Identification of an Aging-Related Gene Signature in Predicting Prognosis and Indicating Tumor Immune Microenvironment in Breast Cancer

The many faces of DGAT1

Acyl-CoA:diacylglycerol acyltransferase 1 (DGAT1) is a multifaced enzyme with a wide spectrum of substrates, from lipids through waxes to retinoids, which makes it an interesting therapeutic target. DGAT1 inhibitors are currently at various stages of preclinical and clinical trials, mostly related to metabolic diseases. Interestingly, in recent years, a growing amount of research has shown the influence of DGAT1 on immune cell metabolism and functions, highlighting its important role during infections and tumorigenesis. In this review, we aim to elucidate the potential immunomodulatory effect of DGAT1 in physiological and pathological conditions.

Cell Populations in Human Breast Cancers are Molecularly and Biologically Distinct with Age

Aging is associated with increased breast cancer risk and outcomes are worse for the oldest and youngest patients, regardless of subtype. It is not known how cells in the breast tumor microenvironment are impacted by age and how they might contribute to age-related disease pathology. Here, we discover age-associated differences in cell states and interactions in human estrogen receptor-positive (ER+) and triple-negative breast cancers (TNBC) using new computational analyses of existing single-cell gene expression data. Age-specific program enrichment (ASPEN) analysis reveals age-related changes, including increased tumor cell epithelial-mesenchymal transition, cancer-associated fibroblast inflammatory responses, and T cell stress responses and apoptosis in TNBC. ER+ breast cancer is dominated by increased cancer cell estrogen receptor 1 (ESR1) and luminal cell activity, reduced immune cell metabolism, and decreased vascular and extracellular matrix (ECM) remodeling with age. Cell interactome analysis reveals candidate signaling pathways that drive many of these cell states. This work lays a foundation for discovery of age-adapted therapeutic interventions for breast cancer.

Predictive model of gene expression regulating invasion and migration of M2 macrophages in breast cancer: clinical prognosis and therapeutic implications

Background

Breast cancer (BRCA) has surpassed lung cancer to become the malignant tumor with the highest incidence in female population. It occurs in malignant cells in breast tissue and is common worldwide. An increasing body of research indicates that M2 macrophages are critical to the occurrence and progression of BRCA. The aim of this work is to build a predictive model of genes related to invasion and migration of M2 macrophages, forecast the prognosis of patients with BRCA, and then evaluate the efficacy of some targeted treatments.

Methods

The Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database supplied the GSE20685 dataset, whereas the expression profile a clinical details of BRCA patients were obtained from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov/) database. The genes linked to M2 macrophages and the differentially elevated genes of invasion and migration were found in GSE20685. To explore the prognosis-related invasion and migration M2 macrophage genes, the TCGA-BRCA dataset was merged with Cox regression and least absolute shrinkage and selection operator (LASSO) regression. GSE58812 was utilized for external validation. After calculating each patient's risk score, the prognostic model was examined by analyses of immune infiltration, medication sensitivity, mutation, and enrichment of the risk score.

Results

The risk score had a strong correlation with both several immune cells and popular anti-tumor medications. Additionally, it was discovered that the risk score was a separate prognostic factor for BRCA.

Conclusions

Based on invasion and migration-related M2 macrophage genes, we investigated and validated predictive characteristics in our study that may offer helpful insights into the progression and prognosis of BRCA.

PCMT1 confirmed as a pan-cancer immune biomarker and a contributor to breast cancer metastasis

Protein L-isoaspartyl (D-aspartyl) methyltransferase (PIMT, gene name PCMT1) is an enzyme that repairs proteins with altered aspartate residues by methylation, restoring their normal structure and function. This study conducted a comprehensive analysis of PCMT1 in pan-cancer. The Cancer Genome Atlas, Human Protein Atlas website, and the Genotype-Tissue Expression were utilized in analysis of PCMT1 expression. We examined the association between PCMT1 expression and various factors, including gene modifications, DNA methylation, immune cell infiltration, immunological checkpoints, drug susceptibility, tumor mutation burden (TMB), and microsatellite instability (MSI). Enrichment analyses determined the potential biological roles and pathways involving PCMT1. Our focus then shifted to the role of PCMT1 in breast invasive carcinoma (BRCA). We found that PCMT1 expression was aberrant in many tumors and significantly influenced the prognosis across several cancer types. Gene alterations in PCMT1 predominantly involved deep deletions and amplifications. A negative correlation was observed between DNA methylation and PCMT1 expression across all studied cancer types except thyroid carcinoma PCMT1 exhibited positive correlations with common lymphoid progenitor and CD4(+) T helper 2 cells, whereas it was inversely correlated with central and effector memory T cells, memory CD8(+) T cells, and CD4(+) T helper 1 cells. In many cancer types, PCMT1 expression closely correlated with immunological checkpoint inhibitors, TMB, and MSI. It was also significantly linked to pathways involved in epithelial-mesenchymal transition (EMT), highlighting its role in cancer metastasis. PCMT1 emerged as a significant predictor of breast cancer progression. In vitro experiments demonstrated that reducing PCMT1 expression decreased BRCA cell migration and invasiveness. Additionally, animal studies confirmed that inhibition of PCMT1 slowed tumor growth.

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer

BACKGROUND

There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC.

METHODS

Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors.

RESULTS

The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1.

CONCLUSION

This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

Immunotherapy targeting isoDGR-protein damage extends lifespan in a mouse model of protein deamidation

Aging results from the accumulation of molecular damage that impairs normal biochemical processes. We previously reported that age-linked damage to amino acid sequence NGR (Asn-Gly-Arg) results in "gain-of-function" conformational switching to isoDGR (isoAsp-Gly-Arg). This integrin-binding motif activates leukocytes and promotes chronic inflammation, which are characteristic features of age-linked cardiovascular disorders. We now report that anti-isoDGR immunotherapy mitigates lifespan reduction of Pcmt1-/- mouse. We observed extensive accumulation of isoDGR and inflammatory cytokine expression in multiple tissues from Pcmt1-/- and naturally aged WT animals, which could also be induced via injection of isoDGR-modified plasma proteins or synthetic peptides into young WT animals. However, weekly injection of anti-isoDGR mAb (1 mg/kg) was sufficient to significantly reduce isoDGR-protein levels in body tissues, decreased pro-inflammatory cytokine concentrations in blood plasma, improved cognition/coordination metrics, and extended the average lifespan of Pcmt1-/- mice. Mechanistically, isoDGR-mAb mediated immune clearance of damaged isoDGR-proteins via antibody-dependent cellular phagocytosis (ADCP). These results indicate that immunotherapy targeting age-linked protein damage may represent an effective intervention strategy in a range of human degenerative disorders.

Identifying prognostic markers in spatially heterogeneous breast cancer microenvironment

To gain deeper insights into the microenvironment of breast cancer, we utilized GeoMx Digital Spatial Profiling (DSP) technology to analyze transcripts from 107 regions of interest in 65 untreated breast cancer tissue samples. Our study revealed spatial heterogeneity in the expression of marker genes in tumor cell enriched, immune cell enriched, and normal epithelial areas. We evaluated a total of 55 prognostic markers in tumor cell enriched regions and 15 in immune cell enriched regions, identifying that tumor cell enriched regions had higher levels of follicular helper T cells, resting dendritic cells, and plasma cells than immune cell enriched regions, while the levels of resting CD4 memory in T cells and regulatory (Treg) T cells were lower. Additionally, we analyzed the heterogeneity of HLA gene families, immunological checkpoints, and metabolic genes in these areas. Through univariate Cox analysis, we identified 5 prognosis-related metabolic genes. Furthermore, we conducted immunostaining experiments, including EMILIN2, SURF4, and LYPLA1, to verify our findings. Our investigation into the spatial heterogeneity of the breast cancer tumor environment has led to the discovery of specific diagnostic and prognostic markers in breast cancer.

Bioinformatics identification and validation of aging-related molecular subtype and prognostic signature in sarcoma

Aging could regulate many biological processes in malignancies by regulating cell senescence. Consensus cluster analysis was conducted to differentiate TCGA sarcoma cases. LASSO cox regression analysis was performed to construct an aging-related prognostic signature. We identified two categories of TCGA-sarcoma with significant difference in prognosis, immune infiltration and chemotherapy and targeted therapy. Moreover, an aging-related prognostic signature was constructed for sarcoma, which had a good performance in predicting the 3-year and 5-year overall survival of sarcoma patients. We also identified a lncRNA MALAT1/miR-508-3p/CCNA2 regulatory axis for sarcoma. This stratification could provide more evidence for estimating prognosis and immunotherapy of sarcoma.

A Novel Aging-Related Prognostic lncRNA Signature Correlated with Immune Cell Infiltration and Response to Immunotherapy in Breast Cancer

Breast cancer (BC) is among the most universal malignant tumors in women worldwide. Aging is a complex phenomenon, caused by a variety of factors, that plays a significant role in tumor development. Consequently, it is crucial to screen for prognostic aging-related long non-coding RNAs (lncRNAs) in BC. The BC samples from the breast-invasive carcinoma cohort were downloaded from The Cancer Genome Atlas (TCGA) database. The differential expression of aging-related lncRNAs (DEarlncRNAs) was screened by Pearson correlation analysis. Univariate Cox regression, LASSO-Cox analysis, and multivariate Cox analysis were performed to construct an aging-related lncRNA signature. The signature was validated in the GSE20685 dataset from the Gene Expression Omnibus (GEO) database. Subsequently, a nomogram was constructed to predict survival in BC patients. The accuracy of prediction performance was assessed through the time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, principal component analyses, decision curve analysis, calibration curve, and concordance index. Finally, differences in tumor mutational burden, tumor-infiltrating immune cells, and patients' response to chemotherapy and immunotherapy between the high- and low-risk score groups were explored. Analysis of the TCGA cohort revealed a six aging-related lncRNA signature consisting of MCF2L-AS1, USP30-AS1, OTUD6B-AS1, MAPT-AS1, PRR34-AS1, and DLGAP1-AS1. The time-dependent ROC curve proved the optimal predictability for prognosis in BC patients with areas under curves (AUCs) of 0.753, 0.772, and 0.722 in 1, 3, and 5 years, respectively. Patients in the low-risk group had better overall survival and significantly lower total tumor mutational burden. Meanwhile, the high-risk group had a lower proportion of tumor-killing immune cells. The low-risk group could benefit more from immunotherapy and some chemotherapeutics than the high-risk group. The aging-related lncRNA signature can provide new perspectives and methods for early BC diagnosis and therapeutic targets, especially tumor immunotherapy.

Construction and validation of an aging-related gene signature for prognosis prediction of patients with breast cancer

BACKGROUND

Breast cancer (BC) is an aging-related disease. Aging-related genes (ARGs) participate in the initiation and development of lung and colon cancer, but the prognosis signature of ARGs in BC has not been clearly studied.

AIMS

This study aimed to construct an ARGs signature to predict the prognosis of patients with breast cancer.

METHOD

Firstly, the expression data of ARGs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were collected. Then COX and least absolulute shrinkage and selection operator(LASSO) were performed to construct the ARGs prognostic signature. The correlation between the signature and immune cell infiltration, immunotherapeutic response and drug sensitivity were subsequently analysed. The TCGA nomogram was constructed by combining the signature with other clinical features, and was validated by using GEO database.

RESULTS

After LASSO and COX regression analyses, a prognostic signature based on nine ARGs, namely, HSP90AA1, NFKB2, PLAU, PTK2, RECQL4, CLU, JAK2, MAP3K5, and S100B, was built by using the TCGA dataset. Moreover, this risk signature is closely related to immune cell infiltration, immunotherapeutic response, and responses to chemotherapy and targeted therapy. Subsequently, The calibration curve demonstrates that the nomogram agrees well with practical prediction results. The receiver operating characteristic curve and decision-making curve analysis demonstrate that ARG signature has the better prognosis diagnosis ability and clinical net benefits.

CONCLUSIONS

Therefore, the proposed ARG prognosis signature is a new prognosis molecular marker of patients with BC, and it can provide good references to individual clinical therapy.

Construction and validation of an aging-related gene signature predicting the prognosis of pancreatic cancer

Background: Pancreatic cancer is a malignancy with a high mortality rate and worse prognosis. Recently, public databases and bioinformatics tools make it easy to develop the prognostic risk model of pancreatic cancer, but the aging-related risk signature has not been reported. The present study aimed to identify an aging-related risk signature with potential prognostic value for pancreatic cancer patients. Method: Gene expression profiling and human clinical information of pancreatic cancer were derived from The Cancer Genome Atlas database (TCGA). Aging-related gene sets were downloaded from The Molecular Signatures Database and aging-related genes were obtained from the Human Ageing Genomic Resources database. Firstly, Gene set enrichment analysis was carried out to investigate the role of aging process in pancreatic cancer. Secondly, differentially expressed genes and aging-related prognostic genes were screened on the basis of the overall survival information. Then, univariate COX and LASSO analysis were performed to establish an aging-related risk signature of pancreatic cancer patients. To facilitate clinical application, a nomogram was established to predict the survival rates of PCa patients. The correlations of risk score with clinical features and immune status were evaluated. Finally, potential therapeutic drugs were screened based on the connectivity map (Cmap) database and verified by molecular docking. For further validation, the protein levels of aging-related genes in normal and tumor tissues were detected in the Human Protein Atlas (HPA) database. Result: The genes of pancreatic cancer were markedly enriched in several aging-associated signaling pathways. We identified 14 key aging-related genes related to prognosis from 9,020 differentially expressed genes and establish an aging-related risk signature. This risk model indicated a strong prognostic capability both in the training set of TCGA cohort and the validation set of PACA-CA cohort and GSE62452 cohort. A nomogram combining risk score and clinical variables was built, and calibration curve and Decision curve analysis (DCA) have proved that it has a good predictive value. Additionally, the risk score was tightly linked with tumor immune microenvironment, immune checkpoints and proinflammatory factors. Moreover, a candidate drug, BRD-A47144777, was screened and verified by molecular docking, indicating this drug has the potential to treat PCa. The protein expression levels of GSK3B, SERPINE1, TOP2A, FEN1 and HIC1 were consistent with our predicted results. Conclusion: In conclusion, we identified an aging-related signature and nomogram with high prediction performance of survival and immune cell infiltration for pancreatic cancer. This signature might potentially help in providing personalized immunotherapy for patients with pancreatic cancer.

PCMT1 has Potential Prognostic Value and Promotes Cell Growth and Motility in Breast Cancer.. [DOI: 10.21203/rs.3.rs-2349165/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Breast cancer (BC) is one of the frequently diagnosed cancers, and the leading cause of cancer-related death among women worldwide. The roles of protein L-isoaspartate (D-aspartate) O-methyltransferase (PCMT1) in human cancer have been exploring, but the clinical significance and biological function of PCMT1 in BC are not yet clear. In this study, based on the TCGA-BRCA data set, the results showed that high expression of PCMT1 gene was significantly correlated with shorter overall survival (OS), disease specific survival (DSS) and progress free suvival (PFS) of BC patients. Utilizing the immunohistochemical assay, we found that PCMT1 protein was located in the cytoplasm of BC cells, and PCMT1 expression was only obviously correlated with progesterone receptor expression of patients (p < 0.05). Survival analysis showed that PCMT1 protein high-expression was an independent unfavorable prognostic factor for BC patients. The in vitro experiments revealed that PCMT1 could regulate growth, migration and invasion capacity of MCF-7 cell, and modulate the expression of AKT/GSK3β/mTOR signaling pathway, EMT and cell cycle-associated protein. In conclusion, PCMT1 was a potential unfavorable prognostic biomarker for BC patient and might influence the AKT/GSK3β/mTOR signaling pathway to regulate the growth and motility of MCF-7 cell.

Comprehensive evaluation of breast cancer immunotherapy and tumor microenvironment characterization based on interleukin genes-related risk model

Breast cancer (BRCA) is the most prevalent malignancy and the leading cause of death in women. Interleukin (IL) genes are critical in tumor initiation and control. Nevertheless, the prognosis value of the IL in BRCA remains unclear. We collected data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and 94 IL genes were identified from GeneCard. Based on the random forest (RF), least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox regression analysis, we constructed an IL signature. GSE22219, GSE25065, and GSE21653 were derived as validation sets. The expression differences in the tumor microenvironment (TME), immunotherapy, and chemosensitivity of BRCA between the high- and low-risk groups were evaluated. Overall, 21 IL genes were selected to construct an IL risk model, of which IL18BP, IL17D, and IL23A were the first time identified as prognostic genes in BRCA. IL score could distinguish BRCA patients with inferior outcomes, and AUC of it was 0.70, 0.76, and 0.72 for 1-,3- and 5- years, respectively, which was also verified in GSE22219, GSE25065, and GSE21653 cohorts. Meanwhile, compared to luminal A and luminal B, HER2-positive and TNBC had significantly higher IL score. Besides, the high-risk group had a significantly higher prevalence of TP53 and TTN but a lower prevalence of PIK3CA, as well as higher tumor mutation burden (TMB) and neoantigen level. High- and low-risk groups exhibited notable differences in immunomodulators and tumor infiltrates immune cells (TIICs), and the high-risk group had significantly lower Tumor Immune Dysfunction and Exclusion (TIDE) score. Additionally, the high-risk group has more responders to immune or anti-HER2 combination therapy, whereas the low-risk group has higher sensitivity to docetaxel and paclitaxel. Consequently, we constructed a reliable risk model based on the IL genes, which can provide more information on both the risk stratification and personalizing management strategies for BRCA.