Identification of a Costimulatory Molecule Gene Signature to Predict Survival and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma

Dysregulated angiogenin and related pathways in the ventral midbrain of "redhead" mice with MC1R disruption

A relationship between the melanoma-related pigmentation gene melanocortin 1 receptor (MC1R) and Parkinson's disease (PD) has been previously suggested. The present study aims to investigate the gene expression pattern in the ventral midbrain (VMB) of MC1R extension (MC1Re/e) mice to provide insights into the underlying mechanism of dopaminergic neuron loss in these mice. RNA sequencing (RNA-seq) was conducted on VMB tissues from MC1Re/e mice and their wild-type (WT) C57BL/6J littermates. Gene expression levels and pathway activity were assessed using differential gene expression analysis, Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA). To validate the RNA-seq results, real-time quantitative polymerase chain reaction (RT-qPCR), Western blotting (WB), and ELISA were performed. Our analyses found significant transcriptomic differences in the VMB between MC1Re/e mice and WT controls. Several immune response-related pathways were identified to be downregulated in the MC1Re/e group. Angiogenin (ANG) was implicated in several of the enriched pathways in MC1Re/e mice. Furthermore, Ang was found to be significantly downregulated in the VMB of MC1Re/e mice, which was confirmed at both mRNA and protein levels. There was no significant difference in Ang protein levels in the serum of MC1Re/e and WT mice. Our results suggest a differential gene expression pattern in the VMB as a result of MC1R mutation. Notably, lower Ang expression may be involved in the neuronal loss observed in the VMB of the MC1Re/e mice.NEW & NOTEWORTHY Our study identifies reduced angiogenin (Ang) expression in the ventral midbrain (VMB) of MC1Re/e mice, validated through RNA-seq, RT-qPCR, and Western blot. This CNS-specific downregulation suggests localized regulatory mechanisms linked to neuroprotection and Parkinson's disease (PD) pathogenesis. Ang's role in neurodegeneration, angiogenesis, and oxidative stress responses highlights its therapeutic potential in PD. These findings provide critical insights into Ang's CNS-specific function and underscore the importance of further research into its mechanistic role in PD.

Costimulatory molecule-related lncRNA model as a potential prognostic biomarker in non-small cell lung cancer

OBJECTIVE

Costimulatory molecules have been demonstrated to exert essential roles in multiple cancers. However, their role in lung cancer remains elusive. Here, we sought to identify costimulatory molecule-related lncRNAs in non-small cell lung cancer (NSCLC) and establish a prognostic signature to predict the prognosis of patients with NSCLC.

METHODS

A total of 535 lung adenocarcinoma (LUAD) and 502 lung squamous cell carcinoma (LUSC) patients from the cancer genome atlas (TCGA) database were recruited. A novel costimulatory molecule-based lncRNA prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm to predict the overall survival. The Homo_sapiens.GRCh38 data set was set as a reference file for probe annotation.

RESULTS

A total of 593 costimulatory molecule-related lncRNAs were extracted. After analysis, six costimulatory molecule-related lncRNAs (AC084859.1, AC079949.2, HSPC324, LINC01150, LINC01150, and AC090617.5) were screened. A prognostic model based on the six lncRNAs was established using systematic bioinformatics analyses. The prognostic model had a prognostic value in NSCLC patients. Furthermore, a prognostic nomogram was established based on clinical parameters and a risk-score model. Patients with different risk scores had considerably different tumor-infiltrating immune cells, somatic mutational loading, clinical outcomes, signaling pathways, and immunotherapy efficacy. In addition, LINC01137 was associated with unfavorable disease outcomes and fueled tumor progression in NSCLC.

CONCLUSION

Taken together, our study demonstrated that a costimulatory molecule-related lncRNA model could be a potential prognostic biomarker in NSCLC. Moreover, LINC01137 could facilitate the proliferation and invasion of lung cancer.

LINC00941 Promotes Cell Malignant Behavior and Is One of Five Costimulatory Molecule-Related lncRNAs That Predict Prognosis in Renal Clear Cell Carcinoma

Background and Objectives: A significant role was played by costimulatory molecules in renal cancer. However, the lncRNAs regulating costimulatory molecules have not been fully investigated. Materials and Methods: Data from the next-sequence file and clinical data were downloaded from the Cancer Genome Atlas (TCGA) database. All analyses were conducted using the R and GraphPad Prism software. Results: A total of 1736 costimulatory molecule-related lncRNAs were determined under the threshold of |Cor| > 0.5 and p-value < 0.001. Furthermore, a prognosis prediction signature consisting of five lncRNAs: LINC00941, AC016773.1, AL162171.1, HOTAIRM1, and AL109741.1 was established with great prediction ability. By combining risk score and clinical parameters, a nomogram plot was constructed for better clinical practice. A biological enrichment analysis indicated that E2F targets, coagulation, IL6/JAK/STAT3 signaling, G2/M checkpoint, and allograft rejection pathways were activated in high-risk patients. Furthermore, a higher infiltration level of resting CD4+ T cell, M2 macrophage, and resting mast cells, while a lower CD8+ T cell infiltration was observed in high-risk patients. It is worthy of note that, low-risk patients might respond better to PD-1 checkpoint therapy. A correlation analysis of LINC00941 revealed that it was positively correlated with Th2 cells, Th1 cells, macrophages, and Treg cells, but negatively correlated with Th17 cells. A pathway enrichment analysis indicated that the pathways of the inflammatory response, G2M checkpoint, and IL6/JAK/STAT3 signaling were significantly activated in patients with high LINC00941 expression. In vitro experiments indicated that LINC00941 can enhance the malignant biological behaviors of renal cancer cells. Conclusions: Our study established a costimulatory molecule-related lncRNAs-based prognosis model with a great prediction prognosis. In addition, LINC00941 could enhance the malignant biological behaviors of renal cancer cells.

Prognostic costimulatory molecule-related signature risk model correlates with immunotherapy response in colon cancer

Costimulatory molecules can promote the activation and proliferation of T cells and play an essential role in immunotherapy. However, their role in the prognosis of colon adenocarcinoma remains elusive. In this study, the expression data of costimulatory molecules and clinicopathological information of 429 patients with colon adenocarcinoma were obtained from The Cancer Genome Atlas database. The patients were divided into training and verification cohorts. Correlation, Cox regression, and Lasso regression analyses were performed to identify costimulatory molecules related to prognosis. After mentioning the construction of the risk mode, a nomogram integrating the clinical characteristics and risk scores of patients was constructed to predict prognosis. Eventually, three prognostic costimulatory molecules were identified and used for constructing a risk model. High expression of these three molecules indicated a poor prognosis. The predictive accuracy of the risk model was verified in the GSE17536 dataset. Subsequently, multivariate regression analysis showed that the signature based on the three costimulatory molecules was an independent risk factor in the training cohort (HR = 2.12; 95% CI = 1.26, 3.56). Based on the risk model and clinicopathological data, the AUC values for predicting the 1-, 3-, and 5-year survival probability of patients with colon adenocarcinoma were 0.77, 0.77, and 0.71, respectively. To the best of our knowledge, this study is the first to report a risk signature constructed based on the costimulatory molecules TNFRSF10c, TNFRSF13c, and TNFRSF11a. This risk signature can serve as a prognostic biomarker for colon adenocarcinoma and is related to the immunotherapeutic response of patients.

The role of costimulatory molecules in glioma biology and immune microenvironment

Background: Extensive research showed costimulatory molecules regulate tumor progression. Nevertheless, a small amount of literature has concentrated on the potential prognostic and therapeutic effects of costimulatory molecules in patients with glioma. Methods: The data were downloaded from The Cancer Genome Atlas (TCGA) database, Chinese Glioma Genome Atlas (CGGA) database, and Gene Expression Omnibus (GEO) database for bioinformatics analysis. R software was applied for statistical analysis. Using the FigureYa and Xiantao online tools (https://www.xiantao.love/) for mapping. Results: The Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were utilized to identify the signature consisting of five costimulatory molecules. Multivariate regression analysis revealed that the prognosis of glioma could be independently predicted by the riskscore. Furthermore, we explored clinical and genomic feature differences between the two groups. The level of tumor mutational burden (TMB) was higher in the high-risk group, while more mutation of IDH1 was observed in the low-risk group. Results of Tumor Immune Dysfunction and Exclusion (TIDE) analysis showed that high-risk patients were more prone to be responded to immunotherapy. In addition, subclass mapping analysis was performed to validate our findings and the results revealed that a significantly higher percentage of immunotherapy response rate was observed in the high-risk group. Conclusion: A novel signature with a good independent predictive capacity of prognosis was successfully identified. And our findings reveal that patients with high-risk scores were more likely to be responded to immunotherapy.

Targeting fibroblast growth factor (FGF)-inducible 14 (Fn14) for tumor therapy

Fibroblast growth factor-inducible 14 (Fn14) is a member of the tumor necrosis factor (TNF) receptor superfamily (TNFRSF) and is activated by its ligand TNF-like weak inducer of apoptosis (TWEAK). The latter occurs as a homotrimeric molecule in a soluble and a membrane-bound form. Soluble TWEAK (sTWEAK) activates the weakly inflammatory alternative NF-κB pathway and sensitizes for TNF-induced cell death while membrane TWEAK (memTWEAK) triggers additionally robust activation of the classical NF-κB pathway and various MAP kinase cascades. Fn14 expression is limited in adult organisms but becomes strongly induced in non-hematopoietic cells by a variety of growth factors, cytokines and physical stressors (e.g., hypoxia, irradiation). Since all these Fn14-inducing factors are frequently also present in the tumor microenvironment, Fn14 is regularly found to be expressed by non-hematopoietic cells of the tumor microenvironment and most solid tumor cells. In general, there are three possibilities how the tumor-Fn14 linkage could be taken into consideration for tumor therapy. First, by exploitation of the cancer associated expression of Fn14 to direct cytotoxic activities (antibody-dependent cell-mediated cytotoxicity (ADCC), cytotoxic payloads, CAR T-cells) to the tumor, second by blockade of potential protumoral activities of the TWEAK/Fn14 system, and third, by stimulation of Fn14 which not only triggers proinflammtory activities but also sensitizes cells for apoptotic and necroptotic cell death. Based on a brief description of the biology of the TWEAK/Fn14 system and Fn14 signaling, we discuss the features of the most relevant Fn14-targeting biologicals and review the preclinical data obtained with these reagents. In particular, we address problems and limitations which became evident in the preclinical studies with Fn14-targeting biologicals and debate possibilities how they could be overcome.

Tumor HPV Status, Level of Regulatory T Cells and Macrophage Infiltration Predict up to 20-Year Non-Disease-Specific Survival in Oropharynx Squamous Cell Carcinoma Patients

Oropharynx squamous cell carcinoma (OPSCC) is of special interest because human papilloma virus (HPV) and/or smoking cause this disease. Influxes of inflammatory cells into such tumors are known to vary with prognoses.

AIMS

To study whether the density of tumor-infiltrating T lymphocytes and tumor-infiltrating macrophages predicted general 20-year overall survival (OS), as well as OS with only disease-specific survival (DSS) patients included.

METHODS

Biopsies from patients treated for OPSCC (n = 180) were stained by immunohistochemistry and the tumor cell macrophage (CD68), pan T lymphocytes (CD3), and regulatory T lymphocytes (Foxp3) densities were determined. The HE-determined percentage of matured tumor cells and the rate of invasion were calculated, and stromal desmoplasia were performed. Tumor HPV presence was studied by PCR. Twenty-year OS and five-year DSS patients were determined.

RESULTS

Tumor HPV status strongly predicted survival. High tumor infiltration of CD3, Foxp3 and CD68-positive cells predicted better twenty-year OS, with and without HPV stratification. Foxp3 and CD68 levels predicted OS, and 20-year among DSS patients, primarily among HPV(+) patients. Tumor HE-derived variables did not predict such survival.

CONCLUSIONS

Tumor HPV status, level of Foxp3 tumor-infiltrating lymphocytes and CD68 tumor-infiltrating macrophages predicted up to 20-year OS of both all patients and disease-specific survived patients.

Integrative Analysis of Bioinformatics and Machine Learning Algorithms Identifies a Novel Diagnostic Model Based on Costimulatory Molecule for Predicting Immune Microenvironment Status in Lung Adenocarcinoma

Costimulatory molecules are an indispensable signal for activating immune cells. However, the features of many costimulatory molecule genes (CMGs) in lung adenocarcinoma (LUAD) are poorly understood. This study systematically explored expression patterns of CMGs in the tumor immune microenvironment (TIME) status of patients with LUAD. Their expression profiles were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Two robust TIME subtypes ("hot" and "cold") were classified by K-means clustering and estimation of stromal and immune cells in malignant tumor tissues using expression data. The "hot" subtype presented higher infiltration in activated immune cells and enrichments in the immune cell receptor signaling pathway and adaptive immune response. Three CMGs (CD80, LTB, and TNFSF8) were screened as final diagnostic markers by means of Least Absolute Shrinkage Selection Operator and Support Vector Machine-Recursive Feature Elimination algorithms. Accordingly, the diagnostic nomogram for predicting individualized TIME status showed satisfactory diagnostic accuracy in The Cancer Genome Atlas training cohort as well as GSE31210 and GSE180347 validation cohorts. Immunohistochemistry staining of 16 specimens revealed an apparently positive correlation between the expression of CMG biomarkers and pathologic response to immunotherapy. Thus, this diagnostic nomogram provided individualized predictions in TIME status of LUAD patients with good predictive accuracy, which could serve as a potential tool for identifying ideal candidates for immunotherapy.

Development of a Costimulatory Molecule Signature to Predict Prognosis, Immune Landscape, and Response to Immune Therapy for Hepatocellular Carcinoma

This work was aimed at investigating the predictive value on prognosis, response to immunotherapy, and association with the immune landscape of costimulatory molecules in HCC patients. We acquired the clinicopathological information and gene expression of HCC patients from public available database (TCGA and GEO). The prognostic model in TCGA database was established with LASSO regression and Cox regression analysis. Through the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis, the enrichment analysis was implemented for analyzing the biological function and associated pathways. Immune microenvironment, immune escape, immune therapy, and tumor mutation were analyzed between both risk groups. TNFRSF4, the critical costimulatory molecule, was chosen for the in-depth investigation in vitro experiments. A novel risk signature based on 8 costimulatory molecules associated with prognosis was constructed from TCGA and proved in the database of GEO. The ROC and Kaplan-Meier curves confirmed that this risk model has good predictive accuracy. Our functional analysis demonstrated costimulatory molecular genes might associate with immune-related functions and pathways. Statistical differences were not shown between both groups, in the aspect of immune landscape, response to immune therapy, and tumor mutation. Knocking down TNFRSF4 expression significantly reduced the proliferation ability and increased the apoptosis ability. On the basis of the costimulatory molecule expression in HCC, a novel risk model was constructed and had an excellent value to predict prognosis, immune microenvironment, and response to immune therapy. TNFRSF4 was identified as an underlying oncogene in HCC and deserves further exploration.

Identification of a novel prognostic signature for HCC and analysis of costimulatory molecule-related lncRNA AC099850.3

Costimulatory molecules are involved in initiation of anti-tumor immune responses while long non‐coding RNAs (lncRNAs) regulate the development of various cancers. However, the roles of lncRNA in hepatocellular carcinoma (HCC) have not been fully established. In this study, we aimed at identifying lncRNAs-related costimulatory molecules in HCC and to construct a prognostic signature for predicting the clinical outcomes for HCC patients. Data were downloaded from The Cancer Genome Atlas database for bioinformatics analyses. Costimulatory molecules were obtained from published literature. The R software, SPSS, and GraphPad Prism were used for statistical analyses. A risk model that is based on five costimulatory molecule-related lncRNAs was constructed using lasso and Cox regression analyses. Multivariate regression analysis revealed that the risk score could predict the prognostic outcomes for HCC. Samples in high- and low-risk groups exhibited significant differences in gene set enrichment and immune infiltration levels. Through colony formation and CCK8 assays, we found that AC099850.3 was strongly associated with HCC cell proliferation. We identified and validated a novel costimulatory molecule-related survival model. In addition, AC099850.3 was found to be closely associated with clinical stages and proliferation of HCC cells, making it a potential target for HCC treatment.

Identification and validation a costimulatory molecule gene signature to predict the prognosis and immunotherapy response for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Costimulatory molecules have been proven to be the foundation of immunotherapy. However, the potential roles of costimulatory molecule genes (CMGs) in HCC remain unclear. Our study is aimed to develop a costimulatory molecule-related gene signature that could evaluate the prognosis of HCC patients.

METHODS

Based on The Cancer Gene Atlas (TCGA) database, univariate Cox regression analysis was applied in CMGs to identify prognosis-related CMGs. Consensus clustering analysis was performed to stratify HCC patients into different subtypes and compared them in OS. Subsequently, the LASSO Cox regression analysis was performed to construct the CMGs-related prognostic signature and Kaplan-Meier survival curves as well as ROC curve were used to validate the predictive capability. Then we explored the correlations of the risk signature with tumor-infiltrating immune cells, tumor mutation burden (TMB) and response to immunotherapy. The expression levels of prognosis-related CMGs were validated based on qRT-PCR and Human Protein Atlas (HPA) databases.

RESULTS

All HCC patients were classified into two clusters based on 11 CMGs with prognosis values and cluster 2 correlated with a poorer prognosis. Next, a prognostic signature of six CMGs was constructed, which was an independent risk factor for HCC patients. Patients with low-risk score were associated with better prognosis. The correlation analysis showed that the risk signature could predict the infiltration of immune cells and immune status of the immune microenvironment in HCC. The qRT-PCR and immunohistochemical results indicated six CMGs with differential expression in HCC tissues and normal tissues.

CONCLUSION

In conclusion, our CMGs-related risk signature could be used as a prediction tool in survival assessment and immunotherapy for HCC patients.

A novel diagnostic model for predicting immune microenvironment subclass based on costimulatory molecules in lung squamous carcinoma

There is still no ideal predictive biomarker for immunotherapy response among patients with non-small cell lung cancer. Costimulatory molecules play a role in anti-tumor immune response. Hence, they can be a potential biomarker for immunotherapy response. The current study comprehensively investigated the expression of costimulatory molecules in lung squamous carcinoma (LUSC) and identified diagnostic biomarkers for immunotherapy response. The costimulatory molecule gene expression profiles of 627 patients were obtained from the The Cancer Genome Atlas, GSE73403, and GSE37745 datasets. Patients were divided into different clusters using the k-means clustering method and were further classified into two discrepant tumor microenvironment (TIME) subclasses (hot and cold tumors) according to the immune score of the ESTIMATE algorithm. A high proportion of activated immune cells, including activated memory CD4 T cells, CD8 T cells, and M1 macrophages. Five CMGs (FAS, TNFRSF14, TNFRSF17, TNFRSF1B, and TNFSF13B) were considered as diagnostic markers using the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Based on the five CMGs, a diagnostic nomogram for predicting individual tumor immune microenvironment subclasses in the TCGA dataset was developed, and its predictive performance was validated using GSE73403 and GSE37745 datasets. The predictive accuracy of the diagnostic nomogram was satisfactory in all three datasets. Therefore, it can be used to identify patients who may benefit more from immunotherapy.