CD38 Predicts Favorable Prognosis by Enhancing Immune Infiltration and Antitumor Immunity in the Epithelial Ovarian Cancer Microenvironment

Targeting cell cycle arrest in breast cancer by phytochemicals from Caryto urens L. fruit ethyl acetate fraction: in silico and in vitro validation

BACKGROUND

Caryota urens, also known as Shivjata, has been documented in ancient Indian texts for its therapeutic benefits, addressing conditions from seminal weakness to gastric ulcers. This study aims to investigate its contemporary medicinal potential in treating breast cancer.

OBJECTIVES

The study focuses on exploring the therapeutic potential of Caryota urens fruit against breast cancer, specifically targeting cell cycle genes CDK1, CDC25A, and PLK1 through bioinformatics, network pharmacology, and in vitro validation.

MATERIALS AND METHODS

Using mass spectrometry and nuclear magnetic resonance (NMR), 60 key phytoconstituents from Caryota urens fruit were identified. Bioinformatics analysis, integrating Gene Cards and GEO databases, 15,474 breast cancer-associated genes focusing on the HR+/HER2-subtype were identified. Molecular docking and qPCR validated the interactions of key phytoconstituents, particularly Episesamin, with CDK1, CDC25A, and PLK1. In vitro studies were conducted on the MCF7 cell line, supplemented by ROC and survival analyses to evaluate diagnostic and therapeutic potential.

RESULTS

The bioinformatics analysis identified CDK1, CDC25A, and PLK1 as pivotal genes regulating cell cycle progression and breast cancer tumorigenesis. Network pharmacology and in vitro studies indicated that phytoconstituents, especially Episesamin, downregulated these genes in breast cancer cells. Molecular docking and qPCR confirmed these interactions, and ROC and survival analyses underscored their diagnostic and therapeutic significance.

CONCLUSIONS

This study suggests that Caryota urens fruit extract, particularly Episesamin, may inhibit breast cancer metastasis by downregulating CDK1, CDC25A, and PLK1, offering promising new strategies for targeting the cell cycle in breast cancer and emphasizing the value of integrating bioinformatics with experimental methods in cancer research.

From gene modules to gene markers: an integrated AI-human approach selects CD38 to represent plasma cell-associated transcriptional signatures

Background

Knowledge-driven prioritization of candidate genes derived from large-scale molecular profiling data for targeted transcriptional profiling assays is challenging due to the vast amount of biomedical literature that needs to be harnessed. We present a workflow leveraging Large Language Models (LLMs) to prioritize candidate genes within module M12.15, a plasma cell-associated module from the BloodGen3 repertoire, by integrating knowledge-driven prioritization with data-driven analysis of transcriptome profiles.

Methods

The workflow involves a two-step process: (1) high-throughput screening using LLMs to score and rank the 17 genes of module M12.15 based on six predefined criteria, and (2) prioritization employing high-resolution scoring and fact-checking, with human experts validating and refining AI-generated scores.

Results

The first step identified five candidate genes (CD38, TNFRSF17, IGJ, TOP2A, and TYMS). Following human-augmented LLM scoring and fact checking, as part of the second step, CD38 and TNFRSF17 emerged as the top candidates. Next, transcriptome profiling data from three datasets was incorporated in the workflow to assess expression levels and correlations with the module average across various conditions and cell types. It is on this basis that CD38 was prioritized as the top candidate, with TNFRSF17 and IGJ identified as promising alternatives.

Conclusion

This study introduces a systematic framework that integrates LLMs with human expertise for gene prioritization. Our analysis identified CD38, TNFRSF17, and IGJ as the top candidates within the plasma cell-associated module M12.15 from the BloodGen3 repertoire, with their relative rankings varying systematically based on specific evaluation criteria, from plasma cell biology to therapeutic relevance. This criterion-dependent ranking demonstrates the ability of the framework to perform nuanced, multi-faceted evaluations. By combining knowledge-driven analysis with data-driven metrics, our approach provides a balanced and comprehensive method for biomarker selection. The methodology established here offers a reproducible and scalable approach that can be applied across diverse biological contexts and extended to analyze large module repertoires.

Single-cell RNA sequencing in ovarian cancer: Current progress and future prospects

Ovarian cancer is one of the most prevalent gynaecological malignancies. The rapid development of single-cell RNA sequencing (scRNA-seq) has allowed scientists to use this technique to study ovarian cancer development, heterogeneity, and tumour environment. Although multiple original research articles have reported the use of scRNA-seq in understanding ovarian cancer and how therapy resistance occurs, there is a lack of a comprehensive review that could summarize the findings from multiple studies. Therefore, this review aimed to fill this gap by comparing and summarizing the results from different studies that have used scRNA-seq in understanding ovarian cancer development, heterogeneity, tumour microenvironment, and treatment resistance. This review will begin with an overview of scRNA-seq workflow, followed by a discussion of various applications of scRNA-seq in studying ovarian cancer. Next, the limitations and future directions of scRNA-seq in ovarian cancer research will be presented.

Biological Functions and Therapeutic Potential of NAD+ Metabolism in Gynecological Cancers

Nicotinamide adenine dinucleotide (NAD+) is an important cofactor for both metabolic and signaling pathways, with the dysregulation of NAD+ levels acting as a driver for diseases such as neurodegeneration, cancers, and metabolic diseases. NAD+ plays an essential role in regulating the growth and progression of cancers by controlling important cellular processes including metabolism, transcription, and translation. NAD+ regulates several metabolic pathways such as glycolysis, the citric acid (TCA) cycle, oxidative phosphorylation, and fatty acid oxidation by acting as a cofactor for redox reactions. Additionally, NAD+ acts as a cofactor for ADP-ribosyl transferases and sirtuins, as well as regulating cellular ADP-ribosylation and deacetylation levels, respectively. The cleavage of NAD+ by CD38-an NAD+ hydrolase expressed on immune cells-produces the immunosuppressive metabolite adenosine. As a result, metabolizing and maintaining NAD+ levels remain crucial for the function of various cells found in the tumor microenvironment, hence its critical role in tissue homeostasis. The NAD+ levels in cells are maintained by a balance between NAD+ biosynthesis and consumption, with synthesis being controlled by the Preiss-Handler, de novo, and NAD+ salvage pathways. The primary source of NAD+ synthesis in a variety of cell types is directed by the expression of the enzymes central to the three biosynthesis pathways. In this review, we describe the role of NAD+ metabolism and its synthesizing and consuming enzymes' control of cancer cell growth and immune responses in gynecologic cancers. Additionally, we review the ongoing efforts to therapeutically target the enzymes critical for NAD+ homeostasis in gynecologic cancers.

Diagnostic, prognostic, and immunological roles of CDSN in ovarian cancer

Globally, ovarian cancer (OC) ranks as a principal cause of cancer-related mortality in females. Immunotherapy has revolutionized the treatment of OC, but the efficacy of immunotherapy is often limited by different immune microenvironments. The objective of this research was to pinpoint and validate candidate genes with potential value as diagnostic and prognostic biomarkers and therapeutic targets in OC. Data on genes associated with gene mutation, prognostic survival, and immune infiltration in OC were procured from the Cancer Genome Atlas (TCGA). Gene differential analysis, mutation site analysis, prognosis and survival analysis, and functional and signaling pathway enrichment analysis were conducted to identify and evaluate key genes. The genes were further investigated using immune infiltration analysis, receiver operating characteristic curves, and immunohistochemistry. The impact of CDSN on OC cell proliferation was investigated utilizing CCK-8, colony formation, and apoptosis detection assays. We identified a set of genes (CDSN, WARS, and CD38) that were highly expressed in OC and significantly associated with mutations and prognosis. Immune infiltration analysis and immunohistochemistry results indicated a correlation with immune infiltration in the tumor microenvironment, particularly in antigen-presenting cells. Receiver operating characteristic curve analysis demonstrated the diagnostic potential of these three genes in OC, with all three genes showing the area under the curve (AUC) above 0.8. In vitro studies suggested that knocked down CDSN expression resulted in a marked lower in the proliferative capacity of OC cells. The candidate gene CDSN identified through bioinformatics analysis and in vitro experiments is associated with mutation and immune infiltration, showing promise as a diagnostic and prognostic biomarker, as well as a therapeutic objective in OC.

CT-based radiomics predicts CD38 expression and indirectly reflects clinical prognosis in epithelial ovarian cancer

Background

Cluster of differentiation 38 (CD38) has been found to be highly expressed in various solid tumours, and its expression level may be associated with patient prognosis and survival. This study aimed to evaluate the prognostic value of CD38 expression for patients with epithelial ovarian cancer (EOC) and construct two computed tomography (CT)-based radiomics models for predicting CD38 expression.

Methods

A total of 333 cases of EOC were enrolled from The Cancer Genome Atlas (TCGA) database for CD38-related bioinformatics and survival analysis. A total of 56 intersection cases from TCGA and The Cancer Imaging Archive (TCIA) databases were selected for radiomics feature extraction and model construction. Logistic regression (LR) and support vector machine (SVM) models were constructed and internally validated using 5-fold cross-validation to assess the performance of the models for CD38 expression levels.

Results

High CD38 expression was an independent protective factor (HR = 0.540) for overall survival (OS) in EOC patients. Five radiomics features based on CT images were selected to build models for the prediction of CD38 expression. In the training and internal validation sets, for the receiver operating characteristic (ROC) curve, the LR model reached an area under the curve (AUC) of 0.739 and 0.732, while the SVM model achieved AUC values of 0.741 and 0.700, respectively. For the precision-recall (PR) curve, the LR and SVM models demonstrated an AUC of 0.760 and 0.721. The calibration curves and decision curve analysis (DCA) provided evidence supporting the fitness and net benefit of the models.

Conclusions

High levels of CD38 expression can improve OS in EOC patients. CT-based radiomics models can be a new predictive tool for CD38 expression, offering possibilities for individualised survival assessment for patients with EOC.

Characterization of purinergic signaling in tumor-infiltrating lymphocytes from lower- and high-grade gliomas

Malignant gliomas are highly heterogeneous glia-derived tumors that present an aggressive and invasive nature, with a dismal prognosis. The multi-dimensional interactions between glioma cells and other tumor microenvironment (TME) non-tumoral components constitute a challenge to finding successful treatment strategies. Several molecules, such as extracellular purines, participate in signaling events and support the immunosuppressive TME of glioma patients. The purinergic signaling and the ectoenzymes network involved in the metabolism of these extracellular nucleotides are still unexplored in the glioma TME, especially in lower-grade gliomas (LGG). Also, differences between IDH-mutant (IDH-Mut) versus wild-type (IDH-WT) gliomas are still unknown in this context. For the first time, to our knowledge, this study characterizes the TME of LGG, high-grade gliomas (HGG) IDH-Mut, and HGG IDH-WT patients regarding purinergic ectoenzymes and P1 receptors, focusing on tumor-infiltrating lymphocytes. Here, we show that ectoenzymes from both canonical and non-canonical pathways are increased in the TME when compared to the peripheral blood. We hypothesize this enhancement supports extracellular adenosine generation, hence increasing TME immunosuppression.

CD38 regulates ovarian function and fecundity via NAD+ metabolism

Mammalian female reproductive lifespan is typically significantly shorter than life expectancy and is associated with a decrease in ovarian NAD+ levels. However, the mechanisms underlying this loss of ovarian NAD+ are unclear. Here, we show that CD38, an NAD+ consuming enzyme, is expressed in the ovarian extrafollicular space, primarily in immune cells, and its levels increase with reproductive age. Reproductively young mice lacking CD38 exhibit larger primordial follicle pools, elevated ovarian NAD+ levels, and increased fecundity relative to wild type controls. This larger ovarian reserve results from a prolonged window of follicle formation during early development. However, the beneficial effect of CD38 loss on reproductive function is not maintained at advanced age. Our results demonstrate a novel role of CD38 in regulating ovarian NAD+ metabolism and establishing the ovarian reserve, a critical process that dictates a female's reproductive lifespan.

Clinical significance and immune infiltration analyses of a novel coagulation-related signature in ovarian cancer

Ovarian cancer (OV) is the most lethal gynecological malignancies worldwide. The coagulation cascade could induce tumor cell infiltration and contribute to OV progression. However, coagulation-related gene (CRG) signature for OV prognosis hasn't been determined yet. In this study, we evaluated the prognostic value of coagulation scores through receiver operating characteristics (ROC) analysis and K-M curves, among OV patients at our institution. Based on the transcriptome data of TCGA-OV cohort, we stratified two coagulation-related subtypes with distinct differences in prognosis and tumor immune microenvironment (p < 0.05). Moreover, from the 6406 differentially-expressed genes (DEGs) between the GTEx (n = 180) and TCGA-OV cohorts (n = 376), we identified 138 potential CRGs. Through LASSO-Cox algorithm, we finally distinguished a 3-gene signature (SERPINA10, CD38, and ZBTB16), with promising prognostic ability in both TCGA (p < 0.001) and ICGC cohorts (p = 0.040). Stepwise, we constructed a nomogram based on the clinical features and coagulation-related signature for overall survival prediction, with the C-index of 0.6761, which was evaluated by calibration curves. Especially, based on tissue microarrays analysis, Quantitative real-time fluorescence PCR (qRT-PCR), and Western Blot, we found that aberrant upregulation of CRGs was related to poor prognosis in OV at both mRNA and protein level (p < 0.05). Collectively, the coagulation-related signature was a robust prognostic biomarker, which could provide therapeutic benefits for chemotherapy/immunotherapy and assist clinical decision in OV patients.

Identification of N1 methyladenosine-related biomarker predicting overall survival outcomes and experimental verification in ovarian cancer

AIM

This study aimed to construct a N1-methyladenosine (m1A)-related biomarker model for predicting the prognosis of ovarian cancer (OVCA).

METHODS

OVCA samples were clustered into two subtypes using the Non-Negative Matrix Factorization (NMF) algorithm, including TCGA (n = 374) as the training set and GSE26712 (n = 185) as the external validation set. Hub genes, which were screened to construct a risk model, and nomogram to predict the overall survival of OVCA were explored and validated through various bioinformatic analysis and quantitative real-time PCR.

RESULTS

Following bootstrap correction, the C-index of nomogram was 0.62515, showing reliable performance. The functions of DEGs in the high- and low-risk groups were mainly enriched in immune response, immune regulation, and immune-related diseases. The immune cells relevant to the expression of hub genes were explored, for example, Natural Killer (NK) cells, T cells, activated dendritic cells (aDC).

CONCLUSIONS

AADAC, CD38, CACNA1C, and ATP1A3 might be used as m1A-related biomarkers for OVCA, and the nomogram labeled with m1A for the first time had excellent performance for predicting overall survival in OVCA.

Crosstalk of Immune Cells and Platelets in an Ovarian Cancer Microenvironment and Their Prognostic Significance

Ovarian cancer (OC) is one of the deadliest gynecological cancers, largely due to the fast development of metastasis and drug resistance. The immune system is a critical component of the OC tumor microenvironment (TME) and immune cells such as T cells, NK cells, and dendritic cells (DC) play a key role in anti-tumor immunity. However, OC tumor cells are well known for evading immune surveillance by modulating the immune response through various mechanisms. Recruiting immune-suppressive cells such as regulatory T cells (Treg cells), macrophages, or myeloid-derived suppressor cells (MDSC) inhibit the anti-tumor immune response and promote the development and progression of OC. Platelets are also involved in immune evasion by interaction with tumor cells or through the secretion of a variety of growth factors and cytokines to promote tumor growth and angiogenesis. In this review, we discuss the role and contribution of immune cells and platelets in TME. Furthermore, we discuss their potential prognostic significance to help in the early detection of OC and to predict disease outcome.

Integration of local and systemic immunity in ovarian cancer: Implications for immunotherapy

Cancer is a disease that induces many local and systemic changes in immunity. The difficult nature of ovarian cancer stems from the lack of characteristic symptoms that contributes to a delayed diagnosis and treatment. Despite the enormous progress in immunotherapy, its efficacy remains limited. The heterogeneity of tumors, lack of diagnostic biomarkers, and complex immune landscape are the main challenges in the treatment of ovarian cancer. Integrative approaches that combine the tumor microenvironment - local immunity - together with periphery - systemic immunity - are urgently needed to improve the understanding of the disease and the efficacy of treatment. In fact, multiparametric analyses are poised to improve our understanding of ovarian tumor immunology. We outline an integrative approach including local and systemic immunity in ovarian cancer. Understanding the nature of both localized and systemic immune responses will be crucial to boosting the efficacy of immunotherapies in ovarian cancer patients.

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Background: Immunotherapy is a promising strategy for ovarian cancer (OC), and this study aims to identify biomarkers related to CD8⁺ T cell infiltration to further discover the potential therapeutic target. Methods: Three datasets with OC transcriptomic data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Two immunotherapy treated cohorts were obtained from the Single Cell Portal and Mariathasan's study. The infiltration fraction of immune cells was quantified using three different algorithms, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and microenvironment cell populations counter (MCPcounter), and single-sample GSEA (ssGSEA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the co-expression modules and related genes. The nonnegative matrix factorization (NMF) method was proposed for sample classification. The mutation analysis was conducted using the "maftools" R package. Key molecular markers with implications for prognosis were screened by univariate COX regression analysis and K-M survival analysis, which were further determined by the receiver operating characteristic (ROC) curve. Results: A total of 313 candidate CD8⁺ T cell-related genes were identified by taking the intersection from the TCGA-OV and GSE140082 cohorts. The NMF clustering analysis suggested that patients in the TCGA-OV cohort were divided into two clusters and the Cluster 1 group showed a worse prognosis. In contrast, Cluster 2 had higher amounts of immune cell infiltration, elevated ssGSEA scores in immunotherapy, and a higher mutation burden. CSMD3, MACF1, PDE4DIP, and OBSCN were more frequently mutated in Cluster 1, while SYNE2 was more frequently mutated in Cluster 2. CD38 and CXCL13 were identified by univariate COX regression analysis and K-M survival analysis in the TCGA-OV cohort, which were further externally validated in GSE140082 and GSE32062. Of note, patients with lower CXCL13 expression showed a worse prognosis and the CR/PR group had a higher expression of CXCL13 in two immunotherapy treated cohorts. Conclusion: OC patients with different CD8⁺ T cell infiltration had distinct clinical prognoses. CXCL13 might be a potential therapeutic target for the treatment of OC.

Construction of a Macrophage Infiltration Regulatory Network and Related Prognostic Model of High-Grade Serous Ovarian Cancer

Background

High-grade serous ovarian cancer (HGSOC) carries the highest mortality in the gynecological cancers; however, therapeutic outcomes have not significantly improved in recent decades. Macrophages play an essential role in the occurrence and development of ovarian cancer, so the mechanisms of macrophage infiltration should be elucidated.

Method

We downloaded transcriptome data of ovarian cancers from the Gene Expression Omnibus and The Cancer Genome Atlas. After rigorous screening, 1566 HGSOC were used for data analysis. CIBERSORT was used to estimate the level of macrophage infiltration and WGCNA was used to identify macrophage-related modules. We constructed a macrophage-related prognostic model using machine learning LASSO algorithm and verified it using multiple HGSOC cohorts.

Results

In the GPL570-OV cohort, high infiltration level of M1 macrophages was associated with a good outcome, while high infiltration level of M2 macrophages was associated with poor outcomes. We used WGCNA to select genes correlated with macrophage infiltration. These genes were used to construct protein-protein interaction maps of macrophage infiltration. IFL44L, RSAD2, IFIT3, MX1, IFIH1, IFI44, and ISG15 were the hub genes in the network. We then constructed a macrophage-related prognostic model composed of CD38, ACE2, BATF2, HLA-DOB, and WARS. The model had the ability to predict the overall survival rate of HGSOC patients in GPL570-OV, GPL6480-OV, TCGA-OV, GSE50088, and GSE26712. In exploring the immune microenvironment, we found that CD4 memory T cells and activated mast cells showed that the degree of infiltration was higher in the high-risk group, while M1 macrophages were the opposite, and HLA molecules were overexpressed in the high-risk group.

Conclusion

We constructed a macrophage infiltration-related protein interaction network that provides a basis for studying macrophages in HGSOC. Our macrophage-related prognostic model is robust and widely applicable. It predicts overall survival in HGSOC patients and may improve HGSOC treatment.

An Integrative Pan-Cancer Analysis of the Oncogenic Role of COPB2 in Human Tumors

Several studies have suggested that coatomer protein complex subunit beta 2 (COPB2) may act as an oncogene in various cancer types. However, no systematic pan-cancer analysis has been performed to date. Therefore, the present study analyzed the potential oncogenic role of COPB2 using TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) datasets. The majority of the cancer types overexpressed the COPB2 protein, and its expression significantly correlated with tumor prognosis. In certain tumors, such as those found in breast and ovarian tissues, phosphorylated S859 exhibited high expression. It was found that mutations of the COPB2 protein in kidney and endometrial cancers exhibited a significant impact on patient prognosis. It is interesting to note that COPB2 expression correlated with the number of cancer-associated fibroblasts in certain tumors, such as cervical and endocervical cancers and colon adenocarcinomas. In addition, COPB2 was involved in the transport of substances and correlated with chemotherapy sensitivity. This is considered the first pan-tumor study, which provided a relatively comprehensive understanding of the mechanism by which COPB2 promotes cancer growth.

Immunological configuration of ovarian carcinoma: features and impact on disease outcome

Epithelial ovarian carcinoma (EOC) is a relatively rare malignancy but is the fifth-leading cause of cancer-related death in women, largely reflecting early, prediagnosis dissemination of malignant disease to the peritoneum. At odds with other neoplasms, EOC is virtually insensitive to immune checkpoint inhibitors, correlating with a tumor microenvironment that exhibits poor infiltration by immune cells and active immunosuppression. Here, we comparatively summarize the humoral and cellular features of primary and metastatic EOC, comparatively analyze their impact on disease outcome, and propose measures to alter them in support of treatment sensitivity and superior patient survival.

Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model

BACKGROUND

Ovarian cancer (OC) is typically diagnosed late, associated with high rates of metastasis and the onset of ascites during late stage disease. Understanding the tumor microenvironment and how it impacts the efficacy of current treatments, including immunotherapies, needs effective in vivo models that are fully characterized. In particular, understanding the role of immune cells within the tumor and ascitic fluid could provide important insights into why OC fails to respond to immunotherapies. In this work, we comprehensively described the immune cell infiltrates in tumor nodules and the ascitic fluid within an optimized preclinical model of advanced ovarian cancer.

METHODS

Green Fluorescent Protein (GFP)-ID8 OC cells were injected intraperitoneally into C57BL/6 mice and the development of advanced stage OC monitored. Nine weeks after tumor injection, mice were sacrificed and tumor nodules analyzed to identify specific immune infiltrates by immunohistochemistry. Ascites, developed in tumor bearing mice over a 10-week period, was characterized by mass cytometry (CyTOF) to qualitatively and quantitatively assess the distribution of the immune cell subsets, and their relationship to ascites from ovarian cancer patients.

RESULTS

Tumor nodules in the peritoneal cavity proved to be enriched in T cells, antigen presenting cells and macrophages, demonstrating an active immune environment and cell-mediated immunity. Assessment of the immune landscape in the ascites showed the predominance of CD8+ , CD4+ , B- , and memory T cells, among others, and the coexistance of different immune cell types within the same tumor microenvironment.

CONCLUSIONS

We performed, for the first time, a multiparametric analysis of the ascitic fluid and specifically identify immune cell populations in the peritoneal cavity of mice with advanced OC. Data obtained highlights the impact of CytOF as a diagnostic tool for this malignancy, with the opportunity to concomitantly identify novel targets, and define personalized therapeutic options.

TGFβ1: An Indicator for Tumor Immune Microenvironment of Colon Cancer From a Comprehensive Analysis of TCGA

BACKGROUND

Tumor microenvironment (TME) and tumor-infiltrating immune cells (TICs) greatly participate in the genesis and development of colon cancer (CC). However, there is little research exploring the dynamic modulation of TME.

METHODS

We analyzed the proportion of immune/stromal component and TICs in the TME of 473 CC samples and 41 normal samples from The Cancer Genome Atlas (TCGA) database through ESTIMATE and CIBERSORT algorithms. Correlation analysis was conducted to evaluate the association between immune/stromal component in the TME and clinicopathological characteristics of CC patients. The difference analysis was performed to obtain the differentially expressed genes (DEGs). These DEGs were further analyzed by GO and KEGG enrichment analyses, PPI network, and COX regression analysis. Transforming growth factor β1 (TGFβ1) was finally overlapped from the above analysis. Paired analysis and GSEA were carried out to understand the role of TGFβ1 in colon cancer. The intersection between the difference analysis and correlation analysis was conducted to learn the association between TGFβ1 and TICs.

RESULTS

Our results showed that the immune component in the TME was negatively related with the stages of CC. GO and KEGG enrichment analysis revealed that 1,110 DEGs obtained from the difference analysis were mainly enriched in immune-related activities. The intersection analysis between PPI network and COX regression analysis indicated that TGFβ1 was significantly associated with the communication of genes in the PPI network and the survival of CC patients. In addition, TGFβ1 was up-regulated in the tumor samples and significantly related with poor prognosis of CC patients. Further GSEA suggested that genes in the TGFβ1 up-regulated group were enriched in immune-related activities and the function of TGFβ1 might depend on the communications with TICs, including T cells CD4 naïve and T cells regulatory.

CONCLUSION

The expression of TGFβ1 might be an indicator for the tumor immune microenvironment of CC and serve as a prognostic factor. Drugs targeting TGFβ1 might be a potential immunotherapy for CC patients in the future.

The role of CD38 in HIV infection

The widely-expressed molecule CD38 is a single-stranded type II transmembrane glycoprotein that is mainly involved in regulating the differentiation and activation state of the cell. CD38 has broad and complex functions, including enzymatic activity, intercellular signal transduction, cell activation, cytokine production, receptor function and adhesion activity, and it plays an important role in the physiological and pathological processes of many diseases. Many studies have shown that CD38 is related to the occurrence and development of HIV infection, and CD38 may regulate its progression through different mechanisms. Therefore, investigating the role of CD38 in HIV infection and the potential signaling pathways that are involved may provide a new perspective on potential treatments for HIV infection. In the present review, the current understanding of the roles CD38 plays in HIV infection are summarized. In addition, the specific role of CD38 in the process of HIV infection of human CD4⁺ T lymphocytes is also discussed.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.