Identification of a Novel Tumor Microenvironment Prognostic Signature for Advanced-Stage Serous Ovarian Cancer

The role of Phafin proteins in cell signaling pathways and diseases

Membrane-associated proteins are important membrane readers that mediate and facilitate the signaling and trafficking pathways in eukaryotic membrane-bound compartments. The protein members in the Phafin family are membrane readers containing two phosphoinositide recognition domains: the Pleckstrin Homology domain and the FYVE (Fab1, YOTB, Vac1, and early endosome antigen 1) domain. Phafin proteins, categorized into two subfamilies, Phafin1 and Phafin2, associate with cellular membranes through interactions involving membrane-embedded phosphoinositides and phosphoinositide-binding domains. These membrane-associated Phafin proteins play pivotal roles by recruiting binding partners and forming complexes, which contribute significantly to apoptotic, autophagic, and macropinocytotic pathways. Elevated expression levels of Phafin1 and Phafin2 are observed in various cancers. A recent study highlights a significant increase in Phafin1 protein levels in the lungs of idiopathic pulmonary fibrosis patients compared to normal subjects, suggesting a crucial role for Phafin1 in the pathogenesis of pulmonary fibrosis. Additionally, phosphatidylinositol-3-phosphate-binding 2 (Pib2), a close relative of the Phafin1 protein, functions as an amino acid sensor activating the TOCR1 pathway in yeasts. This review focuses on delineating the involvement of Phafin proteins in cellular signaling and their implications in diseases and briefly discusses the latest research findings concerning Pib2.

Targeted plasma proteomic analysis uncovers a high-performance biomarker panel for early diagnosis of gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by high morbidity, high mortality and low early diagnosis rate. Early diagnosis plays a crucial role in radically treating GC. The aim of this study was to identify plasma biomarkers for GC and early GC diagnosis.

METHODS

We quantified 369 protein levels with plasma samples from discovery cohort (n = 88) and validation cohort (n = 50) via high-throughput proximity extension assay (PEA) utilizing the Olink-Explore-384-Cardiometabolic panel. The multi-protein signatures were derived from LASSO and Ridge regression models.

RESULTS

In the discovery cohort, 13 proteins (GDF15, ITIH3, BOC, DPP7, EGFR, AMY2A, CCDC80, CD163, GPNMB, LTBP2, CTSZ, CCL18 and NECTIN2) were identified to distinguish GC (Stage I-IV) and early GC (HGIN-I) groups from control group with AUC of 0.994 and AUC of 0.998, severally. The validation cohort yielded AUC of 0.930 and AUC of 0.818 for GC and early GC, respectively.

CONCLUSIONS

This study identified a multi-protein signature with the potential to benefit clinical GC diagnosis, especially for Asian and early GC patients, which may contribute to the development of a less-invasive, convenient, and efficient early screening tool, promoting early diagnosis and treatment of GC and ultimately improving patient survival.

Germline genetic regulation of the colorectal tumor immune microenvironment

OBJECTIVE

To evaluate the contribution of germline genetics to regulating the briskness and diversity of T cell responses in CRC, we conducted a genome-wide association study to examine the associations between germline genetic variation and quantitative measures of T cell landscapes in 2,876 colorectal tumors from participants in the Molecular Epidemiology of Colorectal Cancer Study (MECC).

METHODS

Germline DNA samples were genotyped and imputed using genome-wide arrays. Tumor DNA samples were extracted from paraffin blocks, and T cell receptor clonality and abundance were quantified by immunoSEQ (Adaptive Biotechnologies, Seattle, WA). Tumor infiltrating lymphocytes per high powered field (TILs/hpf) were scored by a gastrointestinal pathologist. Regression models were used to evaluate the associations between each variant and the three T-cell features, adjusting for sex, age, genotyping platform, and global ancestry. Three independent datasets were used for replication.

RESULTS

We identified a SNP (rs4918567) near RBM20 associated with clonality at a genome-wide significant threshold of 5 × 10- 8, with a consistent direction of association in both discovery and replication datasets. Expression quantitative trait (eQTL) analyses and in silico functional annotation for these loci provided insights into potential functional roles, including a statistically significant eQTL between the T allele at rs4918567 and higher expression of ADRA2A (P = 0.012) in healthy colon mucosa.

CONCLUSIONS

Our study suggests that germline genetic variation is associated with the quantity and diversity of adaptive immune responses in CRC. Further studies are warranted to replicate these findings in additional samples and to investigate functional genomic mechanisms.

CPXM1 correlates to poor prognosis and immune cell infiltration in gastric cancer

Background

Gastric cancer (GC) is the fourth most common cause of cancer-related death and the fifth most frequent malignant cancer, especially advanced GC. Carboxypeptidase X member 1 (CPXM1) is an epigenetic factor involved in many physiological processes, including osteoclast differentiation and adipogenesis. Several studies have shown the association of CPXM1 with multiple tumors; however, the mechanism of CPXM1 involvement in the progression of GC is yet to be characterized.

Method

CPXM1 expression data were obtained from the Tumor Immune Estimation Resource. The Cancer Genome Atlas and the Gene Expression Omnibus databases were used to obtain patient-matched clinicopathological information, and the Kaplan-Meier plot database was utilized for the prognosis analysis of GC patients. The Catalog of Somatic Mutations in Cancer and cBioportal databases were adopted to study CPXM1 mutations in tumors. Next, we utilized the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis for mechanism research. Furthermore, we performed tumor microenvironment and immune infiltration analysis based on CPXM1. Finally, we predicted sensitivity to several targeted drugs in GC patients based on CPXM1.CPXM1 is upregulated in GC and is correlated with poor prognosis, gender, and tumor stage in GC patients. Gene enrichment analysis suggested that CPXM1 may regulate the occurrence and progression of GC via the PI3K-AKT and TGF-β pathway. Moreover, CPXM1 expression results in an increase in the proportion of immune and stromal cells. Additionally, the proportion of plasma cells was inversely related to the expression of CPXM1, whereas macrophage M2 expression was proportionate to CPXM1 expression. Finally, six small-molecule drugs that showed notable variations in IC50 between two groups were screened.

Conclusion

These results suggested that CPXM1 regulates the progression of GC and may represent a novel target for the detection and treatment of GC.

Local administration of liposomal-based Plekhf1 gene therapy attenuates pulmonary fibrosis by modulating macrophage polarization

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease with limited therapeutic options. Macrophages, particularly alternatively activated macrophages (M2), have been recognized to contribute to the pathogenesis of pulmonary fibrosis. Therefore, targeting macrophages might be a viable therapeutic strategy for IPF. Herein, we report a potential nanomedicine-based gene therapy for IPF by modulating macrophage M2 activation. In this study, we illustrated that the levels of pleckstrin homology and FYVE domain containing 1 (Plekhf1) were increased in the lungs originating from IPF patients and PF mice. Further functionality studies identified the pivotal role of Plekhf1 in macrophage M2 activation. Mechanistically, Plekhf1 was upregulated by IL-4/IL-13 stimulation, after which Plekhf1 enhanced PI3K/Akt signaling to promote the macrophage M2 program and exacerbate pulmonary fibrosis. Therefore, intratracheal administration of Plekhf1 siRNA-loaded liposomes could effectively suppress the expression of Plekhf1 in the lungs and notably protect mice against BLM-induced lung injury and fibrosis, concomitant with a significant reduction in M2 macrophage accumulation in the lungs. In conclusion, Plekhf1 may play a crucial role in the pathogenesis of pulmonary fibrosis, and Plekhf1 siRNA-loaded liposomes might be a promising therapeutic approach against pulmonary fibrosis.

The Value of CXCL1, CXCL2, CXCL3, and CXCL8 as Potential Prognosis Markers in Cervical Cancer: Evidence of E6/E7 from HPV16 and 18 in Chemokines Regulation

Cervical cancer (CC) is a serious global health issue, and it is well-known that HPV infection is the main etiological factor that triggers carcinogenesis. In cancer, chemokine ligands and receptors are involved in tumor cell growth, metastasis, leukocyte infiltration, and angiogenesis; however, information on the role played by E6/E7 of HPV16/18 in the modulation of chemokines is very limited. Therefore, this study aimed to determine whether chemokines are differentially expressed in CC-derived cell lines; if E6/E7 oncoproteins from HPV16 and 18 are capable of mediating chemokine expression, what is the expression profile of chemokines in tissues derived from CC and what is their impact on the overall survival of patients with this pathology? For this purpose, RNA sequencing and real-time PCR were performed on SiHa, HeLa, and C33A tumorigenic cell lines, on the non-tumorigenic HaCaT cells, and the E6/E7 HPV-transduced HaCaT cell models. Furthermore, chemokine expression and survival analysis were executed on 304 CC and 22 normal tissue samples from The Cancer Genome Atlas (TCGA) repository. The results demonstrate that CXCL1, CXCL2, CXCL3, and CXCL8 are regulated by E6/E7 of HPV16 and 18, are overexpressed in CC biopsies, and that their higher expression is related to a worse prognostic survival.

Identification of STEAP3-based molecular subtype and risk model in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis.

METHODS

STEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC).

RESULTS

STEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients.

CONCLUSION

In summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy.

Chicken Hypothalamic and Ovarian DNA Methylome Alteration in Response to Forced Molting

Epigenetic modifications play an important role in regulating animal adaptation to external stress. To explore how DNA methylation regulates the expression levels of related genes during forced molting (FM) of laying hens, the hypothalamus and ovary tissues were analyzed at five periods using Whole-Genome Bisulfite Sequencing. The results show that methylation levels fluctuated differently in the exon, intron, 5′UTR, 3′UTR, promoter, and intergenic regions of the genome during FM. In addition, 16 differentially methylated genes (DMGs) regulating cell aging, immunity, and development were identified in the two reversible processes of starvation and redevelopment during FM. Comparing DMGs with differentially expressed genes (DEGs) obtained in the same periods, five hypermethylated DMGs (DSTYK, NKTR, SMOC1, SCAMP3, and ATOH8) that inhibited the expression of DEGs were found. Therefore, DMGs epigenetically modify the DEGs during the FM process of chickens, leading to the rapid closure and restart of their reproductive function and a re-increase in the egg-laying rate. Therefore, this study further confirmed that epigenetic modifications could regulate gene expression during FM and provides theoretical support for the subsequent optimization of FM technology.

Role of CPXM1 in Impaired Glucose Metabolism and Ovarian Dysfunction in Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS), a common female endocrinopathy associated with both reproductive and metabolic disorders, has an unclear etiology and unsatisfactory management methods. Carboxypeptidase X, M14 family member 1 (CPXM1) is a protein involved in follicular atresia, insulin production, and adipose tissue production, though its role in PCOS is not fully understood. We used a 60% high-fat diet (HFD) plus dehydroepiandrosterone (DHEA)-induced PCOS mouse model to determine the role of CPXM1 in abnormal glucose metabolism and ovarian dysfunction in PCOS. We found that serum CPXM1 concentrations were higher in PCOS mice and positively correlated with increased levels of serum testosterone and insulin. In both ovarian and adipose tissues of PCOS mice, CPXM1 mRNA and protein levels were significantly increased but GLUT4 levels were significantly decreased. Immunohistochemistry (IHC) staining of the ovary showed increased CPXM1 expression in PCOS. In addition, the protein expression of phosphorylated protein kinase B (p-Akt) was also significantly decreased in PCOS mice. Furthermore, mRNA levels of inflammatory markers such as TNF-α, IL-6, IFN-α, and IFN-γ were increased in ovarian and adipose tissues of PCOS mice. However, IRS-1, IRS-2, and INSR levels were significantly decreased. Our results indicated for the first time that abnormally high expression of CPXM1, increased adiposity, impaired glucose tolerance, and chronic low-grade inflammation may act together in a vicious cycle in the pathophysiology of PCOS. Our research suggests the possibility of CPXM1 as a potential therapeutic target for the treatment of PCOS.

A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA and ATAC sequencing

As part of the Human Cell Atlas Initiative, our goal is to generate single-cell transcriptomics (single-cell RNA sequencing [scRNA-seq], 86,708 cells) and regulatory (single-cell assay on transposase accessible chromatin sequencing [scATAC-seq], 59,830 cells) profiles of the normal postmenopausal ovary and fallopian tube (FT). The FT contains 11 major cell types, and the ovary contains 6. The dominating cell type in the FT and ovary is the stromal cell, which expresses aging-associated genes. FT epithelial cells express multiple ovarian cancer risk-associated genes (CCDC170, RND3, TACC2, STK33, and ADGB) and show active communication between fimbrial epithelial cells and ovarian stromal cells. Integrated single-cell transcriptomics and chromatin accessibility data show that the regulatory landscape of the fimbriae is different from other anatomic regions. Cell types with similar gene expression in the FT display transcriptional profiles. These findings allow us to disentangle the cellular makeup of the postmenopausal FT and ovary, advancing our knowledge of gynecologic diseases in menopause.

Identification of necroptosis subtypes and development of necroptosis-related risk score model for in ovarian cancer

Background: ith the ongoing development of targeted therapy, non-apoptotic cell death, including necroptosis, has become a popular topic in the field of prevention and treatment. The purpose of this study was to explore the effect of necroptosis-related genes (NRGs) on the classification of ovarian cancer (OV) subtypes and to develop a necroptosis-related risk score (NRRS) classification system. Methods: 74 NRGs were obtained from the published studies, and univariate COX regression analysis was carried out between them and OV survival. Consensus clustering analysis was performed on OV samples according to the expression of NRGs related to prognosis. Furthermore, the NRRS model was developed by combining Weighted Gene Co-Expression Network Analysis (WGCNA) with least absolute shrinkage and selection operator (Lasso)-penalized Cox regression and multivariate Cox regression analysis. And the decision tree model was constructed based on the principle of random forest screening factors principle. Results: According to the post-related NRGs, OV was divided into two necroptosis subtypes. Compared with Cluster 1 (C1), the overall survival (OS) of Cluster 2 (C2) was significantly shorter, stromal score and immune score, the infiltration level of tumor associated immune cells and the expression of 20 immune checkpoints were significantly higher. WGCNA identified the blue module most related to necroptosis subtype, and 12 genes in the module were used to construct NRRS. NRRS was an independent prognostic variable of OV. The OS of samples with lower NRRS was significantly longer, and tumor mutation burden and homologous recombination defect were more obvious. Conclusion: This study showed that necroptosis plays an important role in the classification, prognosis, immune infiltration and biological characteristics of OV subtypes. The evaluation of tumor necroptosis may provide a new perspective for OV treatment.

Computational construction of TME-related lncRNAs signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

Background

The tumor microenvironment (TME) is closely related to clear cell renal cell carcinoma (ccRCC) prognosis, and immunotherapy response. In current study, comprehensive bio‐informative analysis was adopted to construct a TME‐related lncRNA signature for immune checkpoint inhibitors (ICIs) and targeted drug responses in ccRCC patients.

Methods

The TME mRNAs were screened following the immune and stromal scores with the data from GSE15641, GSE29609, GSE36895, GSE46699, GSE53757, and The Cancer Genome Atlas (TCGA)‐kidney renal clear cell carcinoma (KIRC). And the TME‐related lncRNAs were recognized using correlation analysis. The TME‐related lncRNAs prognostic model was constructed using the training dataset. Kaplan–Meier analysis, principal‐component analysis, and time‐dependent receiver operating characteristic were used to evaluate the risk model. The immune cell infiltration in TME was evaluated using the single‐sample gene set enrichment analysis (ssGSEA), ESTIMATE, and microenvironment cell populations counter algorithm. The immunophenoscore (IPS) was used to assess the response to immunotherapy with the constructed model.

Results

In the current study, 364 TME‐related lncRNAs were selected based on the integrated bioinformatical analysis. Six TME‐related lncRNAs (LINC00460, LINC01094, AC008870.2, AC068792.1, and AC007637.1) were identified as the prognostic signature in the training dataset and subsequently verified in the testing and entire datasets. Patients in the high‐risk group exhibited poor overall survival and disease‐free survival than those in the low‐risk group. The 1‐, 3‐, and 5‐year areas under the curves of the prognostic signature in the entire dataset were 0.704, 0.683, and 0.750, respectively. The risk score independently predicted ccRCC survival based on univariate and multivariate Cox regression. GSEA analysis suggested that the high‐risk group was concentrated on immune‐related pathways. The high‐risk group were characterized by high immune cell infiltration, high TMB and somatic mutation counters, high IPS‐PD‐1 + CTLA4 scores, and immune checkpoints expression upregulation, reflecting the higher ICIs response. The half inhibitory concentrations of sunitinib, temsirolimus, and rapamycin were low in the high‐risk group.

Conclusion

The TME‐related lncRNAs signature constructed could reliably predict the prognosis and immunotherapy response and targeted ccRCC patients' therapy.

A Novel Prognostic Signature Associated With the Tumor Microenvironment in Kidney Renal Clear Cell Carcinoma

Background

The tumor microenvironment (TME) is a complex and evolving environment, and the tumor immune microenvironment in kidney renal clear cell carcinoma (KIRC) has a strong suppressive profile. This study investigates the potential prognostic role and value of genes of the tumor microenvironment in KIRC.

Methods

The transcriptome sequencing data of 530 cases and 39 cases of KIRC and the corresponding clinical prognosis information were downloaded from TCGA data and GEO data, respectively, and TME-related gene expression profiles were extracted. A prognostic signature was constructed and evaluated using univariate Cox regression analysis and LASSO regression analysis. Gene set enrichment analysis (GSEA) was used to obtain the biological process of gene enrichment in patients with high and low-risk groups.

Results

A prognostic signature consisting of eight TME-related genes (LRFN1, CSF1, UCN, TUBB2B, SERPINF1, ADAM8, ABCB4, CCL22) was constructed. Kaplan-Meier survival analysis yielded significantly lower survival times for patients in the high-risk group than in the low-risk group, and the AUC values for the ROC curves of this prognostic signature were essentially greater than 0.7, and univariate and multifactorial Cox regression analyses indicated that the risk score was independent risk factors for KIRC prognosis. GSEA analysis showed that immune-related biological processes were enriched in the high-risk group and that risk values were strongly associated with multiple immune cell scores and immune checkpoint-related genes (PDCD1, CTLA4).

Conclusions

The prognostic signature can accurately predict the prognosis of KIRC patients, which may provide new ideas for future precision immunotherapy of KIRC.

A Cancer Associated Fibroblasts-Related Six-Gene Panel for Anti-PD-1 Therapy in Melanoma Driven by Weighted Correlation Network Analysis and Supervised Machine Learning

Background

Melanoma is a highly aggressive skin cancer with a poor prognosis and mortality. Immune checkpoint blockade (ICB) therapy (e.g., anti-PD-1 therapy) has opened a new horizon in melanoma treatment, but some patients present a non-responsive state. Cancer-associated fibroblasts (CAFs) make up the majority of stromal cells in the tumor microenvironment (TME) and have an important impact on the response to immunotherapy. There is still a lack of identification of CAFs-related predictors for anti-PD-1 therapy, although the establishment of immunotherapy biomarkers is well underway. This study aims to explore the potential CAFs-related gene panel for predicting the response to anti-PD-1 therapy in melanoma patients and elucidating their potential effect on TME.

Methods

Three gene expression datasets from melanoma patients without anti-PD-1 treatment, in a total of 87 samples, were downloaded from Gene Expression Omnibus (GEO) as the discovery sets (GSE91061) and validation sets (GSE78220 and GSE122220). The CAFs-related module genes were identified from the discovery sets by weighted gene co-expression network analysis (WGCNA). Concurrently, we utilized differential gene analysis on the discovery set to obtain differentially expressed genes (DEGs). Then, CAFs-related key genes were screened with the intersection of CAFs-related module genes and DEGs, succeeded by supervised machine learning-based identification. As a consequence of expression analysis, gene set enrichment analysis, survival analysis, staging analysis, TME analysis, and correlation analysis, the multidimensional systematic characterizations of the key genes were uncovered. The diagnostic performance of the CAFs-related gene panel was assessed by receiver operating characteristic (ROC) curves in the validation sets. Eventually, the CAFs-related gene panel was verified by the expression from the single-cell analysis.

Results

The six-gene panel associated with CAFs were finally identified for predicting the response to anti-PD-1 therapy, including CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI. The multigene panel demonstrated excellent combined diagnostic performance with the area under the curve of ROC reaching 90.5 and 75.4% ~100% in the discovery and validation sets, respectively.

Conclusion

Confirmed by clinical treatment outcomes, the identified CAFs-related genes can be used as a promising biomarker panel for prediction to anti-PD-1 therapy response, which may serve as new immunotherapeutic targets to improve survival outcomes of melanoma patients.

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.