Prognosis Of Ovarian Cancer Research Articles

Establishment and validation of an immune infiltration predictive model for ovarian cancer

BackgroundThe most prevalent mutation in ovarian cancer is the TP53 mutation, which impacts the development and prognosis of the disease. We looked at how the TP53 mutation associates the immunophenotype of ovarian cancer and the prognosis of the disease.MethodsWe investigated the state of TP53 mutations and expression profiles in culturally diverse groups and datasets and developed an immune infiltration predictive model relying on immune-associated genes differently expressed between TP53 WT and TP53 MUT ovarian cancer cases. We aimed to construct an immune infiltration predictive model (IPM) to enhance the prognosis of ovarian cancer and investigate the impact of the IPM on the immunological microenvironment.ResultsTP53 mutagenesis affected the expression of seventy-seven immune response-associated genes. An IPM was implemented and evaluated on ovarian cancer patients to distinguish individuals with low- and high-IPM subgroups of poor survival. For diagnostic and therapeutic use, a nomogram is thus created. According to pathway enrichment analysis, the pathways of the human immune response and immune function abnormalities were the most associated functions and pathways with the IPM genes. Furthermore, patients in the high-risk group showed low proportions of macrophages M1, activated NK cells, CD8+ T cells, and higher CTLA-4, PD-1, PD-L1, and TIM-3 than patients in the low-risk group.ConclusionsThe IPM model may identify high-risk patients and integrate other clinical parameters to predict their overall survival, suggesting it is a potential methodology for optimizing ovarian cancer prognosis.

Copine 1 predicts poor clinical outcomes by promoting M2 macrophage activation in ovarian cancer.

Copine 1 (CPNE1), a membrane-binding protein, influences the prognosis of various cancers. According to cBioPortal, CPNE1 amplification is a prevalent genetic mutation in ovarian cancer but with unknown oncogenic mechanism. This study analysed the CPNE1 expression in ovarian cancer using online datasets, as validated by immunohistochemistry (IHC), quantitative polymerase chain reaction (qPCR) and western blotting. Concurrently, the prognostic value of CPNE1 was accessed. Cell Counting Kit-8, colony formation, transwells and xenograft experiments were performed to evaluate the functions of CPNE1 during ovarian cancer carcinogenesis. CPNE1 and its related genes were analysed by g:Profiler and Tumour Immune Estimation Resource. Furthermore, human monocytic THP-1 cells were co-cultured with ES2 cells to investigate the effect of CPNE1 on macrophage polarization. The results of bioinformatic analysis, IHC, qPCR and western blotting indicated a higher CPNE1 in ovarian cancer. CPNE1 overexpression demonstrated an association with a poor prognosis of ovarian cancer. Functionally, CPNE1 overexpression increased ES2 and SKOV3 cell proliferation, invasion and migration in vitro and promoted ovarian tumour xenograft growth in vivo, while CPNE1 knockdown led to opposite effects. Additionally, CPNE1 expression demonstrated an association with immune cell infiltration in ovarian cancer, especially macrophage. CPNE1 promoted protumour M2 macrophage polarization by upregulating cluster of differentiation 163 (CD163), CD206 and interleukin-10. Our study revealed that CPNE1 mediated M2 macrophage polarization and provided a therapeutic target for ovarian cancer.

Transcriptome analyses reveal new insights on key determinants of perineural invasion in high-grade serous ovarian cancer.

Perineural invasion (PNI) is a pathological feature of many cancers associated with poor outcomes, metastases, and recurrence. In relation to ovarian cancer (OC), there is no information about PNI's role and mechanisms. Our study found that patients with PNI-positive symptoms had significantly shorter overall survival (OS) time than patients with PNI-negative symptoms. Multivariate analyses demonstrated that PNI represented a substantial independent prognostic factor in OC patients. At the transcriptome level, it is noteworthy that PNI positivity was negatively correlated with the degree of infiltration of immune killer cells in OC tumor tissues, including macrophage, central memory CD4 T-cell, natural killer cells, monocyte, and central memory CD4 T-cell. The results of this study revealed that TAS2Rs proteins were markedly upregulated in PNI-positive OC tissues and predicted poor prognoses. Moreover, Immunohistochemical analysis demonstrated that the TAS2R10 protein was associated with poor prognoses and PNI in OC. Consequently, we found for the first time that PNI was a powerful predictor of poor prognosis in OC and analyzed its expression pattern and some preliminary biochemical characterization, providing new clues for guiding clinical prevention and treatment of OC.

Gene expression of non-homologous end-joining pathways in the prognosis of ovarian cancer

Ovarian cancer is the deadliest gynecologic malignancy in women, with a 46% five-year overall survival rate. The objective of the study was to investigate the effects of non-homologous end-joining (NHEJ) genes on clinical outcomes of ovarian cancer patients. To determine if these genes act as prognostic biomarkers of mortality and disease progression, the expression profiles of 48 NHEJ-associated genes were analyzed using an array of statistical and machine learning techniques: logistic regression models, decision trees, naive-Bayes, two sample t-tests, support vector machines, hierarchical clustering, principal component analysis, and neural networks. In this process, the correlation of genes with patient survival and disease progression and recurrence was noted. Also, multiple features from the gene set were found to have significant predictive capabilities. APTX, BRCA1, PAXX, LIG1, and TP53 were identified as most important out of all the candidate genes for predicting clinical outcomes of ovarian cancer patients.

Integrative analysis from multi-center studies identifies a weighted gene co-expression network analysis-based Tregs signature in ovarian cancer.

Ovarian cancer (OC) is a malignancy associated with poor prognosis and has been linked to regulatory T cells (Tregs) in the immune microenvironment. Nevertheless, the association between Tregs-related genes (TRGs) and OC prognosis remains incompletely understood. The xCell algorithm was used to analyze Tregs scores across multiple cohorts. Weighted gene co-expression network analysis (WGCNA) was utilized to identify potential TRGs and molecular subtypes. Furthermore, we used nine machine learning algorithms to create risk models with prognostic indicators for patients. Reverse transcription-quantitative polymerase chain reaction and immunofluorescence staining were used to demonstrate the immunosuppressive ability of Tregs and the expression of key TRGs in clinical samples. Our study found that higher Tregs scores were significantly correlated with poorer overall survival. Recurrent patients exhibited increased Tregs infiltration and reduced CD8+ T cell. Moreover, molecular subtyping using seven key TRGs revealed that subtype B exhibited higher enrichment of multiple oncogenic pathways and had a worse prognosis. Notably, subtype B exhibited high Tregs levels, suggesting immune suppression. In addition, we validated machine learning-derived prognostic models across multiple platform cohorts to better distinguish patient survival and predict immunotherapy efficacy. Finally, the differential expression of key TRGs was validated using clinical samples. Our study provides novel insights into the role of Tregs in the immune microenvironment of OC. We identified potential therapeutic targets derived from Tregs (CD24, FHL2, GPM6A, HOXD8, NAP1L5, REN, and TOX3) for personalized treatment and created a machining learning-based prognostic model for OC patients, which could be useful in clinical practice.

Analysis of clinicopathological features and prognosis of double primary cervical cancer and ovarian cancer based on SEER database.

Double primary cervical cancer and ovarian cancer refer to the simultaneous or successive appearance of cervical cancer and ovarian cancer in the same patient. Due to the low incidence, there are few relevant reports. Therefore, this study is the first population-based analysis of the clinicopathological features as well as the prognostic status of double primary cervical cancer and ovarian cancer. We look forward to providing a reference for future clinical diagnosis and treatment. In this study, 473 cases of double primary cervical cancer and ovarian cancer were collected from 1975 to 2019 through the SEER database. Double primary cancers were considered non-synchronous when they were diagnosed more than 6months apart and were classified as Group A. Double primary cancers were considered synchronous when the interval between diagnosis of the two tumors was less than or equal to 6months and was classified as group B. In this study, the incidence of double primary cervical cancer and ovarian cancer accounted for 0.39% of primary cervical cancer and 0.24% of primary ovarian cancer in the same period. 80% of patients developed second cancer within 107months of their first cancer being diagnosed. Compared with non-synchronous cancer, synchronous cancer is mainly characterized by simultaneous bilateral ovarian involvement and early clinical stage, but highly malignant, high lymph node metastasis rate, and poor prognosis. Most patients developed second cancer within 107months of their first cancer being diagnosed. Age at diagnosis, bilateral ovarian invasion, the interval between diagnoses, pathological type and stage of ovarian cancer, and grade of cervical cancer are important factors affecting survival, which still needs to be confirmed by more extensive studies in future.

N6-methyladenosine Methyltransferase METTL3 Enhances PTGER2 Expression to Increase Ovarian Cancer Stemness and Chemoresistance.

Ovarian cancer is the second leading cause of gynecologic cancer-associated deaths. Cancer stemness and chemoresistance are responsible for ovarian cancer metastasis and the poor prognosis of patients. In this study, we determined the function of N6-methyladenine (m6A) RNA methylation and prostaglandin E receptor 2 (PTGER2) in ovarian cancer progression. The m6A RNA methylation-associated PTGER2 in ovarian cancer was identified using bioinformatics analysis. The role of PTGER2 in ovarian cancer was elucidated in cell lines and clinical samples with cellular and molecular experiments. In this investigation, bioinformatics analysis based on a public cancer database was used to elucidate the impact of m6A modification on the prognosis of patients with ovarian cancer. Moreover, PTGER2 was identified as a potential oncogene associated with the distant metastasis of ovarian cancer and poor patient prognosis. Interestingly, PTGER2 expression was experimentally shown to be enhanced by N6-adenosine-methyltransferase 70 kDa subunit (METTL3)-mediated m6A modification. In addition, PTGER2 enhanced cancer stem cell self-renewal properties, the epithelial-mesenchymal transition, and DNA damage repair, thus potentiating cell stemness, therapy resistance to carboplatin, proliferation, and metastasis of ovarian cancer. Importantly, PTGER2 expression in clinical samples was associated with distant metastasis, predicted poor patient prognosis, and independently served as a prognostic predictor in ovarian cancer. Our work defines PTGER2 as an oncogene and reveals that PTGER2 is a prognostic predictor and novel therapeutic target for the management of ovarian cancer.

High BMP7 expression is associated with poor prognosis in ovarian cancer.

Bone Morphogenetic Protein 7 (BMP7) is an extracellular signalling protein that belongs to the transforming growth factor-β (TGF- β) superfamily. Previous transcriptomic data suggested that BMP7 expression may be disrupted in ovarian carcinoma and may play an important role in the aggressiveness of the disease. However, the protein expression in patient tumours has not been well studied. The current study aimed to assess BMP7 protein expression in a large cohort of ovarian carcinoma patient tumour samples to establish its associations with different clinical endpoints. Ovarian carcinoma tissue samples from 575 patients who underwent surgery for different subtypes of ovarian cancer were used. BMP7 protein expression was analysed by immunohistochemistry using tissue microarray and full face tumour sections. High BMP7 expression is associated with aggressive ovarian cancer clinicopathological variables including advanced FIGO stage, high grade, residual disease and poor overall survival. Elevated cytoplasmic and nuclear BMP7 expression was significantly associated with advanced FIGO stage, high tumour grade, presence of residual tumours and high-grade serous carcinomas (p = 0.001, 0.005, 0.004, <0.001 and p < 0.001, <0.001, 0.002, 0.001 respectively). Increased cytoplasmic and nuclear BMP7 expression was also significantly associated with an adverse overall survival (p = 0.001 and 0.046 respectively). The study highlights the potential of BMP7 as a prognostic tool and as a potential novel target for ovarian cancer therapies to limit disease progression.

Necroptosis-related regulatory pattern and scoring system for predicting therapeutic efficacy and prognosis in ovarian cancer.

Ovarian cancer is difficult to treat and is, therefore, associated with a high fatality rate. Although targeted therapy and immunotherapy have been successfully used clinically to improve the diagnosis and treatment of ovarian cancer, most tumors become drug resistant, and patients experience relapse, meaning that the overall survival rate remains low. There is currently a lack of effective biomarkers for predicting the prognosis and/or outcomes of patients with ovarian cancer. Therefore, we used published transcriptomic data derived from a large ovarian cancer sample set to establish a molecular subtyping model of the core genes involved in necroptosis in ovarian cancer. Clustering analysis and differential gene expression analyses were performed to establish the genomic subtypes related to necroptosis and to explore the patterns of regulatory gene expression related to necroptosis in ovarian cancer. A necroptosis scoring system (NSS) was established using principal component analysis according to different regulatory patterns of necroptosis. In addition, this study revealed important biological processes with essential roles in the regulation of ovarian tumorigenesis, including external encapsulating structure organization, leukocyte migration, oxidative phosphorylation, and focal adhesion. Patients with high NSS scores had unique immunophenotypes, such as more abundant M2 macrophages, monocytes, CD4+ memory T cells, and regulatory T cells. Immune checkpoint CD274 had a greater expression in patients with high NSS values. This NSS could be used as an independent predictor of prognosis to determine the sensitivity of ovarian cancer to various small-molecule inhibitors, immune checkpoint inhibitors, and platinum-based chemotherapy drugs.

The synergistic effect of EMT regulators and m6A modification on prognosis-related immunological signatures for ovarian cancer

Recently, there has been growing interest among researchers in exploring the effects of epithelial-mesenchymal transformation (EMT) or N6-Methyladenosine (m6A) modification regulators on tumor development. However, the synergistic efficiency of these regulators in relation to ovarian cancer development remains unclear. This study aims to explore the transcription patterns of main regulators, including 19 EMT and 22 m6A, in ovarian cancer samples from TCGA datasets and normal samples from GTEx datasets. After conducting a LASSO regression analysis, ten prognostic signatures were identified, namely KIAA1429, WTAP, SNAI1, AXL, IGF2BP1, ELAVL1, CBLL1, CDH2, NANOG and ALKBH5. These signatures were found to have a comprehensive effect on immune infiltrating signatures and the final prognostic outcome. Next, utilizing the ssGSEA algorithm and conducting overall survival analyses, we have identified the key prognosis-related immunological signatures in ovarian cancer to be ALKBH5, WTAP, ELAVL1, and CDH2 as the regulators. The characteristic immune response and related genetic expression have revealed a significant correlation between the alteration of m6A regulators and EMT regulators, indicating a synergistic effect between these two factors in the development of ovarian cancer. In summary, our research offers a novel perspective and strategy to enhance the occurrence, progression, and prognosis of ovarian cancer.

A methylation- and immune-related lncRNA signature to predict ovarian cancer outcome and uncover mechanisms of chemoresistance

Tumor-associated lncRNAs regulated by epigenetic modification switches mediate immune escape and chemoresistance in ovarian cancer (OC). However, the underlying mechanisms and concrete targets have not been systematically elucidated. Here, we discovered that methylation modifications played a significant role in regulating immune cell infiltration and sensitizing OC to chemotherapy by modulating immune-related lncRNAs (irlncRNAs), which represent tumor immune status. Through deep analysis of the TCGA database, a prognostic risk model incorporating four methylation-related lncRNAs (mrlncRNAs) and irlncRNAs was constructed. Twenty-one mrlncRNA/irlncRNA pairs were identified that were significantly related to the overall survival (OS) of OC patients. Subsequently, we selected four lncRNAs to construct a risk signature predictive of OS and indicative of OC immune infiltration, and verified the robustness of the risk signature in an internal validation set. The risk score was an independent prognostic factor for OC prognosis, which was demonstrated via multifactorial Cox regression analysis and nomogram. Moreover, risk scores were negatively related to the expression of CD274, CTLA4, ICOS, LAG3, PDCD1, and PDCD1LG2 and negatively correlated with CD8+, CD4+, and Treg tumor-infiltrating immune cells. In addition, a high-risk score was associated with a higher IC50 value for cisplatin, which was associated with a significantly worse clinical outcome. Next, a competing endogenous RNA (ceRNA) network and a signaling pathway controlling the infiltration of CD8+ T cells were explored based on the lncRNA model, which suggested a potential therapeutic target for immunotherapy. Overall, this study constructed a prognostic model by pairing mrlncRNAs and irlncRNAs and revealed the critical role of the FTO/RP5-991G20.1/hsa-miR-1976/MEIS1 signaling pathway in regulating immune function and enhancing anticancer therapy.

A novel marker integrating multiple genetic alterations better predicts platinum sensitivity in ovarian cancer than HRD score.

Introduction: Platinum-based chemotherapy is the first-line treatment strategy for ovarian cancer patients. The dismal prognosis of ovarian cancer was shown to be stringently associated with the heterogeneity of tumor cells in response to this therapy, therefore understanding platinum sensitivity in ovarian cancer would be helpful for improving patients' quality of life and clinical outcomes. HRDetect, utilized to characterize patients' homologous recombination repair deficiency, was used to predict patients' response to platinum-based chemotherapy. However, whether each of the single features contributing to HRD score is associated with platinum sensitivity remains elusive. Methods: We analyzed the whole-exome sequencing data of 196 patients who received platinum-based chemotherapy from the TCGA database. Genetic features were determined individually to see if they could indicate patients' response to platinum-based chemotherapy and prognosis, then integrated into a Pt-score employing LASSO regression model to assess its predictive performance. Results and discussion: Multiple genetic features, including bi-allelic inactivation of BRCA1/2 genes and genes involved in HR pathway, multiple somatic mutations in genes involved in DNA damage repair (DDR), and previously reported HRD-related features, were found to be stringently associated with platinum sensitivity and improved prognosis. Higher contributions of mutational signature SBS39 or ID6 predicted improved overall survival. Besides, arm-level loss of heterozygosity (LOH) of either chr4p or chr5q predicted significantly better disease-free survival. Notably, some of these features were found independent of HRD. And SBS3, an HRD-related feature, was found irrelevant to platinum sensitivity. Integrated all candidate markers using the LASSO model to yield a Pt-score, which showed better predictive ability compared to HRDetect in determining platinum sensitivity and predicting patients' prognosis, and this performance was validated in an independent cohort. The outcomes of our study will be instrumental in devising effective strategies for treating ovarian cancer with platinum-based chemotherapy.

Improving the cytotoxic response of tumor-infiltrating lymphocytes towards advanced stage ovarian cancer with an oncolytic adenovirus expressing a human vIL-2 cytokine

While the presence of tumor-infiltrating lymphocytes (TILs) associates with improved survival prognosis in ovarian cancer (OvCa) patients, TIL therapy benefit is limited. Here, we evaluated an oncolytic adenovirus coding for a human variant IL-2 (vIL-2) cytokine, Ad5/3-E2F-d24-vIL2 (vIL-2 virus), also known as TILT-452, as an immunotherapeutic strategy to enhance TIL responsiveness towards advanced stage OvCa tumors. Fragments of resected human OvCa tumors were processed into single-cell suspensions, and autologous TILs were expanded from said samples. OvCa tumor specimens were co-cultured with TILs plus vIL-2 virus, and cell killing was assessed in real time through cell impedance measurement. Combination therapy was further evaluated in vivo through a patient-derived xenograft (PDX) ovarian cancer murine model. The combination of vIL-2 virus plus TILs had best cancer cell killing ex vivo compared to TILs monotherapy. These results were supported by an in vivo experiment, where the best OvCa tumor control was obtained when vIL-2 virus was added to TIL therapy. Furthermore, the proposed therapy induced a highly cytotoxic phenotype demonstrated by increased granzyme B intensity in NK cells, CD4+ T, and CD8+ T cells in treated tumors. Our results demonstrate that Ad5/3-E2F-d24-vIL2 therapy consistently improved TILs therapy cytotoxicity in treated human OvCa tumors.

The role of OIP5 in the carcinogenesis and progression of ovarian cancer

BackgroundOpa interacting protein 5 (OIP5), which is a cancer/testis-specific gene, plays a cancer-promoting role in various types of human cancer. However, the role of OIP5 in the carcinogenesis and progression of ovarian cancer remains unknown.MethodsWe first analyzed the expression of OIP5 in ovarian cancer and various human tumors with the Sangerbox online analysis tool. GSE12470, GSE14407 and GSE54388 were downloaded from the Gene Expression Omnibus (GEO) database, and GEO2R was used to screen differentially expressed genes in ovarian cancer tissues. Gene Ontology (GO) enrichment analysis was used to explore the related biological processes. Receiver operating characteristic (ROC) curve was generated to evaluate the predictive ability of OIP5 for ovarian cancer. Next, RT–PCR, immunohistochemistry and Western blotting were utilized to evaluate the expression of OIP5 in ovarian cancer. CCK8, EdU proliferation assays and colony formation assays were used to measure cell proliferation, cell cycle progression was examined by PI staining and flow cytometry, and cell apoptosis was examined by Caspase3/7 activity assays. The effect of OIP5 on the migration and invasion of ovarian cancer cells was analyzed with Transwell assays.ResultsWe found that OIP5 is highly expressed in ovarian cancer through bioinformatics analysis, and importantly, OIP5 may be an important biomarker for the prognosis and diagnosis of ovarian cancer. RT–PCR assays, immunohistochemistry and Western blotting were also used to confirm the high expression of OIP5 in ovarian cancer. Subsequently, we demonstrated that the proliferation and migration of the ovarian cancer cell line A2780 were significantly inhibited after OIP5 gene silencing, apoptosis was increased and cell cycle progression was arrested at the G1 phase.ConclusionThis study indicated that OIP5 was highly expressed in ovarian cancer and that downregulation of OIP5 inhibited the proliferation, migration and invasion of ovarian cancer cells, induced cell cycle arrest and promoted cell apoptosis. Therefore, OIP5 may be an important biomarker for the early diagnosis and potential target for treatment of ovarian cancer.

Single-cell transcriptomics identify TNFRSF1B as a novel T-cell exhaustion marker for ovarian cancer.

Ovarian cancer (OC) patients routinely show poor immunotherapeutic response due to the complex tumour microenvironment (TME). It is urgent to explore new immunotherapeutic markers. Through the single-cell RNA sequencing (scRNA-seq) analyses on high-grade serous OC (HGSOC), moderate severity borderline tumour and matched normal ovary, we identified a novel exhausted T cells subpopulation that related to poor prognosis in OC. Histological staining, multiple immunofluorescences, and flow cytometry were applied to validate some results from scRNA-seq. Furthermore, a tumour-bearing mice model was constructed to investigate the effects of TNFRSF1B treatment on tumour growth in vivo. Highly immunosuppressive TME in HGSOC is displayed compared to moderate severity borderline tumour and matched normal ovary. Subsequently, a novel exhausted subpopulation of CD8+ TNFRSF1B+ T cells is identified, which is associated with poor survival. In vitro experiments demonstrate that TNFRSF1B is specifically upregulated on activated CD8+ T cells and suppressed interferon-γ secretion. The expression of TNFRSF1B on CD8+ T cells is closely related to OC clinical malignancy and is a marker of poor prognosis through 140 OC patients' verification. In addition, the blockade of TNFRSF1B inhibits tumour growth via profoundly remodeling the immune microenvironment in the OC mouse model. Our transcriptomic results analyzed by scRNA-seq delineate a high-resolution snapshot of the entire tumour ecosystem of OC TME. The major applications of our findings were an exhausted subpopulation of CD8+ TNFRSF1B+ T cells for predicting OC patient prognosis and the potential therapeutic value of TNFRSF1B. These findings demonstrated the clinical value of TNFRSF1B as a potential immunotherapy target and extended our understanding of factors contributing to immunotherapy failure in OC.

An analysis of clinical characteristics and prognosis of endometrioid ovarian cancer based on the SEER database and two centers in China (1112)

An analysis of clinical characteristics and prognosis of endometrioid ovarian cancer based on the SEER database and two centers in China (1112)

A tumor-infiltrating immune cells-related pseudogenes signature based on machine-learning predicts outcomes and immunotherapy responses in ovarian cancer

Previous researches have provided evidence for the significant involvement of pseudogenes in immune-related functions across different types of cancer. However, the mechanisms by which pseudogenes regulate immunity in ovarian cancer (OV) and their potential impact on clinical outcomes remain unclear. To address this gap in knowledge, our study utilized a novel computational framework to analyze a total of 491 samples from three public datasets. We employed a combination of 10 machine-learning algorithms to construct a signature known as the tumor-infiltrating immune cells-related pseudogenes signature (TIICPS). The TIICPS, consisting of 12 pseudogenes, demonstrated independent prognostic value for overall survival, surpassing conventional clinical traits, 62 published signatures, and TP53 and BRCA mutation status in three cohorts. Patients with low TIICPS exhibited heightened immune-related pathways, intricate genomic alterations, substantial immune infiltration, and greater potential for immunotherapy efficacy. Consequently, TIICPS holds promise as a predictive tool for prognosis and immunotherapy response in ovarian cancer.

CDK4/6i enhances the antitumor effect of PD1 antibody by promoting TLS formation in ovarian cancer

Ovarian cancer is insensitive to immunotherapy and has a high mortality rate. CDK4/6 inhibitors (CDK4/6i) regulate the tumor microenvironment and play an antitumor role. Our previous research demonstrated that lymphocyte aggregation (tertiary lymphoid structures, TLSs) was observed after CDK4/6i treatment. This may explain the synergistic action of CDK4/6i with the anti-PD1 antibody. However, the key mechanism by which CDK4/6i promotes TLS formation has not been elucidated. We examine the link between TLS and prognosis. Animal models and high-throughput sequencing were used to explore the potential mechanism by which CDK4/6i promotes TLS formation. Our results showed the presence of TLSs was associated with a favorable prognosis for ovarian cancer. CDK4/6i promoted TLS formation and enhanced the immunotherapeutic effect of the anti-PD1 antibody. The potential mechanism of CDK4/6i affecting the formation of TLS may be through modulating SCD1 and its regulatory molecules ATF3 and CCL4. Our findings provide a theoretical basis for the application of CDK4/6i in ovarian cancer.

Prognostic significance of TM4SF1 and DDR1 expression in epithelial ovarian cancer.

Transmembrane 4 L6 family member 1 (TM4SF1) and discoidin domain receptor 1 (DDR1) are expressed in numerous types of cancer, but their expression in epithelial ovarian cancer and the association between their expression and patient prognosis are unclear. The present study aimed to explore the expression of TM4SF1 and DDR1 and their relationship with prognosis in epithelial ovarian cancer. Firstly, the Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) platforms were used to compare the expression levels of TM4SF1 and DDR1 in ovarian cancer and normal ovarian tissue, and Kaplan-Meier plotter was used to analyze the association between gene expression and patient prognosis. The proteins interacting with TM4SF1 and DDR1 were analyzed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and enrichment analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways was conducted for the interacting proteins. Furthermore, immunohistochemical staining was performed to detect the expression of TM4SF1 and DDR1 protein in epithelial ovarian cancer tissue and to analyze the association between expression and prognosis. The Oncomine and GEPIA analyses showed that the expression levels of TM4SF1 and DDR1 were significantly higher in epithelial ovarian cancer than in normal ovarian tissue, and the analysis of clinical samples revealed that TM4SF1 and DDR1 were coexpressed in some cases. STRING analysis indicated that the TM4SF1 and DDR1 proteins interact with each other. The overall survival and progression-free survival of patients whose epithelial ovarian cancer coexpressed TM4SF1 and DDR1 were significantly shorter than those of patients lacking TM4SF1 and DDR1 coexpression. Multivariate analysis indicated that TM4SF1 and DDR1 protein coexpression was an independent prognostic factor. In summary, TM4SF1 and DDR1 proteins were coexpressed in some epithelial ovarian cancer tissues and appear to be adverse prognostic factors for epithelial ovarian cancer. In addition, TM4SF1 and DDR1 may have an interactive or mutual regulatory mechanism.

Construction of an Immunophenoscore-Related Signature for Evaluating Prognosis and Immunotherapy Sensitivity in Ovarian Cancer.

Ovarian cancer (OC) is the deadliest gynecological malignancy in the world, and immunotherapy is emerging as a promising treatment. Immunophenoscore (IPS) is a robust biomarker distinguishing sensitive responders from immunotherapy. In this study, we aimed to construct a prognostic model for predicting overall survival (OS) and identifying patients who would benefit from immunotherapy. First, we combined The Cancer Genome Atlas (TCGA) and The Cancer Immune Atlas (TCIA) data sets and incorporated 229 OC samples into a training cohort. The validation cohort included 240 OC samples from the Gene Expression Omnibus (GEO) cohort. The training cohort was divided into high- and low-IPS subgroups to obtain differentially expressed genes (DEGs). DEGs with OS were identified by Univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct the prognostic model. Then, immune and mutation analyses were performed to explore the relationship between the model and the tumor microenvironment (TME) and tumor mutation burden (TMB). Eighty-three DEGs were obtained between the high-and low-IPS subgroups, where 17 DEGs were associated with OS. The five essential genes were selected to establish the prognostic model, which showed high accuracy for predicting OS and could be an independent survival indicator. OC samples that were divided by risk scores showed distinguished immune status, TME, TMB, immunotherapy response, and chemotherapy sensitivity. Similar results were validated in the GEO cohort. We developed an immunophenoscore-related signature associated with the TME to predict OS and response to immunotherapy in OC.