The Value of the Stemness Index in Ovarian Cancer Prognosis

Stemness-Relevant Gene Signature for Chemotherapeutic Response and Prognosis Prediction in Ovarian Cancer

Background: Ovarian cancer (OC) stands as the leading cause of cancer-related deaths among women, globally, owing to metastasis and acquired chemoresistance. Cancer stem cells (CSCs) are accountable for tumor initiation and exhibit resistance to chemotherapy and radiotherapy. Identifying stemness-related biomarkers that can aid in the stratification of risk and the response to chemotherapy for OC is feasible and critical. Methods: Gene expression and clinical data of patients with OC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Four thousand three hundred seventeen stemness-related genes (SRGs) were acquired from the StemChecker database. TCGA was used as the training dataset, while GSE30161 served as validation dataset. Univariate Cox regression analysis was used to identify overall survival (OS)-related SRGs, and multivariate Cox regression analysis and random survival forest analysis were used for generating stemness-relevant prognostic model. Kaplan-Meier plots were used to visualize survival functions. Receiver operating characteristic (ROC) curves were used to assess the prognostic predictive ability of SRG-based features. Associations between signature score, tumor immune phenotype, and response to chemotherapy were analyzed via TIMER 2.0 and oncoPredict R package, respectively. A cohort of Shanghai Cancer Center was employed to verify the predictive robustness of the signature with respect to chemotherapy response. Results: Seven SRGs (actin-binding Rho activating C-terminal like (ABRACL), growth factor receptor bound protein 7 (GRB7), Lin-28 homolog B (LIN28B), lipolysis stimulated lipoprotein receptor (LSR), neuromedin U (NMU), Solute Carrier Family 4 Member 11 (SLC4A11), and thymocyte selection associated family member 2 (THEMIS2)) were found to have excellent predictive potential for patient survival. Patients in the high stemness risk group presented a poorer prognosis (p  < 0.0001), and patients with lower stemness scores were more likely to benefit from chemotherapy. Several tumorigenesis pathways, such as mitotic spindle and glycolysis, were enriched in the high stemness risk group. Tumor with high-risk scores tended to be in a status of relatively high tumor infiltration of CD4+ T cells, neutrophils, and macrophages, while tumor with low-risk scores tended to be in a status of relatively high tumor infiltration of CD8+ T cells. Conclusions: The stemness-relevant prognostic gene signature has the potential to serve as a clinically helpful biomarker for guiding the management of OC patients.

Leveraging miRNA-mediated expression profiles to predict prognosis and identify distinct molecular subtypes in ovarian cancer: a multi-cohort study

Ovarian cancer (OV) remains the deadliest gynecological malignancy, with non-coding RNA-mediated transcriptomic deregulation significantly influencing its prognosis and heterogeneous progression. In this study, we prioritized miRNA-mediated gene expression profiles by identifying key negative correlations between miRNA-mRNA pairs. We developed a machine learning-based non-coding index (NCI), incorporating a four-gene signature (GAS1, GFPT2, ZFHX4, and KCNA1) to predict patient prognosis and therapeutic response. Validation across multiple datasets revealed that OV patients with higher NCI scores had significantly poorer survival outcomes and resistance to immunotherapy. Additionally, we established a four-class subtyping taxonomy through unsupervised clustering, validated in four independent datasets. The S1 and S3 subtypes were characterized by high NCI scores, abundant stromal and immune infiltration, with the S3 subtype exhibiting the worst survival. Conversely, the S2 subtype showed downregulation of immune response genes, while the S4 subtype displayed epithelial differentiation and favourable prognosis. Integrative analyses of bulk and single-cell transcriptomic data revealed that the S3 subtype had a significantly higher fibroblast proportion compared to other subtypes, whereas the S1 subtype was marked by high T cell content. Through ridge regression-based drug sensitivity analyses, we prioritized candidate therapeutics for each subtype. Notably, the S3 subtype demonstrated sensitivity to dasatinib but resistance to methotrexate. Finally, we developed a user-friendly Shiny-based website to facilitate the application of our prognostic and subtype classification models (https://jli-bioinfo.shinyapps.io/NCI_online/). This study establishes a critical prognostic marker and proposes a novel molecular classification framework grounded in miRNA-regulated gene expression profiles, advancing our understanding of the non-coding mechanisms driving OV heterogeneity.

Molecular Signatures of Cancer Stemness Characterize the Correlations with Prognosis and Immune Landscape and Predict Risk Stratification in Pheochromocytomas and Paragangliomas

BACKGROUND

Pheochromocytoma and paragangliomas (PPGLs) caused refractory hypertension in clinics. The sustained risk of local or metastatic recurrences or new tumor development prompted more research on diagnosis, prognosis prediction, and immunotherapy.

METHOD

The tumor stemness is closely related to the heterogeneous growth of tumor, metastasis, and drug-resistance, and mRNA expression-based stemness indices (mRNAsi) could reflect tumor stemness. This was calculated based on OCLR machine learning algorithm and PPGLs patients' TCGA RNAseq data. The relationship between clinical, molecular, and tumor microenvironment (TME) features and tumor stemness was analyzed through the hub genes that best captured the stem cell characteristics of PPGLs using weighted gene co-expression network analysis (WGCNA), Cox, and LASSO regression analysis.

RESULTS

Our study found that metastatic PPGLs had higher mRNAsi scores, suggesting the degree of tumor stemness could affect metastasis and progression. HRAS, CSDE1, NF1, RET, and VHL-mutant subtypes displayed significant difference in stemness expression. Patients were divided into stemness high-score and low-score subtypes. High-score PPGLs displayed the more unfavorable prognosis compared with low-score, associated with their immune-suppressive features, manifested as low macrophages M1 infiltration and downregulated expression of immune checkpoints. Furthermore, from the viewpoint of stemness features, we established a reliable prognostic for PPGLs, which has the highest AUC value (0.908) in the field so far. And this could stratify PPGLs patients into high-risk and low-risk subtypes, showing the significant differences in prognosis, underlying mechanisms correlated with specific molecular alterations, biological processes activation, and TME. Notably, high immune infiltration and tumor neoantigen in low-risk patients and further resulted in more responsive to immunotherapy.

CONCLUSION

We indicated that tumor stemness could act as the potential biomarker for metastasis or prognosis of PPGLs, and integrated multi-data sources, analyzed valuable stemness-related genes, developed and verified a novel stemness scoring system to predict prognosis and guide the choice of treatment strategies.

The expression landscape and clinical significance of methyltransferase-like 17 in human cancer and hepatocellular carcinoma: a pan-cancer analysis using multiple databases

BACKGROUND

Methyltransferase-like (METTL) family protein plays a crucial role in the progression of malignancies. However, the function of METTL17 across pan-cancers, especially in hepatocellular carcinoma (HCC) is still poorly understood.

METHODS

All original data were downloaded from TCGA, GTEx, HPA, UCSC databases and various data portals. First, we comprehensively analyzed RNA-seq data from the HPA database of 25 human tissues. An array of bioinformatics methods was employed to explore the potential oncogenic roles of METTL17, including analyzing its related prognosis, mutation, landscapes, tumor stemness index, immune cell infiltration, and other factors among different tumors. Additionally, gene set enrichment analysis (GSEA) was used to analyze pathways associated with METTL17 in HCC. Immunohistochemistry (IHC) was performed on clinical samples to validate the differential expression of METTL17 in HCC and normal tissues. Ultimately, we constructed a METTL17-related risk-score model of HCC and validated its prognostic classification efficiency. Survival rates were calculated using the Kaplan-Meier method. Statistical significance was defined as P < 0.05.

RESULTS

METTL17 was differentially expressed in various cancers. METTL17 maintained strong correlations with the cancer patient's prognosis, genetic alterations, tumor stemness index, and immune-infiltrated cells, etc. In addition, IHC experiments verified that METTL expression was significantly decreased in liver tissues of HCC patients compared to normal liver tissue. GESA analysis indicated METTL17 mainly involves oncogenic and immune-related pathways among HCC. MRPS5, CHCHD2, NCBP1, LRPPRC, DAP3, and BMS1 were included in a prognostic model based on METTL17's interaction networks. Kaplan-Meier survival analysis of the prognostic model showed that the overall survival (OS) of the low-risk group was significantly better than that of the high-risk group (P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) of the 1-year, 3-year, and 5-year OS were 0.747, 0.671, and 0.631, respectively.

CONCLUSIONS

METTL17 may serve as a novel prognostic marker and therapeutic target for human tumors, offering a theoretical foundation for formulating more effective and tailored clinical treatment options for cancers, particularly HCC.

Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor.

METHODS

Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups.

RESULTS

By integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses.

CONCLUSIONS

This study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.

TGFBI: A novel therapeutic target for cancer

TGFBI, an extracellular matrix protein induced by transforming growth factor β, has been found to exhibit aberrant expression in various types of cancer. TGFBI plays a crucial role in tumor cell proliferation, angiogenesis, and apoptosis. It also facilitates invasion and metastasis in various types of cancer, including colon, head and neck squamous, renal, and prostate cancers. TGFBI, a prominent p-EMT marker, strongly correlates with lymph node metastasis. TGFBI demonstrates immunosuppressive effects within the tumor immune microenvironment. Targeted therapy directed at TGFBI shows promise as a potential strategy to combat cancer. Hence, a comprehensive review was conducted to examine the impact of TGFBI on various aspects of tumor biology, including cell proliferation, angiogenesis, invasion, metastasis, apoptosis, and the immune microenvironment. This review also delved into the underlying biochemical mechanisms to enhance our understanding of the research advancements related to TGFBI in the context of tumors.

Silencing immune-infiltrating biomarker CCDC80 inhibits malignant characterization and tumor formation in gastric cancer

OBJECTIVE

Tumor immune infiltration leads to poor prognosis of gastric cancer patients and seriously affects the life quality of gastric cancer patients. This study was based on bioinformatics to screen prognostic biomarkers in patients with high degree of immune invasion of gastric cancer. Meanwhile, the action of biomarker CCDC80 was explored in gastric cancer by cell and tumorigenesis experiments, to provide reference for the cure of gastric cancer patients.

METHODS

Data sets and clinical massage on gastric cancer were collected from TCGA database and GEO database. ConsensusClusterPlus was used to cluster gastric cancer patients based on the 28 immune cells infiltration in ssGSEA. R "Limma" package was applied to analyze differential mRNAs between Cluster 1 and Cluster 2. Differential expression genes were screened by single factor analysis. Stemness markers (SERPINF1, DCN, CCDC80, FBLN5, SPARCL1, CCL14, DPYSL3) were identified for differential expression genes. Prognostic value of CCDC80 was evaluated in gastric cancer. Differences in genomic mutation and tumor microenvironment immune infiltration were assessed between high or low CCDC80. Finally, gastric cancer cells (HGC-27 and MKN-45) were selected to evaluate the action of silencing CCDC80 on malignant characterization, macrophage polarization, and tumor formation.

RESULTS

Bioinformatics analysis showed that CCDC80, as a stemness marker, was significantly overexpressed in gastric cancer. CCDC80 was also related to the degree of gastric cancer immune invasion. CCDC80 was up-expressed in cells of gastric cancer. Silencing CCDC80 inhibited malignant characterization and subcutaneous tumor formation of gastric cancer cells. High expression of CCDC80 was positive correspondence with immune invasion. Silencing CCDC80 inhibited M2 polarization and promoted M1 polarization in tumor tissues. In addition, gastric cancer patients were likely to have mutations in CDH1, ACTRT1, GANAB, and CDH10 genes in the High-CCDC80 group.

CONCLUSION

Silencing CCDC80, a prognostic biomarker in patients with immune invasion of gastric cancer, could effectively inhibit the malignant characterization, M2 polarization, and tumor formation of gastric cancer.

Pro-inflammatory cytokines and CXC chemokines as game-changer in age-associated prostate cancer and ovarian cancer: Insights from preclinical and clinical studies' outcomes

Prostate cancer (PC) and Ovarian cancer (OC) are two of the most common types of cancer that affect the reproductive systems of older men and women. These cancers are associated with a poor quality of life among the aged population. Therefore, finding new and innovative ways to detect, treat, and prevent these cancers in older patients is essential. Finding biomarkers for these malignancies will increase the chance of early detection and effective treatment, subsequently improving the survival rate. Studies have shown that the prevalence and health of some illnesses are linked to an impaired immune system. However, the age-associated changes in the immune system during malignancies such as PC and OC are poorly understood. Recent research has suggested that the excessive production of inflammatory immune mediators, such as interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor (TGF), tumor necrosis factor (TNF), CXC motif chemokine ligand 1 (CXCL1), CXC motif chemokine ligand 12 (CXCL12), and CXC motif chemokine ligand 13 (CXCL13), etc., significantly impact the development of PC and OC in elderly patients. Our review focuses on the latest functional studies of pro-inflammatory cytokines (interleukins) and CXC chemokines, which serve as biomarkers in elderly patients with PC and OC. Thus, we aim to shed light on how these biomarkers affect the development of PC and OC in elderly patients. We also examine the current status and future perspective of cytokines (interleukins) and CXC chemokines-based therapeutic targets in OC and PC treatment for elderly patients.

AHRR and SFRP2 in primary versus recurrent high-grade serous ovarian carcinoma and their prognostic implication

BACKGROUND

The aim of this study was to analyse transcriptomic differences between primary and recurrent high-grade serous ovarian carcinoma (HGSOC) to identify prognostic biomarkers.

METHODS

We analysed 19 paired primary and recurrent HGSOC samples using targeted RNA sequencing. We selected the best candidates using in silico survival and pathway analysis and validated the biomarkers using immunohistochemistry on a cohort of 44 paired samples, an additional cohort of 504 primary HGSOCs and explored their function.

RESULTS

We identified 233 differential expressed genes. Twenty-three showed a significant prognostic value for PFS and OS in silico. Seven markers (AHRR, COL5A2, FABP4, HMGCS2, ITGA5, SFRP2 and WNT9B) were chosen for validation at the protein level. AHRR expression was higher in primary tumours (p < 0.0001) and correlated with better patient survival (p < 0.05). Stromal SFRP2 expression was higher in recurrent samples (p = 0.009) and protein expression in primary tumours was associated with worse patient survival (p = 0.022). In multivariate analysis, tumour AHRR and SFRP2 remained independent prognostic markers. In vitro studies supported the anti-tumorigenic role of AHRR and the oncogenic function of SFRP2.

CONCLUSIONS

Our results underline the relevance of AHRR and SFRP2 proteins in aryl-hydrocarbon receptor and Wnt-signalling, respectively, and might lead to establishing them as biomarkers in HGSOC.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.

Stemness signature and targeted therapeutic drugs identification for Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and carries the worst prognosis, characterized by the lack of progesterone, estrogen, and HER2 gene expression. This study aimed to analyze cancer stemness-related gene signature to determine patients' risk stratification and prognosis feature with TNBC. Here one-class logistic regression (OCLR) algorithm was applied to compute the stemness index of TNBC patients. Cox and LASSO regression analysis was performed on stemness-index related genes to establish 16 genes-based prognostic signature, and their predictive performance was verified in TCGA and METABERIC merged data cohort. We diagnosed the expression level of prognostic genes signature in the tumor immune microenvironment, analyzed the TNBC scRNA-seq GSE176078 dataset, and further validated the expression level of prognostic genes using the HPA database. Finally, the small molecular compounds targeted at the anti-tumor effect of predictive genes were screened by molecular docking; this novel stemness-based prognostic genes signature study could facilitate the prognosis of patients with TNBC and thus provide a feasible therapeutic target for TNBC.

Stemness genes and miR-1247-3p expression associate with clinicopathological parameters and prognosis in lung adenocarcinoma

Lung cancer makes up one-fourth of all cancer-related mortality with the highest mortality rate among all cancers. Despite recent scientific advancements in cancer therapeutics, the 5-year survival rate of lung adenocarcinoma (LUAD) cancer patients remains below 15 percent. It has been suggested that the high mortality rate of LUAD is linked to the acquisition of progenitor-like cells with stem-like characteristics that assist the whole tumor in regulating immune cell infiltration. To examine this hypothesis further, this study mined several databases to explore the presence of stemness-related genes and miRNAs in LUAD cancers. We examine their association with immune and accessory cell infiltration rates and patient survival. We found 3 stem cell-related genes, ORC1L, KIF20A, and DLGAP5, present in LUAD that also correlate with changes in immune infiltration rates and reduced patient survival rates. Additionally, the modulation in myeloid-derived suppressor cell (MDSC) infiltration and miRNA hsa-mir-1247-3p mediated targeting of tumor suppressor SLC24A4 and oncogenes RAB3B and HJURP appears to primarily regulate LUAD patient survival. Given these findings, hsa-mir-1247-3p and/or its associated gene targets may offer a promising avenue to enhance patient survivability.

The TGFBI gene and protein expression in topotecan resistant ovarian cancer cell lines

PURPOSE

The primary limiting factor in achieving cures for patients with cancer, particularly ovarian cancer, is drug resistance. The mechanisms of drug resistance of cancer cells during chemotherapy may include compounds of the extracellular matrix, such as the transforming growth factor-beta-induced protein (TGFBI). In this study, we aimed to analyze the TGFBI gene and protein expression in different sensitive and drug-resistant ovarian cancer cell lines, as well as test if TGFBI can be involved in the response to topotecan (TOP) at the very early stages of treatment.

MATERIALS AND METHODS

In this study, we conducted a detailed analysis of TGFBI expression in different ovarian cancer cell lines (A2780, A2780TR1, A2780TR2, W1, W1TR, SKOV-3, PEA1, PEA2 and PEO23). The level of TGFBI mRNA (QPCR), intracellular and extracellular protein (Western blot analysis) were assessed in this study.

RESULTS

We observed upregulation of TGFBI mRNA in drug-resistant cell lines and estrogen-receptor positive cell lines, which was supported by overexpression of both intracellular and extracellular TGFBI protein. We also showed the TGFBI expression after a short period of treatment of sensitive ovarian cancer cell lines with TOP.

CONCLUSION

The expression of TGFBI in ovarian cancer cell lines suggests its role in the development of drug resistance.

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.

Development and validation of a novel anoikis-related gene signature for predicting prognosis in ovarian cancer

Anoikis plays a critical role in variable cancer types. However, studies that focus on the prognostic values of anoikis-related genes (ANRGs) in OV are scarce. Cohorts with transcriptome data and corresponding clinicopathologic data of OV patients were collected and consolidated from public databases. Multiple bioinformatics approaches were used to screen key genes from 446 anoikis-related genes, including Cox regression analysis, random survival forest analysis, and Kaplan-Meier analysis of best combinations. A five-gene signature was constructed in the discovery cohort (TCGA) and validated in four validation cohorts (GEO). Risk score of the signature stratified patients into high-risk (HRisk) and low-risk (LRisk) subgroups. Patients in the HRisk group were associated with worse OS than those in the LRisk group in both the TCGA cohort (p<0.0001, HR=2.718, 95%CI:1.872-3.947) and the four GEO cohorts (p<0.05). Multivariate Cox regression analyses confirmed that the risk score served as an independent prognostic factor in both cohorts. The signature's predictive capacity was further demonstrated by the nomogram analysis. Pathway enrichment analysis revealed that immunosuppressive and malignant progression-related pathways were enriched in the HRisk group, including TGF-β, WNT and ECM pathways. The LRisk group was characterized by immune-active signaling pathways (interferon-gamma, T cell activation, etc.) and higher proportions of anti-tumor immune cells (NK, M1, etc.) while HRisk patients were associated with higher stromal scores and less TCR richness. In conclusion, the signature reveals a close relationship between the anoikis and prognosis and may provide a potential therapeutic target for OV patients.

Body Composition and Metabolic Dysfunction Really Matter for the Achievement of Better Outcomes in High-Grade Serous Ovarian Cancer

Although obesity-associated metabolic disorders have a negative impact on various cancers, such evidence remains controversial for ovarian cancer. Here, we aimed to evaluate the impact of body composition (BC) and metabolism disorders on outcomes in high-grade serous ovarian cancer (HGSOC).

METHODS

We analyzed clinical/genomic data from two cohorts (PUC n = 123/TCGA-OV n = 415). BC was estimated using the measurement of adiposity/muscle mass by a CT scan. A list of 425 genes linked to obesity/lipid metabolism was used to cluster patients using non-negative matrix factorization. Differential expression, gene set enrichment analyses, and Ecotyper were performed. Survival curves and Cox-regression models were also built-up.

RESULTS

We identified four BC types and two clusters that, unlike BMI, effectively correlate with survival. High adiposity and sarcopenia were associated with worse outcomes. We also found that recovery of a normal BC and drug interventions to correct metabolism disorders had a positive impact on outcomes. Additionally, we showed that immune-cell-depleted microenvironments predominate in HGSOC, which was more evident among the BC types and the obesity/lipid metabolism cluster with worse prognosis.

CONCLUSIONS

We have demonstrated the relevance of BC and metabolism disorders as determinants of outcomes in HGSOC. We have shone a spotlight on the relevance of incorporating corrective measures addressing these disorders to obtain better results.