Analysis of Risk Factors for Secondary Endometrial Cancer-Related Death: A SEER-Based Study.

Purpose: The aim of this study was to analyze the survival of patients with endometrial cancer diagnosed after a prior cancer and identify risk factors of endometrial cancer death in this population. Methods: Totally 1371 women diagnosed with second primary endometrial cancer (SPEC) between 2004 and 2015 were identified using the SEER database. Clinicopathological characteristics were collected, and Fine and Gray regression model was employed to assess the impact of treatment for the first primary cancer (FPC) and SPEC on the mortality of endometrial cancer patients. After propensity score matching (PSM), patients diagnosed with single primary endometrial cancer and SPEC between 2004 and 2015 were included as the second cohort. Kaplan-Meier and Cox survival risk models were used to assess the influence of previous cancer history on survival. Results: Patients previously diagnosed as lung cancer exhibited the lowest overall survival (OS). A diagnostic interval of ≥3 years was significantly associated with higher mortality from SPEC compared with that <3 years. Surgical treatment for SPEC was linked to a reduced risk of endometrial cancer-specific mortality (ECSM) and non-ECSM. Conversely, radiotherapy and chemotherapy were associated with an increased risk of ECSM. The 1-, 3-, and 5-year OS rates of patients with SPEC were significantly lower than those with single primary endometrial cancer whether before or after PSM. Univariate and multivariate analyses further demonstrated that endometrial cancer, either as FPC or SPEC, was independently associated with an increased risk for endometrial cancer-specific survival (ECSS) and OS. Conclusion: Chemotherapy and radiotherapy for SPEC can elevate the risk of ECSM. Whether as FPC or SPEC, endometrial cancer is demonstrated to be a significant independent risk factor for ECSS and OS.

VISTA: A promising target for overcoming immune evasion in gynecologic cancers.

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but has shown limited efficacy in gynecologic cancers. VISTA (V-domain Ig suppressor of T-cell activation), a member of the B7 family, is emerging as another checkpoint that regulates the anti-tumor immune responses within the tumor microenvironment. This paper reviews the structure, expression, and mechanism of action of VISTA. Furthermore, it highlights recent advances in VISTA-blocking therapies and their potential in improving outcomes for patients with gynecologic cancers. By understanding the role of VISTA in mediating the immune evasion of gynecologic tumors, we can develop more effective combinatory treatment strategies that could overcome resistance to current ICB therapies.

Identification of CXCL13 as a Promising Biomarker for Immune Checkpoint Blockade Therapy and PARP Inhibitor Therapy in Ovarian Cancer.

Ovarian cancer has poor response rates to immune checkpoint blockade (ICB) therapy, despite the use of genomic sequencing to identify molecular targets. Homologous recombination deficiency (HRD) is a conventional indicator of genomic instability (GI) and has been used as a marker for targeted therapies. Indicators reflecting HRD status have shown potential in predicting the efficacy of ICB treatment. Public databases, including TCGA, ICGC, and GEO, were used to obtain data. HRD scores, neoantigen load, and TMB were obtained from the TCGA cohort. Candidate biomarkers were validated in multiple databases, such as the Imvigor210 immunotherapy cohort and the open-source single-cell sequencing database. Immunohistochemistry was performed to further validate the results in independent cohorts. CXCL10, CXCL11, and CXCL13 were found to be significantly upregulated in HRD tumors and exhibited prognostic value. A comprehensive analysis of the tumor immune microenvironment (TIME) revealed that CXCL13 expression positively correlated with neoantigen load and immune cell infiltration. In addition, single-cell sequencing data and clinical trial results supported the utility of CXCL13 as a biomarker for ICB therapy. Not only does CXCL13 serve as a biomarker reflecting HRD status, but it also introduces a potentially novel perspective on prognostic biomarkers for ICB in ovarian cancer.

Cancer-associated fibroblast-secreted FGF7 as an ovarian cancer progression promoter.

BACKGROUND: Ovarian cancer (OC) is distinguished by its aggressive nature and the limited efficacy of current treatment strategies. Recent studies have emphasized the significant role of cancer-associated fibroblasts (CAFs) in OC development and progression. METHODS: Employing sophisticated machine learning techniques on bulk transcriptomic datasets, we identified fibroblast growth factor 7 (FGF7), derived from CAFs, as a potential oncogenic factor. We investigated the relationship between FGF7 expression and various clinical parameters. A series of in vitro experiments were undertaken to evaluate the effect of CAFs-derived FGF7 on OC cell activities, such as proliferation, migration, and invasion. Single-cell transcriptomic analysis was also conducted to elucidate the interaction between FGF7 and its receptor. Detailed mechanistic investigations sought to clarify the pathways through which FGF7 fosters OC progression. RESULTS: Our findings indicate that higher FGF7 levels correlate with advanced tumor stages, increased vascular invasion, and poorer prognosis. CAFs-derived FGF7 significantly enhanced OC cell proliferation, migration, and invasion. Single-cell analysis and in vitro studies revealed that CAFs-derived FGF7 inhibits the ubiquitination and degradation of hypoxia-inducible factor 1 alpha (HIF-1α) via FGFR2 interaction. Activation of the FGF7/HIF-1α pathway resulted in the upregulation of mesenchymal markers and downregulation of epithelial markers. Importantly, in vivo treatment with neutralizing antibodies targeting CAFs-derived FGF7 substantially reduced tumor growth. CONCLUSION: Neutralizing FGF7 in the medium or inhibiting HIF-1α signaling reversed the effects of FGF7-mediated EMT, emphasizing the dependence of FGF7-mediated EMT on HIF-1α activation. These findings suggest that targeting the FGF7/HIF-1α/EMT axis may offer new therapeutic opportunities to intervene in OC progression.

In Situ Nanofiber Formation Blocks AXL and GAS6 Binding to Suppress Ovarian Cancer Development.

Anexelekto (AXL) is an attractive molecular target for ovarian cancer therapy because of its important role in ovarian cancer initiation and progression. To date, several AXL inhibitors have entered clinical trials for the treatment of ovarian cancer. However, the disadvantages of low AXL affinity and severe off-target toxicity of these inhibitors limit their further clinical applications. Herein, by rational design of a nonapeptide derivative Nap-Phe-Phe-Glu-Ile-Arg-Leu-Arg-Phe-Lys (Nap-IR), a strategy of in situ nanofiber formation is proposed to suppress ovarian cancer growth. After administration, Nap-IR specifically targets overexpressed AXL on ovarian cancer cell membranes and undergoes a receptor-instructed nanoparticle-to-nanofiber transition. In vivo and in vitro experiments demonstrate that in situ formed Nap-IR nanofibers efficiently induce apoptosis of ovarian cancer cells by blocking AXL activation and disrupting subsequent downstream signaling events. Remarkably, Nap-IR can synergistically enhance the anticancer effect of cisplatin against HO8910 ovarian tumors. It is anticipated that the Nap-IR can be applied in clinical ovarian cancer therapy in the near future.

Comprehensive single-cell analysis reveals heterogeneity of fibroblast subpopulations in ovarian cancer tissue microenvironment.

Background: Ovarian cancer, as a highly malignant tumor, features the critical involvement of tumor-associated fibroblasts in the ovarian cancer tissue microenvironment. However, due to the apparent heterogeneity within fibroblast subpopulations, the specific functions of these subpopulations in the ovarian cancer tissue microenvironment remain insufficiently elucidated. Methods: In this study, we integrated single-cell sequencing data from 32 ovarian cancer samples derived from four distinct cohorts and 3226 bulk RNA-seq data from GEO and TCGA-OV cohorts. Utilizing computational frameworks such as Seurat, Monocle 2, Cellchat, and others, we analyzed the characteristics of the ovarian cancer tissue microenvironment, focusing particularly on fibroblast subpopulations and their differentiation trajectories. Employing the CIBERSORTX computational framework, we assessed various cellular components within the ovarian cancer tissue microenvironment and evaluated their associations with ovarian cancer prognosis. Additionally, we conducted Mendelian randomization analysis based on cis-eQTL to investigate causal relationships between gene expression and ovarian cancer. Results: Through integrative analysis, we identified 13 major cell types present in ovarian cancer tissues, including CD8+ T cells, malignant cells, and fibroblasts. Analysis of the tumor microenvironment (TME) cell proportions revealed a significant increase in the proportion of CD8+ T cells and CD4+ T cells in tumor tissues compared to normal tissues, while fibroblasts predominated in normal tissues. Further subgroup analysis of fibroblasts identified seven subgroups, with the MMP11+Fib subgroup showing the highest activity in the TGFß signaling pathway. Single-cell analysis suggested that oxidative phosphorylation could be a key pathway driving fibroblast differentiation, and the ATRNL1+KCN + Fib subgroup exhibited chromosomal copy number variations. Prognostic analysis using a large sample size indicated that high infiltration of MMP11+ fibroblasts was associated with poor prognosis in ovarian cancer. SMR analysis identified 132 fibroblast differentiation-related genes, which were linked to pathways such as platinum drug resistance. Conclusions: In the context of ovarian cancer, fibroblasts expressing MMP11 emerge as the primary drivers of the TGF-beta signaling pathway. Their presence correlates with an increased risk of adverse ovarian prognoses. Additionally, the genetic regulation governing the differentiation of fibroblasts associated with ovarian cancer correlates with the emergence of drug resistance.

Significant Prognostic Factor at Age Cut-off of 73 Years for Advanced Ovarian Serous Cystadenocarcinoma Patients: Insights from Real-World Study.

Objective: The objective of this research was to determine the age cut-off for worse prognosis and investigate age-related differentially expressed genes (DEGs) in patients with advanced ovarian serous cystadenocarcinoma (AOSC). Methods: In this research, we included a cohort of 20,846 patients diagnosed with AOSC, along with RNA-seq data from 374 patients in publicly available databases. Then we used the X-tile software to determine the age cut-off and stratified the patients into young and old groups. We utilized propensity score matching (PSM) to balance baseline between the young and old groups. Furthermore, we conducted an enrichment analysis of DEGs between the two age groups using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology (GO) to identify dysregulated pathways. To evaluate the potential prognostic value of the DEGs, we performed survival analysis, such as Kaplan-Meier analysis and Log rank test. Results: We stratified the patients into young group (n=16,336) and old group (n=4510) based on the cut-off age of 73 years by X-tile software. Age over 73 years was identified as an independent risk factor for overall survival (OS) and cancer-specific survival (CSS). Next, we identified 436 DEGs and found that the neurotrophin signaling pathway and translation factor activity were associated with prognosis outcomes. Among the top 10 hub genes (RELA, NFKBIA, TRAF6, IRAK2, TAB3, AKT1, TBP, EIF2S2, MAPK10, and SUPT3H), RELA, TAB3, AKT1, TBP, and SUPT3H were found to be significantly associated with poor prognosis in old patients with AOSC. Conclusion: Our study determined 73 years as the cutoff value for age in patients with AOSC. RELA, TAB3, AKT1, TBP, and SUPT3H were identified as age-related DEGs that could contribute to the poor prognosis of older patients with AOSC.

Exosomes derived from ovarian cancer cells regulate proliferation and migration of cancer-associated fibroblasts.

Cancer-associated fibroblast (CAF) is an essential risk factor for ovarian cancer. Exosomes can mediate cellular communication in the tumour microenvironment, but the interaction of tumour cell exosomes with CAF is less studied in Ovarian cancer. This study identified H19/miR-29c-3p/LOXL2-COL1A1 as a ceRNA regulatory network involved in regulating tumour matrix-associated signaling pathways associated with CAF. Cellular assays demonstrated that exosomes from ovarian cancer cell line SKOV3 significantly promoted the proliferation and migration of CAF. The results of mixed transplantation tumour experiments in nude mice showed that exosomes of SKOV3 significantly promoted tumour growth. Ovarian cancer tumour-derived exosomes can regulate CAF proliferation and migration through H19/miR-29c-3p/LOXL2-COL1A1. This study reveals the regulatory role of tumour exosomes on CAF, which may provide a theoretical basis for the development of therapeutic regimens targeting fibroblasts in ovarian cancer.

Exploration of cuprotosis-related genes for predicting prognosis and immunological characteristics in acute myeloid leukaemia based on genome and transcriptome.

BACKGROUND: Acute myeloid leukemia (AML) is a common hematologic malignancy with a generally unfavorable prognosis. Cuprotosis as a new form of programmed cell death has been shown to play an important role in tumorigenesis and progression; However, the relationship between cuprotosis and the prognosis of AML patients remains unclear. METHODS: Transcriptomic and genomics data, along with clinical information, were obtained from the TCGA and GEO databases. Especially, unsupervised clustering and machining learning were used to identify molecular subtypes and cuprotosis-related risk scores respectively. Kaplan-Meier analysis, univariate and multivariate Cox regression, and Receiver Operator Characteristic curve (ROC) were performed to assess the prognosis based on cuprotosis-related genes (CRGs). Moreover, multiple algorithms were used to evaluate immunological heterogeneity among patients with different risk scores. For in vitro analysis, the expression of genes involved in CRGs was detected by Quantitative Reverse Transcription Polymerase (qRT-PCR) in AML patients. RESULTS: Transcriptomic and genome data indicated the immense heterogeneity in the CRGs landscape of normal and tumor samples. Cuprotosis subtype A and cuprotosis regulatory subtype B in the genomics map and biological characteristics were significantly different from the other groups. Furthermore, these two subtypes had lower risk scores and longer survival times compared to other groups. Cox analyses indicated that risk score was an independent prognostic factor for AML patients. In addition, our risk score could be an indicator of survival outcomes in immunotherapy datasets. CONCLUSIONS: Our study demonstrates the potential of CRGs in guiding the prognosis, treatment, and immunological characteristics of AML patients.

STMHCpan, an accurate Star-Transformer-based extensible framework for predicting MHC I allele binding peptides.

Peptide-major histocompatibility complex I (MHC I) binding affinity prediction is crucial for vaccine development, but existing methods face limitations such as small datasets, model overfitting due to excessive parameters and suboptimal performance. Here, we present STMHCPan (STAR-MHCPan), an open-source package based on the Star-Transformer model, for MHC I binding peptide prediction. Our approach introduces an attention mechanism to improve the deep learning network architecture and performance in antigen prediction. Compared with classical deep learning algorithms, STMHCPan exhibits improved performance with fewer parameters in receptor affinity training. Furthermore, STMHCPan outperforms existing ligand benchmark datasets identified by mass spectrometry. It can also handle peptides of arbitrary length and is highly scalable for predicting T-cell responses. Our software is freely available for use, training and extension through Github (https://github.com/Luckysoutheast/STMHCPan.git).

Association of smoking, alcohol, and coffee consumption with the risk of ovarian cancer and prognosis: a mendelian randomization study.

OBJECTIVE: Currently, the association between smoking, alcohol, and coffee intake and the risk of ovarian cancer (OC) remains conflicting. In this study, we used a two-sample mendelian randomization (MR) method to evaluate the association of smoking, drinking and coffee consumption with the risk of OC and prognosis. METHODS: Five risk factors related to lifestyles (cigarettes per day, smoking initiation, smoking cessation, alcohol consumption and coffee consumption) were chosen from the Genome-Wide Association Study, and 28, 105, 10, 36 and 36 single-nucleotide polymorphisms (SNPs) were obtained as instrumental variables (IVs). Outcome variables were achieved from the Ovarian Cancer Association Consortium. Inverse-variance-weighted method was mainly used to compute odds ratios (OR) and 95% confidence intervals (Cl). RESULTS: The two-sample MR analysis supported the causal association of genetically predicted smoking initiation (OR: 1.15 per SD, 95%CI: 1.02-1.29, P = 0.027) and coffee consumption (OR: 1.40 per 50% increase, 95%CI: 1.02-1.93, P = 0.040) with the risk of OC, but not cigarettes per day, smoking cessation, and alcohol consumption. Subgroup analysis based on histological subtypes revealed a positive genetical predictive association between coffee consumption and endometrioid OC (OR: 3.01, 95%CI: 1.50-6.04, P = 0.002). Several smoking initiation-related SNPs (rs7585579, rs7929518, rs2378662, rs10001365, rs11078713, rs7929518, and rs62098013), and coffee consumption-related SNPs (rs4410790, and rs1057868) were all associated with overall survival and cancer-specific survival in OC. CONCLUSION: Our findings provide the evidence for a favorable causal association of genetically predicted smoking initiation and coffee consumption with OC risk, and coffee consumption is linked to a greater risk of endometrioid OC.

Engineered exosome-mediated messenger RNA and single-chain variable fragment delivery for human chimeric antigen receptor T-cell engineering.

BACKGROUND AIMS: Most current chimeric antigen receptor (CAR) T cells are generated by viral transduction, which induces persistent expression of CARs and may cause serious undesirable effects. Messenger RNA (mRNA)-based approaches in manufacturing CAR T cells are being developed to overcome these challenges. However, the most common method of delivering mRNA to T cells is electroporation, which can be toxic to cells. METHODS: The authors designed and engineered an exosome delivery platform using the bacteriophage MS2 system in combination with the highly expressed protein lysosome-associated membrane protein 2 isoform B on exosomes. RESULTS: The authors' delivery platform achieved specific loading and delivery of mRNA into target cells and achieved expression of specific proteins, and anti-CD3/CD28 single-chain variable fragments (scFvs) expressed outside the exosomal membrane effectively activated primary T cells in a similar way to commercial magnetic beads. CONCLUSIONS: The delivery of CAR mRNA and anti-CD3/CD28 scFvs via designed exosomes can be used for ex vivo production of CAR T cells with cancer cell killing capacity. The authors' results indicate the potential applications of the engineered exosome delivery platform for direct conversion of primary T cells to CAR T cells while providing a novel strategy for producing CAR T cells in vivo.

The role of hypoxia-related genes in TACE-refractory hepatocellular carcinoma: Exploration of prognosis, immunological characteristics and drug resistance based on onco-multi-OMICS approach.

Transcatheter arterial chemoembolization (TACE) is an effective treatment for hepatocellular carcinoma (HCC). During TACE, chemotherapeutic agents are locally infused into the tumor and simultaneously cause hypoxia in tumor cells. Importantly, the poor effect of TACE in some HCC patients has been shown to be related to dysregulated expression of hypoxia-related genes (HRGs). Therefore, we identified 33 HRGs associated with TACE (HRGTs) by differential analysis and characterized the mutational landscape of HRGTs. Among 586 HCC patients, two molecular subtypes reflecting survival status were identified by consistent clustering analysis based on 24 prognosis-associated HRGs. Comparing the transcriptomic difference of the above molecular subtypes, three molecular subtypes that could reflect changes in the immune microenvironment were then identified. Ultimately, four HRGTs (CTSO, MMP1, SPP1, TPX2) were identified based on machine learning approachs. Importantly, risk assessment can be performed for each patient by these genes. Based on the parameters of the risk model, we determined that high-risk patients have a more active immune microenvironment, indicating "hot tumor" status. And the Tumor Immune Dysfunction and Exclusion (TIDE), the Cancer Immunome Atlas (TCIA), and Genome of Drug Sensitivity in Cancer (GDSC) databases further demonstrated that high-risk patients have a positive response to immunotherapy and have lower IC50 values for drugs targeting cell cycle, PI3K/mTOR, WNT, and RTK related signaling pathways. Finally, single-cell level analysis revealed significant overexpression of CTSO, MMP1, SPP1, and TPX2 in malignant cell after PD-L1/CTLA-4 treatment. In conclusion, Onco-Multi-OMICS analysis showed that HRGs are potential biomarkers for patients with refractory TACE, and it provides a novel immunological perspective for developing personalized therapies.

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study.

Purpose: Mucinous ovarian cancer (MOC) is a rare histological type of EOC. In order to guide the clinical diagnosis and management of MOC patients, we constructed and verified a nomogram for the estimation of overall survival in patients with MOC. Patients and Methods: We collected 494 patients with MOC diagnosed from 2010 to 2015 in SEER database, and the following main inclusion criteria were used: (1) patients whose MOC was confirmed by pathology; (2) patients without a history of primary other cancer. Subsequently, we performed randomized grouping (6:4) and Cox hazard regression analysis in the training group. Subsequently, the nomogram was established. A variety of indicators were used to validate the prognosis value of nomogram, including the C-index, area under the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier analysis was used to compare the survival results among different risk subgroups. Results: Cox hazard regression analysis revealed that age, grade, FIGO stage and log odds of positive lymph nodes stage were independent risk factors for patients with MOC. In the training group, the C-index of the nomogram was 0.827 (95% CI: 0.791-0.863) and the areas under the curve (AUC) predicting the 1-, 3- and 5-year survival rate were 0.853 (95% CI: 0.791-0.915), 0.886 (95% CI: 0.852-0.920) and 0.815 (95% CI: 0.766-0.864), respectively. The calibration curve revealed that the nomogram of the 1-, 3- and 5-year survival rate was consistent with the actual fact. Patients with high risk had a poorer prognosis than those with low risk (P < 0.001). DCA revealed that the nomogram had the best clinical value than other classical prognostic markers. Similarly, nomogram had excellent prognostic ability in the testing group. Conclusion: The nomogram was constructed to predict overall survival in patients with MOC, which had the significance for clinical evaluation.

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer.

Cancer-associated fibroblasts (CAFs) are a major contributor to tumor stromal crosstalk in the tumor microenvironment (TME) and boost tumor progression by promoting angiogenesis and lymphangiogenesis. This study aimed to identify prognostic genes associated with CAFs that lead to high morbidity and mortality in ovarian cancer (OC) patients. We performed bioinformatics analysis in 16 multicenter studies (2,742 patients) and identified CAF-associated hub genes using the weighted gene co-expression network analysis (WGCNA). A machine learning methodology was used to identify COL16A1, COL5A2, GREM1, LUM, SRPX, and TIMP3 and construct a prognostic signature. Subsequently, a series of bioinformatics algorithms indicated risk stratification based on the above signature, suggesting that high-risk patients have a worse prognosis, weaker immune response, and lower tumor mutational burden (TMB) status but may be more sensitive to routine chemotherapeutic agents. Finally, we characterized prognostic markers using cell lines, immunohistochemistry, and single-cell sequencing. In conclusion, these results suggest that the CAF-related signature may be a novel pretreatment guide for anti-CAFs, and prognostic markers in CAFs may be potential therapeutic targets to inhibit OC progression.

Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma.

Few breakthroughs have been achieved in the treatment of lower-grade glioma (LGG) in recent decades. Apart from the conventional pathological and histological classifications, subtypes based on immunogenomics would provide reference for individualized treatment and prognosis prediction. Our study identified four immunotypes of lower-grade glioma (clusters A, B, C, and D) by bioinformatics methods in TCGA-LGG and two CGGA datasets. Cluster A was an "immune-cold" phenotype with the lowest immune infiltration and longest survival expectation, whereas cluster D was an "immune-rich" subtype with the highest immune infiltration and poor survival expectation. The expression of immune checkpoints increased along with immune infiltration degrees among the clusters. It was notable that immune clusters correlated with a variety of clinical and immunogenomic factors such as age, WHO grades, IDH1/2 mutation, PTEN, EGFR, ATRX, and TP53 status. In addition, LGGs in cluster D were sensitive to cisplatin, gemcitabine, and immune checkpoint PD-1 inhibitors. RTK-RAS and TP53 pathways were affected in cluster D. Functional pathways such as cytokine-cytokine receptor interaction, antigen processing and presentation, cell adhesion molecules (CAMs), and ECM-receptor interaction were also enriched in cluster D. Hub genes were selected by the Matthews correlation coefficient (MCC) algorithm in the blue module of a gene co-expression network. Our studies might provide an immunogenomics subtyping reference for immunotherapy in LGG.

Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.

Purpose: The hypoxic microenvironment is involved in the tumorigenesis of ovarian cancer (OC). Therefore, we aim to develop a non-invasive radiogenomics approach to identify a hypoxia pattern with potential application in patient prognostication. Methods: Specific hypoxia-related genes (sHRGs) were identified based on RNA-seq of OC cell lines cultured with different oxygen conditions. Meanwhile, multiple hypoxia-related subtypes were identified by unsupervised consensus analysis and LASSO-Cox regression analysis. Subsequently, diversified bioinformatics algorithms were used to explore the immune microenvironment, prognosis, biological pathway alteration, and drug sensitivity among different subtypes. Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms. Results: One hundred forty sHRGs and three types of hypoxia-related subtypes were identified. Among them, hypoxia-cluster-B, gene-cluster-B, and high-risk subtypes had poor survival outcomes. The subtypes were closely related to each other, and hypoxia-cluster-B and gene-cluster-B had higher hypoxia risk scores. Notably, the low-risk subtype had an active immune microenvironment and may benefit from immunotherapy. Finally, a four-feature radiogenomics model was constructed to reveal hypoxia risk status, and the model achieved area under the curve (AUC) values of 0.900 and 0.703 for the training and testing cohorts, respectively. Conclusion: As a non-invasive approach, computed tomography-based radiogenomics biomarkers may enable the pretreatment prediction of the hypoxia pattern, prognosis, therapeutic effect, and immune microenvironment in patients with OC.