patients with low MET scores. Anti-PD-1 (aPD-1) therapy profoundly affected antitumor immunity activation and metastasis inhibition in these patients. In summary, our study demonstrates the effectiveness of the MET score in predicting melanoma metastatic potential. For patients with low MET scores, aPD-1 therapy may be a potential treatment strategy to inhibit metastasis. Patients with high MET scores may benefit from combination therapies.