A Focal Adhesion-Related Gene Signature Predicts Prognosis in Glioma and Correlates With Radiation Response and Immune Microenvironment

Establishing and Validating an Innovative Focal Adhesion-Linked Gene Signature for Enhanced Prognostic Assessment in Endometrial Cancer

Studies have highlighted the significant role of focal adhesion signaling in cancer. Nevertheless, its specific involvement in the pathogenesis of endometrial cancer and its clinical significance remains uncertain. We analyzed TCGA-UCEC and GSE119041 datasets with corresponding clinical data to investigate focal adhesion-related gene expression and their clinical significance. A signature, "FA-riskScore," was developed using LASSO regression in the TCGA cohort and validated in the GSE dataset. The FA-riskScore was compared with four existing models in terms of their prediction performance. We employed univariate and multivariate Cox regression analyses towards FA-riskScore to assess its independent prognostic value. A prognostic evaluation nomogram based on our model and clinical indexes was established subsequently. Biological and immune differences between high- and low-risk groups were explored through functional enrichment, PPI network analysis, mutation mining, TME evaluation, and single-cell analysis. Sensitivity tests on commonly targeted drugs were performed on both groups, and Connectivity MAP identified potentially effective molecules for high-risk patients. qRT-PCR validated the expressions of FA-riskScore genes. FA-riskScore, based on FN1, RELN, PARVG, and PTEN, indicated a poorer prognosis for high-risk patients. Compared with published models, FA-riskScore achieved better and more stable performance. High-risk groups exhibited a more challenging TME and suppressive immune status. qRT-PCR showed differential expression in FN1, RELN, and PTEN. Connectivity MAP analysis suggested that BU-239, potassium-canrenoate, and tubocurarine are effective for high-risk patients. This study introduces a novel prognostic model for endometrial cancer and offers insights into focal adhesion's role in cancer pathogenesis.

Correlation of tumor-associated macrophage infiltration in glioblastoma with magnetic resonance imaging characteristics: a retrospective cross-sectional study

Background

Glioblastoma (Gb) is the most common primary malignant tumor of brain with poor prognosis. Immune cells are the main factors affecting the prognosis of Gb, tumor-associated macrophages (TAMs) are the predominant infiltrating immune cell population in the immune microenvironment of Gb. Analyzing the relationship between magnetic resonance imaging (MRI) features and TAMs of Gb, and using imaging features to characterize the infiltration level of TAMs in tumor tissue may provide indicators for clinical decision-making and prognosis evaluation of Gb.

Methods

Data from 140 in patients with isocitrate dehydrogenase (IDH) wild-type Gb diagnosed via histopathology and molecular diagnosis in the Second Hospital of Lanzhou University from January 2018 to April 2022 were collected in this retrospective, cross-sectional study. MRI images were reviewed for lesion location, cyst, necrosis, hemorrhage, contrast-enhanced T1-weighted MRI signal intensity, average apparent diffusion coefficient (ADCmean), and minimum apparent diffusion coefficient (ADCmin). Immunohistochemical staining with anti-CD163 and anti-CD68 antibodies was employed for macrophage detection. The positive cell percentage was estimated in 9 microscopic fields at 400× magnification per whole-slide image with ImageJ software (National Institutes of Health). Additionally, the relationship between MRI features, molecular, states and the positive CD68 and CD163 expression was analyzed.

Results

Our study discovered that the mean or median values of CD68+ and CD163+ TAMs were 7.39% and 14.98%, respectively. There was an obvious correlation between CD163+ TAMs and CD68+ TAMs (r=0.497; P=0.000). CD68+ and CD163+ macrophage infiltration correlated with age at diagnosis in patients with Gb (CD68+: r=0.230, P=0.006; CD163+: r=0.172, P=0.042). The levels of Gb TAM infiltration in different tumor locations varied, with the temporal lobe having the highest CD163+ macrophage and CD68+ macrophage infiltration (18.58% and 9.46%, respectively). CD163+ macrophage infiltration was positively correlated with ADCmean (r=0.208; P=0.014). The infiltration of CD68+ macrophages differed significantly between groups with varying degrees of tumor enhancement (H =4.228; P=0.017). There was a significant difference in CD68+ TAMs and CD163+ TAMs between the wild-type and mutant-type telomerase reverse transcriptase (TERT) types (P=0.004 and P=0.031, respectively).

Conclusions

Age, location of the tumor, degree of tumor enhancement, ADC value, and TERT mutation status were associated with macrophage infiltration. These findings may serve as an effective tool for characterizing the tumor microenvironment in patients with Gb.

A predictive model of immune infiltration and prognosis of head and neck squamous cell carcinoma based on cell adhesion-related genes: including molecular biological validation

Background

Focal adhesion serves as a bridge between tumour cells and the extracellular matrix (ECM) and has multiple roles in tumour invasion, migration, and therapeutic resistance. However, studies on focal adhesion-related genes (FARGs) in head and neck squamous cell carcinoma (HNSCC) are limited.

Methods

Data on HNSCC samples were obtained from The Cancer Genome Atlas and GSE41613 datasets, and 199 FARGs were obtained from the Molecular Signatures database. The integrated datasets' dimensions were reduced by the use of cluster analysis, which was also used to classify patients with HNSCC into subclusters. A FARG signature model was developed and utilized to calculate each patient's risk score using least extreme shrinkage and selection operator regression analysis. The risk score was done to quantify the subgroups of all patients. We evaluated the model's value for prognostic prediction, immune infiltration status, and therapeutic response in HNSCC. Preliminary molecular and biological experiments were performed to verify these results.

Results

Two different HNSCC molecular subtypes were identified according to FARGs, and patients with C2 had a shorter overall survival (OS) than those with C1. We constructed an FARG signature comprising nine genes. We constructed a FARG signature consisting of nine genes. Patients with higher risk scores calculated from the FARG signature had a lower OS, and the FARG signature was considered an independent prognostic factor for HNSCC in univariate and multivariate analyses. FARGs are associated with immune cell invasion, gene mutation status, and chemosensitivity. Finally, we observed an abnormal overexpression of MAPK9 in HNSCC tissues, and MAPK9 knockdown greatly impeded the proliferation, migration, and invasion of HNSCC cells.

Conclusion

The FARG signature can provide reliable prognostic prediction for patients with HNSCC. Apart from that, the genes in this model were related to immune invasion, gene mutation status, and chemosensitivity, which may provide new ideas for targeted therapies for HNSCC.

DUSP10 upregulation is a poor prognosticator and promotes cell proliferation and migration in glioma

Dual-specificity phosphatase 10 (DUSP10) correlates with inflammation, cytokine secretion, cell proliferation, survival, and apoptosis. However, its role in glioma is unclear. Herein, we sought to examine the expression and the underlying carcinogenic mechanisms of DUSP10 action in glioma. DUSP10 expression in glioma was significantly higher than that in normal brain tissues. High DUSP10 expression indicated adverse clinical outcomes in glioma patients. Increased DUSP10 expression correlated significantly with clinical features in glioma. Univariate Cox analysis showed that high DUSP10 expression was a potential independent marker of poor prognosis in glioma. Furthermore, DUSP10 expression in glioma correlated negatively with its DNA methylation levels. DNA methylation level of DUSP10 also correlated negatively with poor prognosis in glioma. More importantly, DUSP10 expression correlated positively with the infiltration of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells in glioma. Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis confirmed that DUSP10 participated in signaling pathways involved in focal adhesion, TNF cascade, Th17 cell differentiation, and NF-kappa B cascade. Finally, we uncovered that DUSP10 was dramatically upregulated in glioblastoma (GBM) cells and that the knockdown of DUSP10 inhibited glioma cell proliferation and migration. Our findings suggested that DUSP10 may serve as a potential prognostic biomarker in glioma.

The basement membrane-related gene signature is associated with immunity and predicts survival accurately in hepatocellular carcinoma

AIMS

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Expression defects and turnover of basement membrane (BM) proteins are key pathogenic factors in cancer. It is still uncertain how the expression of BM-related genes (BMGs) in HCC relates to prognosis.

METHODS

All of the HCC cohort's RNA-seq and clinical information came from TCGA datasets. The least absolute shrinkage and selection operator (LASSO) regression algorithm was utilized to filter down the candidate genes and construct the prognostic model. Univariate and multivariate Cox analyses were run to examine if the risk score may serve as a standalone prognostic indicator. The single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze examine immune cell infiltration and pathway activity.

RESULTS

Five genes and their risk coefficients were eventually identified and patients with HCC were classified as either high or low risk based on the median of risk scores. Multivariate Cox regression analysis found a significant correlation between risk score and OS (p < 0.001). Subgroup analysis showed that BMGs signature had good prediction ability for HCC patients in age, gender, T stage, and AJCC stage (all p < 0.05). According to the ssGSEA, the high-risk subgroup showed higher levels of immune cell infiltration and immune-related pathways were more engaged in the high-risk group.

CONCLUSIONS

Our research systematically built a prognostic model using risk score based on BMGs signature in HCC patients. The immune feature analysis of the BMGs signature indicated a potential regulation between tumor immunity and BM in HCC.

Construction and validation of an angiogenesis-related gene expression signature associated with clinical outcome and tumor immune microenvironment in glioma

Background: Glioma is the most prevalent malignant intracranial tumor. Many studies have shown that angiogenesis plays a crucial role in glioma tumorigenesis, metastasis, and prognosis. In this study, we conducted a comprehensive analysis of angiogenesis-related genes (ARGs) in glioma.

Methods: RNA-sequencing data of glioma patients were obtained from TCGA and CGGA databases. Via consensus clustering analysis, ARGs in the sequencing data were distinctly classified into two subgroups. We performed univariate Cox regression analysis to determine prognostic differentially expressed ARGs and least absolute shrinkage and selection operator Cox regression to construct a 14-ARG risk signature. The CIBERSORT algorithm was used to explore immune cell infiltration, and the ESTIMATE algorithm was applied to calculate immune and stromal scores.

Results: We found that the 14-ARG signature reflected the infiltration characteristics of different immune cells in the tumor immune microenvironment. Additionally, total tumor mutational burden increased significantly in the high-risk group. We combined the 14-ARG signature with patient clinicopathological data to construct a nomogram for predicting 1-, 3-, and 5-year overall survival with good accuracy. The predictive value of the prognostic model was verified in the CGGA cohort. SPP1 was a potential biomarker of glioma risk and was involved in the proliferation, invasion, and angiogenesis of glioma cells.

Conclusion: In conclusion, we established and validated a novel ARG risk signature that independently predicted the clinical outcomes of glioma patients and was associated with the tumor immune microenvironment.

PD-L2 Serves as a Potential Prognostic Biomarker That Correlates With Immune Infiltration and May Predict Therapeutic Sensitivity in Lower-Grade Gliomas

Although patients with lower-grade gliomas (LGGs; grades II and III) have a relatively favorable prognosis, patients frequently relapse and tend to progress to higher-grade gliomas, leading to treatment resistance, poor survival, and ultimately treatment failure. However, until now, thorough research has not yet been reported on the relationship between PD-L2 and immune infiltration and therapeutic sensitivity to immunotherapy and TMZ-based chemotherapy of LGGs. In this study, we found that the expression of PD-L2 is upregulated in glioma, with high PD-L2 expression predicting a worse prognosis. Univariate and multivariate Cox regression analysis both indicated that PD-L2 represented an independent prognostic factor with high accuracy in survival prediction for LGGs. A nomogram comprising of age, grade, IDH mutation, and PD-L2 was established for predicting OS. Additionally, PD-L2 was found to be remarkably correlated with immune infiltration and some anti-tumor immune functions. The degree of PD-L2 expression was also found to be strongly related to the prediction of therapeutic sensitivity to immunotherapy and TMZ-based chemotherapy. Furthermore, immunohistochemistry demonstrated that PD-L2 and the macrophage biomarker CD68 were both increased in glioma, with PD-L2 expression having a strong positive connection with CD68 expression. Taken together, PD-L2 is a prognostic biomarker for LGGs patients that may provide novel insights into glioma individualized therapeutic strategies and guide effective immunotherapy and chemotherapy.

Prognostic Features of the Tumor Immune Microenvironment in Glioma and Their Clinical Applications: Analysis of Multiple Cohorts

Glioma is the most common malignant tumor of the central nervous system. Tumor purity is a source of important prognostic factor for glioma patients, showing the key roles of the microenvironment in glioma prognosis. In this study, we systematically screened functional characterization related to the tumor immune microenvironment and constructed a risk model named Glioma MicroEnvironment Functional Signature (GMEFS) based on eight cohorts. The prognostic value of the GMEFS model was also verified in another two glioma cohorts, glioblastoma (GBM) and low-grade glioma (LGG) cohorts, from The Cancer Genome Atlas (TCGA). Nomograms were established in the training and testing cohorts to validate the clinical use of this model. Furthermore, the relationships between the risk score, intrinsic molecular subtypes, tumor purity, and tumor-infiltrating immune cell abundance were also evaluated. Meanwhile, the performance of the GMEFS model in glioma formation and glioma recurrence was systematically analyzed based on 16 glioma cohorts from the Gene Expression Omnibus (GEO) database. Based on multiple-cohort integrated analysis, risk subpathway signatures were identified, and a drug–subpathway association network was further constructed to explore candidate therapy target regions. Three subpathways derived from Focal adhesion (path: 04510) were identified and contained known targets including platelet derived growth factor receptor alpha (PDGFRA), epidermal growth factor receptor (EGFR), and erb-b2 receptor tyrosine kinase 2 (ERBB2). In conclusion, the novel functional signatures identified in this study could serve as a robust prognostic biomarker, and this study provided a framework to identify candidate therapeutic target regions, which further guide glioma patients’ clinical decision.