High Expression of DLGAP5 Indicates Poor Prognosis and Immunotherapy in Lung Adenocarcinoma and Promotes Proliferation through Regulation of the Cell Cycle

Transcriptional regulation of DLGAP5 by AR suppresses p53 signaling and inhibits CD8+T cell infiltration in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a challenging subtype with unclear biological mechanisms. Recently, the transcription factor androgen receptor (AR) and its regulation of the DLGAP5 gene have gained attention in TNBC pathogenesis. In this study, we found a positive correlation between high AR expression and TNBC cell proliferation and growth. Furthermore, we confirmed DLGAP5 as a critical downstream regulator of AR with high expression in TNBC tissues. Knockdown of DLGAP5 significantly inhibited TNBC cell proliferation, migration, and invasion. AR was observed to directly bind to the DLGAP5 promoter, enhancing its transcriptional activity and suppressing the activation of the p53 signaling pathway. In vivo experiments further validated that downregulation of AR or DLGAP5 inhibited tumor growth and enhanced CD8+T cell infiltration. This study highlights the crucial roles of AR and DLGAP5 in TNBC growth and immune cell infiltration. Taken together, AR inhibits the p53 signaling pathway by promoting DLGAP5 expression, thereby impacting CD8+T cell infiltration in TNBC.

DLGAP5 promotes lung adenocarcinoma growth via upregulating PLK1 and serves as a therapeutic target

BACKGROUND

Human discs large-associated protein 5 (DLGAP5) is reported to play a pivotal role in regulating the cell cycle and implicate in tumorigenesis and progression of various cancers. Our current research endeavored to explore the prognostic value, immune implication, biological function and targeting strategy of DLGAP5 in LUAD through approaches including bioinformatics, network pharmacology analysis and experimental study.

METHODS

Multiple databases, including TCGA, GEO, CPTAC and Human Protein Atlas, were utilized to explore the expression and clinical significance of DLGAP5 in LUAD. The genetic alterations of DLGAP5 were assessed through cBioPortal and COSMIC databases. The relationship between DLGAP5 expression and genetic abnormalities of driver genes in LUAD was analyzed through TIMER2.0 database. CancerSEA database was utilized to explore the function of DLGAP5 in 14 different states in LUAD at single-cell resolution. GDSC database was utilized to analyze the impact of DLGAP5 on IC50 of frequently-used anti-LUAD drugs. CIBERSORT method and TIMER2.0 database was utilized to explore the relationship between DLGAP5 and tumor immune infiltration. Network pharmacology was applied to screen potential DLGAP5 inhibitor. In vitro and in vivo experiments were utilized to evaluate biological function and downstream targets of DLGAP5, and the effect of screened DLGAP5 inhibitor on LUAD growth.

RESULTS

High DLGAP5 expression was commonly observed in LUAD and associated with mutation of major driver genes, poor prognosis, high IC50 values of frequently-used anti-LUAD drugs, increasing immune infiltration and elevated immune checkpoint blockade-related genes in LUAD. PLK1 was revealed as a potential DLGAP5 downstream target in LUAD. DLGAP5 overexpression or knockdown significantly promoted or inhibited LUAD cell proliferation and PLK1 expression. PLK1 overexpression well rescued DLGAP5 knockdown-induced cell proliferation inhibition, or vice versa. Furthermore, by virtual screening of an investigational drug library from the DrugBank database, AT9283 was screened and identified as a novel DLGAP5 inhibitor. AT9283 effectively suppressed growth of LUAD cells both in vitro and in vivo. DLGAP5 overexpression significantly reversed AT9283-induced proliferation inhibition. Moreover, AT9283 significantly suppressed DLGAP5 and PLK1 expression, while DLGAP5 overexpression significantly reversed AT9283-induced PLK1 suppression.

CONCLUSION

Our research has demonstrated that DLGAP5 is upregulated in LUAD and exhibits a strong correlation with unfavorable prognosis. Furthermore, DLGAP5 assumes a significant function in the regulation of tumor immunity and treatment outcome of immune checkpoint inhibitors. Of note, we found that DLGAP5 promotes cell proliferation of LUAD via upregulating PLK1. Targeting DLGAP5 by AT9283, our newly identified DLGAP5 inhibitor, suppresses LUAD growth. DLGAP5 may become a promising prognostic biomarker and therapeutic target for patients with LUAD.

Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis

Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application. Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103. Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells. Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.

DLGAP5 Regulates the Proliferation, Migration, Invasion, and Cell Cycle of Breast Cancer Cells via the JAK2/STAT3 Signaling Axis

The aim of this study was to discover new biomarkers to detect breast cancer (BC), which is an aggressive cancer with a high mortality rate. In this study, bioinformatic analyses (differential analysis, weighted gene co-expression network analysis, and machine learning) were performed to identify potential candidate genes for BC to study their molecular mechanisms. Furthermore, Quantitative Real-time PCR and immunohistochemistry assays were used to examine the protein and mRNA expression levels of a particular candidate gene (DLGAP5). And the effects of DLGAP5 on cell proliferation, migration, invasion, and cell cycle were further assessed using the Cell Counting Kit-8 assay, colony formation, Transwell, wound healing, and flow cytometry assays. Moreover, the changes in the JAK2/STAT3 signaling-pathway-related proteins were detected by Western Blot. A total of 44 overlapping genes were obtained by differential analysis and weighted gene co-expression network analysis, of which 25 genes were found in the most tightly connected cluster. Finally, NEK2, CKS2, UHRF1, DLGAP5, and FAM83D were considered as potential biomarkers of BC. Moreover, DLGAP5 was highly expressed in BC. The down-regulation of DLGAP5 may inhibit the proliferation, migration, invasion, and cell cycle of BC cells, and the opposite was true for DLGAP5 overexpression. Correspondingly, silencing or overexpression of the DLGAP5 gene inhibited or activated the JAK2/STAT3 signaling pathway, respectively. DLGAP5, as a potential biomarker of BC, may impact the cell proliferation, migration, invasion, cell cycle, and BC development by modulating the JAK2/STAT3 signaling pathway.

Prognostic value of genes related to cancer-associated fibroblasts in lung adenocarcinoma

BACKGROUND

Although it has been established that cancer-associated fibroblasts (CAFs) facilitate tumor development, the relationship between CAFs and the prognosis of patients with lung adenocarcinoma (LUAD) has not been extensively explored.

OBJECTIVE

This study was formulated to investigate the prognostic value of CAF-related genes in LUAD.

METHODS

Differential analysis was carried out with TCGA-LUAD dataset as the training set. By overlapping differentially expressed genes (DEGs) with genes associated with CAF, CAF-related DEGs specific to LUAD were obtained. A prognostic risk model was constructed by Lasso and Cox regression analysis, and samples were grouped according to median risk score. The efficacy of the model was accessed through survival curve and receiver operating characteristic curve (ROC) analyses, with the validation set for verification. Risk score combined with clinical factors was utilized for Cox analysis to verify the independence of the model, and a nomogram was drawn. GSEA was performed on different risk groups. Immunologic infiltration and tumor mutational burden were assessed in different risk groups.

RESULTS

Eleven feature genes including DLGAP5, KCNE2, UPK2, NPAS2, ARHGAP11A, ANGPTL4, ANLN, DKK1, SMUG1, C16orf74, and ACAD8 were identified, based on which a prognostic model was constructed. Risk score could predict the prognosis of LUAD patients and could be an independent prognostic factor for LUAD patients. GSEA outcomes displayed significant enrichment of genes in the high-risk group in the P53 SIGNALING PATHWAY. In comparison to the low-risk group, the high-risk group exhibited a decreased degree of immune infiltration and an elevated level of tumor mutational burden.

CONCLUSION

An 11-gene model was constructed based on CAF-related genes to predict LUAD prognosis. This model represented an independent prognostic factor for LUAD.