The immune-related gene CD52 is a favorable biomarker for breast cancer prognosis

Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding

Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.

Breast cancer clinical outcomes and tumor immune microenvironment: cross-dialogue of multiple epigenetic modification profiles

The discovery of RNA methylation alterations associated with cancer holds promise for their utilization as potential biomarkers in cancer diagnosis, prognosis, and prediction. RNA methylation has been found to impact the immunological microenvironment of tumors, but the specific role of methylation-related genes (MRGs), particularly in breast cancer (BC), the most common cancer among women globally, within the tumor microenvironment remains unknown. In this study, we obtained data from TCGA and GEO databases to investigate the expression patterns of MRGs in both genomic and transcriptional domains in BC. By analyzing the data, we identified two distinct genetic groupings that were correlated with clinicopathological characteristics, prognosis, degree of TME cell infiltration, and other abnormalities in MRGs among patients. Subsequently, an MRG model was developed to predict overall survival (OS) and its accuracy was evaluated in BC patients. Additionally, a highly precise nomogram was created to enhance the practical usability of the MRG model. In low-risk groups, we observed lower TBM values and higher TIDE scores. We further explored how MRGs influence a patient's prognosis, clinically significant characteristics, response to therapy, and the TME. These risk signatures have the potential to improve treatment strategies for BC patients and could be applied in future clinical settings. Moreover, they may also be utilized to determine prognosis and biological features in these patients.

Neoadjuvant chemotherapy-induced remodeling of human hormonal receptor-positive breast cancer revealed by single-cell RNA sequencing

Hormone receptor-positive breast cancer (HR+ BC) is known to be relatively insensitive to chemotherapy, and since chemotherapy has remained the major neoadjuvant therapy for HR+ BC, the undetermined mechanism of chemoresistance and how chemotherapy reshapes the immune microenvironment need to be explored by high-throughput technology. By using single-cell RNA sequencing and multiplexed immunofluorescence staining analysis of HR+ BC samples (paired pre- and post-neoadjuvant chemotherapy (NAC)), the levels of previously unrecognized immune cell subsets, including CD8+ T cells with pronounced expression of T-cell development (LMNA) and cytotoxicity (FGFBP2) markers, CD4+ T cells characterized by proliferation marker (ATP1B3) expression and macrophages characterized by CD52 expression, were found to be increased post-NAC, which were predictive of chemosensitivity and their antitumor function was also validated with in vitro experiments. In terms of immune checkpoint expression of CD8+ T cells, we found their changes were inconsistent post-NAC, that LAG3, VSIR were decreased, and PDCD1, HAVCR2, CTLA4, KLRC1 and BTLA were increased. In addition, we have identified novel genomic and transcriptional patterns of chemoresistant cancer cells, both innate and acquired, and have confirmed their prognostic value with TCGA cohorts. By shedding light on the ecosystem of HR+ BC reshaped by chemotherapy, our results uncover valuable candidates for predicting chemosensitivity and overcoming chemoresistance in HR+ BC.

Leveraging a disulfidptosis-related signature to predict the prognosis and immunotherapy effectiveness of cutaneous melanoma based on machine learning

BACKGROUND

Disulfidptosis is a recently discovered programmed cell death pathway. However, the exact molecular mechanism of disulfidptosis in cutaneous melanoma remains unclear.

METHODS

In this study, clustering analysis was performed using data from public databases to construct a prognostic model, which was subsequently externally validated. The biological functions of the model genes were then investigated through various experimental techniques, including qRT-PCR, Western blotting, CCK-8 assay, wound healing assay, and Transwell assay.

RESULTS

We constructed a signature using cutaneous melanoma (CM) data, which accurately predicts the overall survival (OS) of patients. The predictive value of this signature for prognosis and immune therapy response was validated using multiple external datasets. High-risk CM subgroups may exhibit decreased survival rates, alterations in the tumor microenvironment (TME), and increased tumor mutation burden. We initially verified the expression levels of five optimum disulfidptosis-related genes (ODRGs) in normal tissues and CM. The expression levels of these genes were further confirmed in HaCaT cells and three melanoma cell lines using qPCR and protein blotting analysis. HLA-DQA1 emerged as the gene with the highest regression coefficient in our risk model, highlighting its role in CM. Mechanistically, HLA-DQA1 demonstrated the ability to suppress CM cell growth, proliferation, and migration.

CONCLUSION

In this study, a novel signature related to disulfidptosis was constructed, which accurately predicts the survival rate and treatment sensitivity of CM patients. Additionally, HLA-DQA1 is expected to be a feasible therapeutic target for effective clinical treatment of CM.

Shedding light on macrophage immunotherapy in lung cancer

The search for therapeutic options for lung cancer continues to advance, with rapid advances in the search for therapies to improve patient prognosis. At present, systemic chemotherapy, immune checkpoint inhibitor therapy, antiangiogenic therapy, and targeted therapy for driver gene positivity are available in the clinic. Common clinical treatments fail to achieve desired outcomes due to immunosuppression of the tumor microenvironment (TME). Tumor immune evasion is mediated by cytokines, chemokines, immune cells, and other cells such as vascular endothelial cells within the tumor immune microenvironment. Tumor-associated macrophages (TAMs) are important immune cells in the TME, inducing tumor angiogenesis, encouraging tumor cell proliferation and migration, and suppressing antitumor immune responses. Thus, TAM targeting becomes the key to lung cancer immunotherapy. This review focuses on macrophage phenotype, polarization mechanism, role in lung cancer, and advances in macrophage centric immunotherapies.

Mining of clinical and prognosis related genes in the tumor microenvironment of endometrial cancer: A field synopsis of observational study

Endometrial cancer (EC) is the sixth most common malignant tumor in women worldwide, and its morbidity and mortality are on the rise. The purpose of this study was to explore potential tumor microenvironment (TME)-related biomarkers associated with the clinical features and prognosis of EC. The Estimating Stromal and Immune Cells in Malignancy Using Expression Data (ESTIMATE) algorithm was used to calculate TME immune and stromal scores of EC samples and to analyze the relationship between immune/stromal scores, clinical features, and prognosis. Heat maps and Venn maps were used to screen for differentially expressed genes (DEGs). The ESTIMATE algorithm revealed immune score was significantly correlated with overall survival and tumor grade in patients with EC. A total of 1448 DEGs were screened, of which 387 were intersecting genes. Gene Ontology (GO) analysis revealed that the biological processes (BP) related to intersecting genes mainly included T cell activation and regulation of lymphocyte activation. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the intersecting genes were closely related to immune-related signaling pathways. Thirty core genes with more than 7 nodes were identified using protein-protein interaction (PPI) analysis. Six independent prognostic genes of EC were identified using Kaplan-Meier survival analysis and multivariate Cox analysis, namely CD5, BATF, CACNA2D2, LTA, CD52, and NOL4, which are all immune-infiltrating genes that are closely related to clinical features. The current study identified 6 key genes closely related to immune infiltration in the TME of EC that predict clinical outcomes, which may provide new insights into novel prognostic biomarkers and immunotherapy for patients with EC.

Prognostic and immune implications of a novel 7-methylguanosine-related microRNA signature in breast invasive carcinoma: from exploration to validation

OBJECTIVES

This study aims to develop and validate a prognostic signature based on 7-methylguanosine-related (M7G-related) miRNAs for predicting prognosis and immune implications in breast invasive carcinoma (BRCA).

MATERIALS AND METHODS

M7G-related miRNA data of BRCA were obtained from The Cancer Genome Atlas (TCGA). Least absolute shrinkage and selection operator (LASSO)-penalized, univariate, and multivariate Cox regression analyses were used to construct the prognostic signature. Furthermore, the predictive validity was verified using Kaplan-Meier (KM) survival risk and receiver operating characteristic (ROC) plots. Internal random sampling verification was used to simplify and validate the signature. RT-qPCR was used to quantify the expression level of transcriptional profiles. The independent prognostic role of the risk score was validated using univariate and multivariate regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for functional and immune enrichment analysis.

RESULTS

A total of 18 M7G-related miRNAs were identified to construct the prognostic signature in BRCA. The low-risk group exhibited significantly higher overall survival than the high-risk group in the KM survival plot (P < 0.001). The area under the curve (AUC) for 1-, 3-, and 5-year survivals in the ROC curve were 0.737, 0.724, and 0.702, respectively. The survival significance in the training and testing cohorts was confirmed by random sampling verification. The most prominent miRNAs in the signature were the miR-7, miR-139, miR-10b, and miR-4728. Furthermore, immune scores for B, mast, and Th1 cells varied between risk groups. Our research demonstrated that CD52 was the most positively correlated gene with immune cells and functions in BRCA.

CONCLUSION

Our study presents a comprehensive and systematic analysis of M7G-related miRNAs to construct a prognostic signature in BRCA. The signature demonstrated excellent prognostic validity, with the risk score as an independent prognostic factor. These results provide critical evidence for further investigation of M7G miRNAs and offer new insights for BRCA patients in the context of effective immunotherapy.

Specialized functions and sexual dimorphism explain the functional diversity of the myeloid populations during glioma progression

Malignant gliomas are aggressive, hard-to-treat brain tumors. Their tumor microenvironment is massively infiltrated by myeloid cells, mostly brain-resident microglia, bone marrow (BM)-derived monocytes/macrophages, and dendritic cells that support tumor progression. Single-cell omics studies significantly dissected immune cell heterogeneity, but dynamics and specific functions of individual subpopulations were poorly recognized. We use Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) to precisely dissect myeloid cell identities and functionalities in murine GL261 gliomas. We demonstrate that the diversity of myeloid cells infiltrating gliomas is dictated by cell type and cell state. Glioma-activated microglia are the major source of cytokines attracting other immune cells, whereas BM-derived cells show the monocyte-to-macrophage transition in the glioma microenvironment. This transition is coupled with a phenotypic switch from the IFN-related to antigen-presentation and tumor-supportive gene expression. Moreover, we found sex-dependent differences in transcriptional programs and composition of myeloid cells in murine and human glioblastomas.

Enhancing progestin therapy via HDAC inhibitors in endometrial cancer

Uterine endometrial cancer (EC) incidence and deaths are on the rise. Hormone therapy, a traditional treatment regimen for this disease, uses progesterone and its synthetic analogue, progestin, to induce cell differentiation, apoptosis, and inhibition of invasion. This therapy is highly effective for progesterone receptor (PR) positive tumors in the short term. However, responsiveness decreases over time due to loss of PR expression; acquired resistance leads to treatment failure and poor prognosis. Primary resistance occurs in advanced, PR-negative tumors. Regardless, progestin therapy can be effective if the PR downregulation mechanism is reversed and if functional PR expression is restored. Using histone deacetylase inhibitors (HDACi), we inhibited cell proliferation in three EC cell lines and restored functional PR expression at the mRNA and protein levels. Two HDACi were tested using an endometrial xenograft tumor model: entinostat, an oral drug, and romidepsin, an IV drug. In vitro and in vivo studies support that entinostat decreased EC tumor growth, induced differentiation, and increased expression of the PR-targeted gene, PAEP. These findings supported the approval of a new NIH NCTN clinical trial, NRG-GY011, which concluded that dual treatment of MPA and entinostat, decreased expression of the proliferation marker, Ki67, but did not increase PR expression relative to single treatment with MPA in this short-term study. Therefore, a more potent HDACi, romidepsin, was investigated. Romidepsin treatment inhibited tumor growth and enhanced progestin treatment efficacy. More importantly, PR, PAEP, and KIAA1324 expressions were upregulated. Using a chromatin immunoprecipitation assay, we verified that HDACi can reverse PR downregulation mechanisms in mice models. Other potential drug efficacy markers, such as CD52, DLK1, GALNT9, and GNG2, were identified by transcriptome analysis and verified by q-PCR. Many of the upregulated drug efficacy markers predict favorable patient outcomes, while downregulated genes predict worse survival. Here, our current data suggests that romidepsin is a more potent HDACi that has the potential to achieve more robust upregulation of PR expression and may be a more promising candidate for future clinical trials.

[CD40LG is a novel immune- and stroma-related prognostic biomarker in the tumor microenvironment of invasive breast cancer]

OBJECTIVE

To identify tumor microenvironment (TME)- related genes associated with the occurrence of invasive breast cancer as potential prognostic biomarkers and therapeutic targets.

METHODS

RNA transcriptome data and clinically relevant data were retrieved from TCGA database, and the StromalScore and ImmuneScore were calculated using the ESTIMATE algorithm. The differentially expressed genes (DEGs) were screened by taking the intersection. A protein- protein interaction network was established, and univariate COX regression analysis was used to identify the core genes among the DEGs. A core gene was selected for GSEA and CIBERSORT analysis to determine the function of the core gene and the proportion of tumor-infiltrating immune cells, respectively. Western blotting and qRT-PCR were performed to verify the expression level of CD40LG in breast cancer cell lines and clinical specimens.

RESULTS

A total of 1222 samples (124 normal and 1098 tumor samples) were extracted from TCGA for analysis, from which 487 DEGs were identified. These genes were mainly enriched in immune-related pathways, and crossover analysis identified 11 key genes (CD40LG, ITK, CD5, CD3E, SPN, IL7R, CD48, CCL19, CD2, CD52, and CD2711) associated with breast cancer TME status. CD40LG was selected as the core gene, whose high expression was found to be associated with a longer overall survival of breast cancer patients (P=0.002), and its expression level differed significantly with TNM stage and tumor size (P < 0.05). GSEA and CIBERSORT analyses indicated that CD40LG expression level was associated with immune activity in the TME. Western blotting and qRT-PCR showed that the protein and mRNA expression of CD40LG were significantly lower in breast cancer cells and cancer tissues than in normal breast cells and adjacent tissues.

CONCLUSIONS

The high expression of CD40LG in TME is positively correlated with the survival of patients with invasive breast cancer, suggesting its value as a potential new biomarker for predicting prognosis of the patients.

Using immune-related lncRNAs to construct novel biomarkers and investigate the immune landscape of breast cancer

Background

The role of immune-related long noncoding RNAs (irlncRNAs) in breast cancer (BRCA) is still unclear. Recently, studies have performed analyses based on the expression of irlncRNAs, however, in the present study, we used a novel method that did not require the specific expression levels of lncRNAs of BRCA patients.

Methods

We downloaded transcriptome and clinical data of BRCA patients from The Cancer Genome Atlas (TCGA), obtained immune genes from the Immport database, and extracted immune genes and lncRNAs for correlation analysis. Then, the differential expression of irlncRNA pairs (IRLPs) was determined and the prognostic signature was established by the IRLPs. The immune cell abundance of the TCGA-BRCA cohort was downloaded from the Tumor IMmune Estimation Resource (TIMER) database, and the relationship between the risk score of the IRLP signature and immune cell abundance was analyzed. Finally, we explored the relationship between risk scores and drug sensitivity based on the R package pRRophetic.

Results

Univariate cox regression results showed that 33 IRLPs had significant effects on the overall survival (OS) of BRCA patients. Then 22 IRLPs were obtained via lasso regression for further analysis. Multivariate regression analysis obtained 12 IRLPs to establish the IRLP prognostic signature. The model showed that this IRLP signature could act as a prognostic biomarker for BRCA patients. Kaplan-Meier (KM) survival analysis indicated that low-risk patients of IRLP’s signature had a better OS (P<0.001). Advanced status BRCA patients may have higher risk scores, and univariate and multivariate cox regression analyses showed that risk scores were independent prognostic factors of clinical features (P<0.001). The results of the relationship between risk scores and immune infiltration showed that M1 macrophages were higher in the low-risk group (P=0.00015), while M2 macrophages were higher in the high-risk group (P=0.0015). The high-risk group had a greater sensitivity to chemotherapeutic agents such as cisplatin, docetaxel, doxorubicin, and gemcitabine.

Conclusions

In present study, we used a novel method that did not require the specific expression levels of lncRNAs of BRCA patients, which can be used as a novel model for predicting the prognosis of BRCA patients.