Identification of novel genes in testicular cancer microenvironment based on ESTIMATE algorithm-derived immune scores

VDR is a potential prognostic biomarker and positively correlated with immune infiltration: a comprehensive pan-cancer analysis with experimental verification

The vitamin D receptor (VDR) is a transcription factor that mediates a variety of biological functions of 1,25-dihydroxyvitamin D3. Although there is growing evidence of cytological and animal studies supporting the suppressive role of VDR in cancers, the conclusion is still controversial in human cancers and no systematic pan-cancer analysis of VDR is available. We explored the relationships between VDR expression and prognosis, immune infiltration, tumor microenvironment, or gene set enrichment analysis (GSEA) in 33 types of human cancers based on multiple public databases and R software. Meanwhile, the expression and role of VDR were experimentally validated in papillary thyroid cancer (PTC). VDR expression decreased in 8 types and increased in 12 types of cancer compared with normal tissues. Increased expression of VDR was associated with either good or poor prognosis in 13 cancer types. VDR expression was positively correlated with the infiltration of cancer-associated fibroblasts, macrophages, or neutrophils in 20, 12, and 10 cancer types respectively and this correlation was experimentally validated in PTC. Increased VDR expression was associated with increased percentage of stromal or immune components in tumor microenvironment (TME) in 24 cancer types. VDR positively and negatively correlated genes were enriched in immune cell function and energy metabolism pathways, respectively, in the top 9 highly lethal tumors. Additionally, VDR expression was increased in PTC and inhibited cell proliferation and migration. In conclusion, VDR is a potential prognostic biomarker and positively correlated with immune infiltration as well as stromal or immune components in TME in multiple human cancers.

Identification of prognostic risk score of disulfidptosis-related genes and molecular subtypes in glioma

Background

Programmed cell death is closely related to glioma. As a novel kind of cell death, the mechanism of disulfidptosis in glioma remains unclear. Therefore, it is of great importance to study the role of disulfidptosis-related genes (DRGs) in glioma.

Methods

We first investigated the genetic and transcriptional alterations of 15 DRGs. Two consensus cluster analyses were used to evaluate the association between DRGs and glioma subtypes. In addition, we constructed prognostic DRG risk scores to predict overall survival (OS) in glioma patients. Furthermore, we developed a nomogram to enhance the clinical utility of the DRG risk score. Finally, the expression levels of DRGs were verified by immunohistochemistry (IHC) staining.

Results

Most DRGs (14/15) were dysregulated in gliomas. The 15 DRGs were rarely mutated in gliomas, and only 50 of 987 samples (5.07 %) showed gene mutations. However, most of them had copy number variation (CNV) deletions or amplifications. Two distinct molecular subtypes were identified by cluster analysis, and DRG alterations were found to be related to the clinical characteristics, prognosis, and tumor immune microenvironment (TIME). The DRG risk score model based on 12 genes was developed and showed good performance in predicting OS. The nomogram confirmed that the risk score had a particularly strong influence on the prognosis of glioma. Furthermore, we discovered that low DRG scores, low tumor mutation burden, and immunosuppression were features of patients with better prognoses.

Conclusion

The DRG risk model can be used for the evaluation of clinical characteristics, prognosis prediction, and TIME estimation of glioma patients. These DRGs may be potential therapeutic targets in glioma.

PTPRC functions as a prognosis biomarker in the tumor microenvironment of cutaneous melanoma

Cutaneous melanoma is one of the most malignant types of skin cancer, with an extremely poor prognosis. Immune cells infiltrated in the tumor microenvironment (TME) affects melanoma initiation, progression, prognosis and immunotherapy strategies in melanoma. The potential utility of TME-related genes as a prognostic model for melanoma and as a predictor of immunotherapeutic response merits further exploration. In this study, we determined that an immune-related gene, protein tyrosine phosphatase receptor type C (PTPRC), was positively correlated with the positive prognosis of melanoma patients. Integration of this gene with TNM classification created a predictive model that showed better performance in determining overall survival than others. PTPRC expression was positively correlated with the levels of immune checkpoint molecules, and PTPRC knockdown significantly enhanced the migration, invasion, and proliferation of melanoma cells. Finally, immunohistochemical results from HPA and Real-time quantitative PCR of clinical tissues confirmed that PTPRC expression was higher in melanoma than in normal skin. In conclusion, PTPRC served as a potential predictor of survival and response to immunotherapy in melanoma patients. The risk model combining the PTPRC and TNM classifications holds the potential to be a promising tool for prognostic prediction of cutaneous melanoma. This will help in the effective clinical management of melanoma patients.

Radiation-sensitive genetic prognostic model identifies individuals at risk for radiation resistance in head and neck squamous cell carcinoma

BACKGROUND

The advantages of radiotherapy for head and neck squamous cell carcinoma (HNSCC) depend on the radiation sensitivity of the patient. Here, we established and verified radiological factor-related gene signature and built a prognostic risk model to predict whether radiotherapy would be beneficial.

METHODS

Data from The Cancer Genome Atlas, Gene Expression Omnibus, and RadAtlas databases were subjected to LASSO regression, univariate COX regression, and multivariate COX regression analyses to integrate genomic and clinical information from patients with HNSCC. HNSCC radiation-related prognostic genes were identified, and patients classified into high- and low-risk groups, based on risk scores. Variations in radiation sensitivity according to immunological microenvironment, functional pathways, and immunotherapy response were investigated. Finally, the expression of HNSCC radiation-related genes was verified by qRT-PCR.

RESULTS

We built a clinical risk prediction model comprising a 15-gene signature and used it to divide patients into two groups based on their susceptibility to radiation: radiation-sensitive and radiation-resistant. Overall survival was significantly greater in the radiation-sensitive than the radiation-resistant group. Further, our model was an independent predictor of radiotherapy response, outperforming other clinical parameters, and could be combined with tumor mutational burden, to identify the target population with good predictive value for prognosis at 1, 2, and 3 years. Additionally, the radiation-resistant group was more vulnerable to low levels of immune infiltration, which are significantly associated with DNA damage repair, hypoxia, and cell cycle regulation. Tumor Immune Dysfunction and Exclusion scores also suggested that the resistant group would respond less favorably to immunotherapy.

CONCLUSIONS

Our prognostic model based on a radiation-related gene signature has potential for application as a tool for risk stratification of radiation therapy for patients with HNSCC, helping to identify candidates for radiation therapy and overcome radiation resistance.

Identification of anoikis-related subtypes and immune landscape in kidney renal clear cell carcinoma

Anoikis is a specific form of programmed cell death induced by the loss of cell contact with the extracellular matrix and other cells, and plays an important role in organism development, tissue homeostasis, disease development and tumor metastasis. We comprehensively investigated the expression patterns of anoikis-related genes (ARGs) in kidney renal clear cell carcinoma (KIRC) from public databases. Anoikis-related prognostic signatures were established based on four ARGs expression, in which KIRC patients were assigned different risk scores and divided into two different risk groups. In addition, four ARGs expression was validated by qRT-PCR. A better prognosis was observed in the low-risk group, but with lower immune activity (including immune cells and immune-related functions) in the tumor microenvironment. Combined with the relevant clinical characteristics, a nomogram for clinical application was established. Receiver operating characteristics (ROC) and calibration curves were constructed to demonstrate the predictive power of this risk signature. In addition, higher risk scores were significantly and positively correlated with higher gene expression of tumor mutation load (TMB), immune checkpoints (ICPs) and mismatch repair (MMR)-related proteins in general. The results also suggested that the high-risk group was more sensitive to immunotherapy and certain chemotherapeutic agents. Anoikis-related prognostic signatures may provide a better understanding of the roles of ARGs and offer new perspectives for clinical prognosis and individualized treatment.

CCDC103 as a Prognostic Biomarker Correlated with Tumor Progression and Immune Infiltration in Glioma

Background

The Coiled-coil domain-containing proteins (CCDCs) are expressed in many cancers, but the role of Coiled-coil domain-containing protein 103 (CCDC103) in cancers remains unclear. Further investigations are necessary to ascertain its diagnostic significance and understand its biological function in cancers. This study aims to elucidate the biological functionalities of CCDC103 in glioma and evaluate the correlation between CCDC103 expression with glioma progression.

Methods

Clinical data on glioma patients were acquired from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and the Gene Expression Omnibus (GEO). The evaluation encompassed the examination of correlations between CCDC103 expression, pathological characteristics, and clinical outcomes. Furthermore, the analysis included the assessment of the correlations between CCDC103 expression and immune cell infiltration as well as glioma progression.

Results

Gliomas have higher levels of CCDC103 expression than the para-carcinoma tissues. Poorer prognosis, unfavorable histological characteristics, the absence of IDH gene mutations, and the absence of chromosome 1p and 19q deletions were all associated with higher expression of CCDC103 in gliomas. In addition to patient age, tumor grade, the absence of IDH mutations, and the absence of chromosome 1p and 19q deletions, univariate and multivariate Cox analyses showed that CCDC103 expression was independently prognostic of overall survival, disease-free survival, and progression-free survival in patients with glioma. Furthermore, tumor infiltration of B cells, neutrophils, macrophages, and dendritic cells were all linked with elevated expression of CCDC103. High CCDC103 expression was linked to immune response-related signaling pathways and cell proliferation, according to gene set enrichment analysis (GSEA). Notably, the knockdown of CCDC103 in glioma cell lines resulted in a significant reduction in cell proliferation and migration.

Conclusion

The correlation between CCDC103 expression and both glioma progression and immune cell infiltration implies that CCDC103 expression holds promise as a valuable prognostic biomarker for glioma.

A tumor-associated macrophages related model for predicting biochemical recurrence and tumor immune environment in prostate cancer

OBJECTIVE

To identify tumor-associated macrophages (TAMs) related molecular subtypes and develop a TAMs related prognostic model for prostate cancer (PCa).

METHODS

Consensus clustering analysis was used to identify TAMs related molecular clusters. A TAMs related prognostic model was developed using univariate and multivariate Cox analysis.

RESULTS

Three TAMs related molecular clusters were identified and were confirmed to be associated with prognosis, clinicopathological characteristics, PD-L1 expression levels and tumor microenvironment. A TAMs related prognostic model was constructed. Patients in low-risk group all showed a more appreciable biochemical recurrence-free survival (BCRFS) than patients in high-risk group in train cohort, test cohort, entire TCGA cohort and validation cohort. SLC26A3 attenuated progression of PCa and prevented macrophage polarizing to TAMs phenotype, which was initially verified.

CONCLUSIONS

We successfully identified molecular clusters related to TAMs. Additionally, we developed a prognostic model involving TAMs that exhibits excellent predictive performance for biochemical recurrence-free survival in PCa.

Analysis of angiogenesis-related subtypes of hepatocellular carcinoma and tumor microenvironment infiltration feature in hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is a highly vascularized tumor, and angiogenesis plays an important role in its progression. However, the role of angiogenesis in cell infiltration in the tumor microenvironment (TME) remains unclear.

METHODS

We evaluated the associations of 35 angiogenesis-related genes (ARGs) with the clinicopathological features of 816 HCC patients. In addition, we assessed the associations between the ARGs and TME cell infiltration. A nomogram was constructed to determine the prognostic value of ARGs for HCC. The ARG score was used to distinguish angiogenic subtypes of HCC, and its usefulness for predicting the prognosis and treatment response of HCC patients was evaluated.

RESULTS

We distinguished three ARG clusters differing in terms of TME cell infiltration, immune cell activation status, clinicopathological features, and clinical outcomes. There were significant associations of ARG expression with tumor immunity, the epithelial-mesenchymal transition (EMT), and transforming growth factor-β expression. An ARG score model was constructed to generate a risk score for each patient based on differentially expressed genes between clusters. Furthermore, a high ARG score was associated with high expression of CTLA-4 and PD-L1/PD-1, and a low Tumor Immune Dysfunction and Exclusion score, indicating the usefulness of the ARG score for selecting patients for immunotherapy. Considering the relationship between ARGs and tumor immunity, immunotherapy combined with vascular-targeted therapy may be the best treatment for HCC.

CONCLUSIONS

ARGs play an important role in TME diversity and complexity in HCC patients. The ARG score of HCC predicts TME invasion and can guide immunotherapy.

Immune infiltration and a necroptosis-related gene signature for predicting the prognosis of patients with cervical cancer

Background: Cervical cancer (CC), the fourth most common cancer among women worldwide, has high morbidity and mortality. Necroptosis is a newly discovered form of cell death that plays an important role in cancer development, progression, and metastasis. However, the expression of necroptosis-related genes (NRGs) in CC and their relationship with CC prognosis remain unclear. Therefore, we screened the signature NRGs in CC and constructed a risk prognostic model. Methods: We downloaded gene data and clinical information of patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) from The Cancer Genome Atlas (TCGA) database. We performed functional enrichment analysis on the differentially expressed NRGs (DENRGs). We constructed prognostic models and evaluated them by Cox and LASSO regressions for DENRGs, and validated them using the International Cancer Genome Consortium (ICGC) dataset. We used the obtained risk score to classify patients into high- and low-risk groups. We employed the ESTIMATE and single sample gene set enrichment analysis (ssGSEA) algorithms to explore the relationship between the risk score and the clinical phenotype and the tumor immune microenvironment. Results: With LASSO regression, we established a prognostic model of CC including 16 signature DENRGs (TMP3, CHMP4C, EEF1A1, FASN, TNF, S100A10, IL1A, H1.2, SLC25A5, GLTP, IFNG, H2AC13, TUBB4B, AKNA, TYK2, and H1.5). The risk score was associated with poor prognosis in CC. Survival was lower in the high-risk group than the low-risk group. The nomogram based on the risk score, T stage, and N stage showed good prognostic predictive power. We found significant differences in immune scores, immune infiltration analysis, and immune checkpoints between the high- and low-risk groups (p < 0.05). Conclusion: We screened for DENRGs based on the TCGA database by using bioinformatics methods, and constructed prognostic models based on the signature DENRGs, which we confirmed as possibly having important biological functions in CC. Our study provides a new perspective on CC prognosis and immunity, and offers a series of new targets for future treatment.

A Novel Tumor Mutation Burden Related lncRNA Signature Identified Prognosis and Tumor Immune Microenvironment Features in Clear Cell Renal Cell Carcinoma

BACKGROUND

Emerging evidence indicates that long noncoding RNA (lncRNA) plays an important biological role in clear cell renal cell carcinoma (ccRCC); however, the clinical value of tumor mutation burden-related lncRNA in ccRCC patients is unknown yet.

METHODS

Somatic mutation profiles and lncRNA expression data of ccRCC were downloaded from the TCGA database. We retrospectively analyzed lncRNA expression data and survival information from 116 patients with ccRCC fromJanuary 2013 to January 2014. Univariate and multivariate Cox regression analyses were performed to construct lncRNA signature, and the prognosis value was determined by Kaplan-Mayer and receiver operating characteristic curve (ROC) analysis.

RESULTS

Based on 160 differentially expressed TMB-related lncRNAs, two TMB-related molecular clusters with distinct immune checkpoints expression and immune cells infiltration were established for ccRCC patients. Moreover, a novel TMB-related lncRNA signature was constructed based on five lncRNAs for individualized prognosis assessment. High-risk group represents significantly worse overall survival in all cohorts. The area under the ROC curve was 0.716, 0.775 and 0.744 in the training cohort, testing cohort and TCGA cohort, respectively. Results of qRT-PCR successfully validated the expression levels of AP002360.3, LINC00460, AL590094.1, LINC00944 and LINC01843 in HK-2, 786-O, 769-P and ACHN cells. More importantly, the predictive performance of TMB-related lncRNA signature was successfully validated in an independent cohort of 116 ccRCC patients at our institution.

CONCLUSION

This study successfully developed and validated a novel TMB-related lncRNA signature for individualized prognosis assessment of ccRCC patients.

Crosstalk of Oxidative Phosphorylation-Related Subtypes, Establishment of a Prognostic Signature and Immune Infiltration Characteristics in Colorectal Adenocarcinoma

Simple Summary

Oxidative phosphorylation (OXPHOS) plays an important role in the progression of colorectal adenocarcinoma (COAD). The aim of our study was to investigate the expression pattern of OXPHOS-related genes (ORGs), and an OXPHOS-related prognostic signature was constructed to classify COAD patients into high-risk and low-risk groups. Then, we analyzed the relationship between risk scores and tumor microenvironment, somatic mutation, and efficacy of immunotherapy and chemotherapy. Additionally, a nomogram was established by combining clinical features and risk scores, and its predictive ability was verified by receiver operating characteristics and calibration curves. Overall, the OXPHOS-related signature can be used as a reliable prognostic predictor of COAD patients.

Abstract

Oxidative phosphorylation (OXPHOS) is an emerging target in cancer therapy. However, the prognostic signature of OXPHOS in colorectal adenocarcinoma (COAD) remains non-existent. We comprehensively investigated the expression pattern of OXPHOS-related genes (ORGs) in COAD from public databases. Based on four ORGs, an OXPHOS-related prognostic signature was established in which COAD patients were assigned different risk scores and classified into two different risk groups. It was observed that the low-risk group had a better prognosis but lower immune activities including immune cells and immune-related function in the tumor microenvironment. Combining with relevant clinical features, a nomogram for clinical application was also established. Receiver operating characteristic (ROC) and calibration curves were constructed to demonstrate the predictive ability of this risk signature. Moreover, a higher risk score was significantly positively correlated with higher tumor mutation burden (TMB) and generally higher gene expression of immune checkpoint, N6-methyladenosine (m6A) RNA methylation regulators and mismatch repair (MMR) related proteins. The results also indicated that the high-risk group was more sensitive to immunotherapy and certain chemotherapy drugs. In conclusion, OXPHOS-related prognostic signature can be utilized to better understand the roles of ORGs and offer new perspectives for clinical prognosis and personalized treatment.

A hypoxia-related lncRNA model for prediction of head and neck squamous cell carcinoma prognosis

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the most common and highly heterogeneous malignancies worldwide. Increasing studies have proven that hypoxia and related long non-coding RNA (lncRNA) are involved in the occurrence and prognosis of HNSCC. The goal of this work is to construct a risk assessment model using hypoxia-related lncRNAs (hrlncRNAs) for HNSCC prognosis prediction and personalized treatment.

METHODS

Transcriptome expression matrix, clinical follow-up data, and somatic mutation data of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA). We used co-expression analysis to identify hrlncRNAs, then screened for differentially expressed lncRNAs (DEhrlncRNAs), and paired these DEhrlncRNAs. The risk model was established through univariate, least absolute shrinkage and selection operator (LASSO), and stepwise multivariate Cox regression. Finally, we assessed the model from multiple perspectives of tumor mutation burden (TMB), tumor immune infiltration, chemotherapeutic sensitivity, immune checkpoint inhibitor (ICI), and functional enrichment.

RESULTS

The risk assessment model included 14 hrlncRNA pairs. The risk score was observed to be a reliable prognostic factor. The high-risk patients had an unfavorable prognosis and significant differences from the low-risk group in TMB and tumor immune infiltration. In the high-risk patients, the common immune checkpoints were down-regulated, including CTLA4 and PDCD1, and the sensibility to paclitaxel and docetaxel was higher. The functional enrichment analysis suggested that the low-risk group was accompanied by activated immune function.

CONCLUSIONS

The risk assessment model of 14-hrlncRNA-pairs demonstrated a promising prognostic prediction for HNSCC patients and can guide personalized clinical treatment.

Combined prognostic value of preoperative serum thyrotrophin and thyroid hormone concentration in papillary thyroid cancer

Background

A growing number of studies have found a close association between thyroid hormones and thyrotrophin (TSH), and they also have prognostic significance in some cancer types; this study aimed to investigate the prognostic value of free triiodothyronine (fT3), free thyroxine (fT4), fT3/fT4, TSH, and their combination in patients with papillary thyroid carcinoma (PTC).

Methods

This study retrospectively analyzed the relevant data of 726 newly diagnosed PTC patients. Both univariate and multivariate analyses were used to predict the recurrence rate, and a risk score was established. In addition, with the use of a random survival forest, a random forest (RF) score was constructed. After calculating the area under the curve (AUC), the diagnostic efficacy of risk score, RF score, and four indicators was compared.

Results

fT3, fT4, fT3/fT4, and TSH were strongly associated with some invasive clinicopathological features and postoperative recurrence. Patients with high expression of fT4 and TSH have a high risk of recurrence. By contrast, patients with high expression of fT3 and fT3/fT4 have a low risk of recurrence. At the same time, the combined use of various indicators is more helpful for establishing an accurate diagnosis. By comparison, we found that the RF score was better than the risk score in terms of predicting the recurrence of PTC.

Conclusion

The diagnostic accuracy of a combination of fT3, fT4, fT3/fT4, and TSH can help improve our clinical estimate of the risk of recurrent PTC, thus allowing the development of a more effective treatment plan for patients.

A radiation resistance related index for biochemical recurrence and tumor immune environment in prostate cancer patients

PURPOSE

To establish and verify a novel radiation resistance related index for predicting biochemical recurrence and tumor immune environment in prostate cancer (PCa) patients.

MATERIALS AND METHODS

The transcriptome information of PCa were obtained from GEO and TCGA portal. We identified radiation resistance related genes (RRGs) between radioresistant and radiosensitive PCa cells. We conducted multivariate Cox analysis to construct a novel radiation resistance related index for predicting biochemical recurrence (BCR)-free survival (BCRFS). Internal and external validations were conducted. Preliminary experimental verifications were performed.

RESULTS

We identified 194 differentially expressed RRGs and three radiation resistance related molecular clusters for PCa. Moreover, we established a novel radiation resistance related index and succeeded in conducting internal and external validations. High-risk populations meant significantly worse BCRFS in training, testing and validating cohort. The area under receiver operating characteristic curve were 0.809, 0.698, and 0.712 in training, testing, and validating cohort. The immune microenvironment was significantly different between high and low-risk score patients. Preliminary experiment identified and validated three potential biomarkers related to radiation resistance (ZNF695, TM4SF19, CCDC3) of PCa.

CONCLUSIONS

This study successfully established and verified a novel radiation resistance related index, which had an excellent performance in predicting BCR and tumor immune microenvironment in patients with PCa.

Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes for Breast Cancer

Background

The changes of lipid metabolism have been implicated in the development of many tumors, but its role in breast invasive carcinoma (BRCA) remains to be fully established. Here, we attempted to ascertain the prognostic value of lipid metabolism-related genes in BRCA.

Methods

We obtained RNA expression data and clinical information for BRCA and normal samples from public databases and downloaded a lipid metabolism-related gene set. Ingenuity Pathway Analysis (IPA) was applied to identify the potential pathways and functions of Differentially Expressed Genes (DEGs) related to lipid metabolism. Subsequently, univariate and multivariate Cox regression analyses were utilized to construct the prognostic gene signature. Functional enrichment analysis of prognostic genes was achieved by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Kaplan-Meier analysis, Receiver Operating Characteristic (ROC) curves, clinical follow-up results were employed to assess the prognostic potency. Potential compounds targeting prognostic genes were screened by Connectivity Map (CMap) database and a prognostic gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Furthermore, we separately validated the selected marker genes in BRCA samples and human breast cancer cell lines (MCF-7, MDA-MB-231).

Results

IPA and functional enrichment analysis demonstrated that the 162 lipid metabolism-related DEGs we obtained were involved in many lipid metabolism and BRCA pathological signatures. The prognostic classifier we constructed comprising SDC1 and SORBS1 can serve as an independent prognostic marker for BRCA. CMap filtered 37 potential compounds against prognostic genes, of which 16 compounds could target both two prognostic genes were identified by CTD. The functions of the two prognostic genes in breast cancer cells were verified by cell function experiments.

Conclusion

Within this study, we identified a novel prognostic classifier based on two lipid metabolism-related genes: SDC1 and SORBS1. This result highlighted a new perspective on the metabolic exploration of BRCA.

The interaction between long non-coding RNA LINC01564 and POU2F1 promotes the proliferation and metastasis of gastric cancer

Background

An increasing number of studies have demonstrated that long non-coding RNAs (lncRNAs) serve as key regulators in tumor development and progression. However, only a few lncRNAs have been functionally characterized in gastric cancer (GC).

Methods

Bioinformatics analysis was conducted to find lncRNAs that are associated with GC metastasis. RNA FISH, RIP, and RNA pull down assays were used to study the complementary binding of LINC01564 complementary to the 3′UTR of transcription factor POU2F1. The transcription activation of LINC01564 by POU2F1 as a transcription factor was examined by ChIP assay. In vitro assays such as MTT, cell invasion assay, and clonogenic assay were conducted to examined the impacts of LINC01564 and POU2F1 on GC cell proliferation and invasion. Experiments in vivo were performed to access the impacts of LINC01564 and POU2F1 on GC metastasis.

Results

The results showed that LINC01564 complementary bound to the 3′UTR of POU2F1 to form an RNA duplex, whereby stabilizing POU2F1 mRNA and increasing the enrichment in cells. The level of LINC01564 was also increased by POU2F1 through transcription activation. In vitro assays showed that LINC01564 promoted the proliferation, invasion and migration of GC cells through increasing POU2F1. In vivo experiments indicate the promotion of GC proliferation and metastasis by the interaction between LINC01564 and POU2F1.

Conclusion

Taken together, our results indicate that the interaction between LINC01564 and POU2F1 promotes the proliferation, migration and invasion of GC cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03391-x.

LINC01564 Promotes the TMZ Resistance of Glioma Cells by Upregulating NFE2L2 Expression to Inhibit Ferroptosis

Glioma is the most common and malignant brain tumor with poor prognosis. We investigated the effects of LINC01564 on temozolomide (TMZ) resistance of glioma cells. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the high expression of LINC01564 in human TMZ-resistant glioma cell lines. Functional experiments verified that LINC01564 and SRSF1 promote the proliferation and TMZ resistance and inhibit the apoptosis of TMZ-treated glioma cells. Iron and ROS detection analyses showed that LINC01564 and SRSF1 suppress ferroptosis in glioma cells. Western blot proved that LINC01564 is positively associated with NFE2L2. Mechanism experiments verified the interaction between SRSF1 and MAPK8 3' UTR. In vitro kinase assays showed that MAPK8 can phosphorylate NFE2L2. Rescue experiments showed that MAPK8 reverses the effect of LINC01564 ablation on cell apoptosis and ferroptosis. Meanwhile, NFE2L2 countervails the effect of MAPK8 ablation on the apoptosis and ferroptosis of glioma cells. Animal experiments proved that LINC01564 and MAPK8 facilitate the TMZ resistance of glioma cells in vivo. In conclusion, LINC01564 promotes the TMZ resistance of glioma cells by upregulating NFE2L2 expression to inhibit ferroptosis, which might offer a new perspective into TMZ treatment of glioma. The diagram of the specific mechanism that LINC01564 promotes the TMZ resistance of glioma cells by upregulating NFE2L2 expression to inhibit ferroptosis.

Identification of the Shared Gene Signatures and Biological Mechanism in Type 2 Diabetes and Pancreatic Cancer

Background

The relationship between pancreatic cancer (PC) and type 2 diabetes mellitus (T2DM) has long been widely recognized, but the interaction mechanisms are still unknown. This study was aimed to investigate the shared gene signatures and molecular processes between PC and T2DM.

Methods

The Gene Expression Omnibus (GEO) database was used to retrieve the RNA sequence and patient information of PC and T2DM. Weighted gene co-expression network analysis (WGCNA) was performed to discover a co-expression network associated with PC and T2DM. Enrichment analysis of shared genes present in PC and T2DM was performed by ClueGO software. These results were validated in the other four cohorts based on differential gene analysis. The predictive significance of S100A6 in PC was evaluated using univariate and multivariate Cox analyses, as well as Kaplan-Meier plots. The biological process of S100A6 enrichment in PC was detected using Gene Set Enrichment Analysis (GSEA). The involvement of S100A6 in the tumor immune microenvironment (TIME) was assessed by CIBERSORT. In vitro assays were used to further confirm the function of S100A6 in PC.

Results

WGCNA recognized three major modules for T2DM and two major modules for PC. There were 44 shared genes identified for PC and T2DM, and Gene Ontology (GO) analysis showed that regulation of endodermal cell fate specification was primarily enriched. In addition, a key shared gene S100A6 was derived in the validation tests. S100A6 was shown to be highly expressed in PC compared to non-tumor tissues. PC patients with high S100A6 expression had worse overall survival (OS) than those with low expression. GSEA revealed that S100A6 is involved in cancer-related pathways and glycometabolism-related pathways. There is a strong relationship between S100A6 and TIME. In vitro functional assays showed that S100A6 helped to induce the PC cells' proliferation and migration. We also proposed a diagram of common mechanisms of PC and T2DM.

Conclusions

This study firstly revealed that the regulation of endodermal cell fate specification may be common pathogenesis of PC and T2DM and identified S100A6 as a possible biomarker and therapeutic target for PC and T2DM patients.

Identification and validation of a prognostic model for melanoma patients with 9 ferroptosis-related gene signature

Background

Melanoma is a highly heterogeneous and aggressive cutaneous malignancy. Ferroptosis, a new pathway of cell death depending on the intracellar iron, has been shown to be significantly associated with apoptosis of a number of tumors, including melanoma. Nevertheless, the relationship between ferroptosis-related genes (FRGs) and the melanoma patients’ prognosis needs to be explored.

Methods

Download expression profiles of FRGs and clinical data from The Cancer Genome Atlas (TCGA) database. 70% data were randomly selected from the TCGA database and utilized the univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model to create a prognostic model, and the remaining 30% was used to validate the predictive power of the model. In addition, GSE65904 and GSE22153 date sets as the verification cohort to testify the predictive ability of the signature.

Results

We identified nine FRGs relating with melanoma patients’ overall survival (OS) and established a prognostic model based on their expression. During the research, patients were divided into group of high-risk and low-risk according to the results of LASSO regression analysis. Survival time was significantly longer in the low-risk group than that of in the high-risk group (P < 0.001). Enrichment analysis of different risk groups demonstrated that the reasons for the difference were related to immune-related pathways, and the degree of immune cell infiltration in the low-risk group was significantly higher than that in the high-risk group.

Conclusions

The FRG prognostic model we established can predict the prognosis of melanoma patients and may further guide subsequent treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08475-y.

A Novel Ferroptosis-Based Molecular Signature Associated with Biochemical Recurrence-Free Survival and Tumor Immune Microenvironment of Prostate Cancer

Objective: To identify ferroptosis-related molecular clusters, and to develop and validate a ferroptosis-based molecular signature for predicting biochemical recurrence-free survival (BCRFS) and tumor immune microenvironment of prostate cancer (PCa). Materials and Methods: The clinical data and transcriptome data of PCa were downloaded from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained from FerrDb database. We performed consensus clustering analysis to identify ferroptosis-related molecular subtypes for PCa. Univariate and multivariate Cox regression analysis were used to establish a ferroptosis-based signature for predicting BCRFS. Internal verification, external verification and subgroup survival analysis were then successfully performed. Results: There was a total of 40 differentially expressed FRGs in PCa. We then identified three ferroptosis-related molecular clusters of PCa, which have significantly different immune infiltrating cells, tumor immune microenvironment and PD-L1 expression level. More importantly, a novel ferroptosis-based signature for predicting BCRFS of PCa based on four FRGs (including ASNS, GPT2, NFE2L2, RRM2) was developed. Internal and external verifications were then successfully performed. Patients with high-risk score were associated with significant poor BCRFS compared with those with low-risk score in training cohort, testing cohort and validating cohort, respectively. The area under time-dependent Receiver Operating Characteristic (ROC) curve were 0.755, 0.705 and 0.726 in training cohort, testing cohort and validating cohort, respectively, indicating the great performance of this signature. Independent prognostic analysis indicated that this signature was an independent predictor for BCRFS of PCa. Subgroup analysis revealed that this signature was particularly suitable for younger or stage T III-IV or stage N0 or cluster 1-2 PCa patients. Patients with high-risk score have significantly different tumor immune microenvironment in comparison with those with low-risk score. The results of qRT-PCR successfully verified the mRNA expression levels of ASNS, GPT2, RRM2 and NFE2L2 in DU-145 and RWPE-1 cells while the results of IHC staining exactly verified the relative protein expression levels of ASNS, GPT2, RRM2 and NFE2L2 between PCa and BPH tissues. Conclusions: This study successfully identified three ferroptosis-related molecular clusters. Besides, we developed and validated a novel ferroptosis-based molecular signature, which performed well in predicting BCRFS and tumor immune microenvironment of PCa.

CX3CR1 Acts as a Protective Biomarker in the Tumor Microenvironment of Colorectal Cancer

The tumor microenvironment (TME) plays an important role in the pathogenesis of many cancers. We aimed to screen the TME-related hub genes of colorectal adenoma (CRAD) and identify possible prognostic biomarkers. The gene expression profiles and clinical data of 464 CRAD patients in The Cancer Genome Atlas (TCGA) database were downloaded. The Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) algorithm was performed to calculate the ImmuneScore, StromalScore, and EstimateScore. Thereafter, differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein–protein interaction (PPI) analysis were performed to explore the roles of DEGs. Furthermore, univariate and multivariate Cox analyses were accomplished to identify independent prognostic factors of CRAD. CX3CR1 was selected as a hub gene, and the expression was confirmed in colorectal cancer (CRC) patients and cell lines. The correlations between CX3CR1 and tumor-infiltrating immune cells were estimated by Tumor IMmune Estimation Resource database (TIMER) and CIBERSORT analysis. Besides, we investigated the effects of coculture with THP-1-derived macrophages with HCT8 cells with low CX3CR1 expression on immune marker expression, cell viability, and migration. There were significant differences in the ImmuneScore and EstimateScore among different stages. Patients with low scores presented significantly lower lifetimes than those in the high-score group. Moreover, we recognized 1,578 intersection genes in ImmuneScore and StromalScore, and these genes were mainly enriched in numerous immune-related biological processes. CX3CR1 was found to be associated with immune cell infiltration levels, immune marker expression, and macrophage polarization. Simultaneous silencing of CX3CR1 and coculture with THP-1 cells further regulated macrophage polarization and promoted the cell proliferation and migration of CRC cells. CX3CR1 was decreased in CRAD tissues and cell lines and was related to T and N stages, tumor differentiation, and prognosis. Our results suggest that CX3CR1 contributes to the recruitment and regulation of immune-infiltrating cells and macrophage polarization in CRC and TAM-induced CRC progression. CX3CR1 may act as a prognostic biomarker in CRC.

Identification and Validation of a Novel Genomic Instability-Associated Long Non-Coding RNA Prognostic Signature in Head and Neck Squamous Cell Carcinoma

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most aggressive malignant cancers worldwide, and accurate prognostic models are urgently needed. Emerging evidence revealed that long non-coding RNAs (lncRNAs) are related to genomic instability. We sought to identify and validate a genomic instability-associated lncRNA prognostic signature to assess HNSCC patient survival outcomes.

Methods: RNA-sequencing data, somatic mutation files, and patient clinical data were downloaded from The Cancer Genome Atlas database. A total of 491 patients with completely clinical files were randomly divided into training and testing sets. In the training set, genomic instability-associated lncRNAs were screened through univariate Cox regression analyses and least absolute shrinkage and selection operator regression analyses to build a genomic instability-associated lncRNA signature (GILncSig). In addition, time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier survival curve, and clinical stratification analyses were used to evaluate the signature’s reliability. Finally, in situ hybridization experiments were performed to validate GILncSig expression levels between adjacent non-tumor tissues and tumor tissues from HNSCC patients.

Results: Four genomic instability-associated lncRNAs (AC023310.4, AC091729.1, LINC01564, and MIR3142HG) were selected for the prognostic signature. The model was successfully validated using the testing cohort. ROC analysis demonstrated its strong predictive ability for HNSCC prognosis. Univariate and multivariate Cox analyses revealed that the GILncSig was an independent predictor of prognosis. HNSCC patients with a low-risk score showed a substantially better prognosis than the high-risk groups. The in situ hybridization experiments using human HNSCC tissue revealed high GILncSig expression in HNSCC tissues compared with adjacent non-tumor tissues.

Conclusion: We developed a novel GILncSig for prognosis prediction in HNSCC patients, and the components of that signature might be therapeutic targets for HNSCC.

Transcriptome Analysis Reveals the Mechanism of Natural Ovarian Ageing

BACKGROUND

The decline in the quantity and quality of oocytes due to ovarian ageing in women is now a significant threat to reproductive health today as the concept of delayed fertility becomes widespread. However, the molecular mechanisms of natural ovarian ageing have not been fully elucidated.

METHOD

Here, we used transcriptomic data from 180 normal ovarian tissues from GTEx V8 to analyze the expression profile of ovarian tissues from women with age segments of 20-29 (22 individuals), 30-39 (14 individuals), 40-49 (37 individuals), 50-59 (61 individuals), 60-69 (42 individuals), and 70-79 (4 individuals), respectively. XCELL was used to assess the infiltration score of 64 cell types of the ovary. WGCNA was used to characterize the co-expression network during the natural aging of the ovary. ClusterprofileR was used for functional enrichment analysis of co-expression modules. MsViper was used for master regulator analysis.

RESULTS

The infiltration score of endothelial cells and activated antigen-presenting cells during natural ovarian ageing increased significantly at ages 30-39, 40-49, and then decreased, whereas CD4+ Tcm increased with age. WGCNA identified six co-expression modules from ovarian tissue transcriptomic data species. The red module was significantly and positively correlated with senescence and CD4+ Tcm, and the turquoise module was significantly and positively correlated with Endothelial Cells. We further explored ovarian tissue for women aged 20-29 and 30-39 years. The GSEA results showed that the Chemokine signaling pathway was significantly activated in the 30-39-year-old group, while Oocyte meiosis was significantly inhibited. Finally, the results of msviper found that transcription factors such as KDM1A, PRDM5, ZNF726, PPARG, FOXJ2, and GLI2 were mainly activated in the 20-29 years group, while VAV1, RUNX3, ZC3H12D, MYCL, and IRF5 were mainly activated in the 30-39 years group and that these transcription factor activities were diagnostic of natural ovarian ageing (AUC: 0.65-0.71).

CONCLUSION

Natural ageing of the ovary is significantly correlated with immune cell infiltration and activation of inflammation-related signaling pathways, with inflammation levels reaching a maximum during early ovarian ageing (30-39, 40-49) and then gradually decreasing after that. These studies provide a research basis for exploring the mechanisms of natural ovarian ageing.

Ubiquitination-Related Molecular Subtypes and a Novel Prognostic Index for Bladder Cancer Patients

Objective: To develop and validate ubiquitination-related molecular subtypes and a novel prognostic index using ubiquitination-related genes (URGs) for patients with bladder cancer (BCa).

Materials and Methods: We downloaded the clinical data and transcriptome data of BCa from TCGA and GEO database. Consensus clustering analysis was conducted to identify ubiquitination-related molecular subtypes for BCa. Besides, we performed univariate and multivariate Cox regression analysis to develop a novel prognostic URGs-related index for BCa. We conducted internal and external verification in TCGA cohort and GEO cohort, respectively. Furthermore, the associations of ubiquitination-related molecular subtypes and prognostic index with tumor immune environment were also investigated.

Results: A total of four ubiquitination-related molecular subtypes of BCa were finally identified. These four molecular subtypes had significantly different clinical characteristics, prognosis, PD-L1 expression level and tumor microenvironment. Besides, we developed a novel prognostic index using six URGs (including HLA-A, TMEM129, UBE2D1, UBE2N, UBE2T and USP5). The difference in OS between high and low-risk group was statistically significant in training cohort, testing cohort, and validating cohort. The area under ROC curve (AUC) for OS prediction was 0.736, 0.723, and 0.683 in training cohort, testing cohort, and validating cohort, respectively. Multivariate survival analysis showed that this index was an independent predictor for OS. This prognostic index was especially suitable for subtype 1 and 3, older, male, high grade, AJCC stage III-IV, stage N0, stage T3-4 BCa patients.

Conclusions: This study identified a total of four ubiquitination-related molecular subtypes with significantly different tumor microenvironment, prognosis, clinical characteristics and PD-L1 expression level. Besides, a novel ubiquitination-related prognostic index for BCa patients was developed and successfully verified, which performed well in predicting prognosis of BCa.

A Novel Cancer Stemness-Related Signature for Predicting Prognosis in Patients with Colon Adenocarcinoma

Objective

To explore the cancer stemness features and develop a novel cancer stemness-related prognostic signature for colon adenocarcinoma (COAD).

Methods

We downloaded the mRNA expression data and clinical data of COAD from TCGA database and GEO database. Stemness index, mRNAsi, was utilized to investigate cancer stemness features. Weighted gene coexpression network analysis (WGCNA) was used to identify cancer stemness-related genes. Univariate and multivariate Cox regression analyses were applied to construct a prognostic risk cancer stemness-related signature. We then performed internal and external validation. The relationship between cancer stemness and COAD immune microenvironment was investigated.

Results

COAD patients with higher mRNAsi score or EREG-mRNAsi score have significant longer overall survival (OS). We identified 483 differently expressed genes (DEGs) between the high and low mRNAsi score groups. We developed a cancer stemness-related signature using fifteen genes (including RAB31, COL6A3, COL5A2, CCDC80, ADAM12, VGLL3, ECM2, POSTN, DPYSL3, PCDH7, CRISPLD2, COLEC12, NRP2, ISLR, and CCDC8) for prognosis prediction of COAD. Low-risk score was associated with significantly preferable OS in comparison with high-risk score, and the area under the ROC curve (AUC) for OS prediction was 0.705. The prognostic signature was an independent predictor for OS of COAD. Macrophages, mast cells, and T helper cells were the vital infiltration immune cells, and APC costimulation and type II IFN response were the vital immune pathways in COAD.

Conclusions

We developed and validated a novel cancer stemness-related prognostic signature for COAD, which would contribute to understanding of molecular mechanism in COAD.

Angiogenesis-Related Molecular Subtypes and a Novel Prognostic Signature in Clear Cell Renal Cell Carcinoma Patients

Background

This study aimed to develop and validate a novel angiogenesis-related gene (ARG) signature and molecular subtypes by bioinformatics analysis.

Materials and Methods

The transcriptome data and clinical data were obtained from TCGA and ICGC database. We performed consensus clustering analysis to identify angiogenesis molecular subtypes for ccRCC. Univariate and multivariate Cox regression analyses were used to develop a novel ARG-related signature as a prognostic biomarker for ccRCC. Internal and external validation were then performed in TCGA and ICGC cohort, respectively.

Results

We identified a total of two angiogenesis molecular subtypes of ccRCC. The overall survival (OS) of subtype 1 ccRCC was significantly decreased compared with that of subtype 2 ccRCC (P=0.001). These two molecular subtypes have significantly different tumor microenvironment and immune checkpoint inhibitor sensitivities (P<0.05). Besides, we developed a novel signature based on three ARGs (including MSX1, TIMP1 and JAG2) for subtype 1 ccRCC. The difference in OS between high- and low-risk group was statistically significant in training cohort (P=0.009), test cohort (P=0.024), the whole type 1 cohort (P<0.001), and validation cohort (P=0.041). The AUC for one-year OS prediction was 0.732, 0.710, 0.725, and 0.645 in training cohort, test cohort, the whole type 1 cohort, and validation cohort, respectively. Independent prognostic analysis showed that this signature was an independent predictor for OS of subtype 1 ccRCC (P=0.028914). The power of this prognostic signature was superior to other signatures reported in previous studies.

Conclusion

We developed and successfully validated a novel ARG signature for predicting prognosis of subtype 1 ccRCC, which was superior to several previous signatures.

The landscape of tumors-infiltrate immune cells in papillary thyroid carcinoma and its prognostic value

Introduction

Thyroid cancer is a very common malignant tumor in the endocrine system, while the incidence of papillary thyroid carcinoma (PTC) throughout the world also shows a trend of increase year by year. In this study, we constructed two models: ICIscore and Riskscore. Combined with these two models, we can make more accurate and reasonable inferences about the prognosis of PTC patients.

Methods

We selected 481 PTC samples from TCGA and 147 PTC samples from GEO (49 samples in GSE33630, 65 samples in GSE35570 and 33 samples in GSE60542). We performed consistent clustering for them and divided them into three subgroups and screened differentially expressed genes from these three subgroups. Then we divided the differential genes into three subtypes. We also distinguished the up-regulated and down-regulated genes and calculated ICIscore for each PTC sample. ICIscore consists of two parts: (1) the PCAu was calculated from up-regulated genes. (2) the PCAd was calculated from down-regulated genes. The PCAu and PCAd of each sample were the first principal component of the relevant gene. What’s more, we divided the patients into two groups and constructed mRNA prognostic signatures. Additionally we also verified the independent prognostic value of the signature.

Results

Though ICIscore, we were able to observe the relationship between immune infiltration and prognosis. The result suggests that the activation of the immune system may have both positive and negative consequences. Though Riskscore, we could make more accurate predictions about the prognosis of patients with PTC. Meanwhile, we also generated and validated the ICIscore group and Riskscore group respectively.

Conclusion

All the research results show that by combining the two models constructed, ICIscore and Riskscore, we can make a more accurate and reasonable inference about the prognosis of patients with clinical PTC patients. This suggests that we can provide more effective and reasonable treatment plan for clinical PTC patients.

C3AR1 mRNA as a Potential Therapeutic Target Associates With Clinical Outcomes and Tumor Microenvironment in Osteosarcoma

Objective: Targeting cancer-specific messenger RNAs (mRNAs) may offer novel insights into therapeutic strategies in osteosarcoma. This study aimed to discover possible osteosarcoma-specific mRNA and probe its biological functions.

Methods: Based on mRNA-seq data from the TARGET database, stromal and immune scores were estimated for each osteosarcoma sample via the ESTIMATE algorithm. Stromal and immune mRNAs were obtained via integration of differentially expressed mRNAs between high and low stromal / immune score groups. Among hub and prognostic mRNAs, C3AR1 mRNA was focused and its prognostic value was assessed. The associations between C3AR1 mRNA and immune cells were analyzed via the CIBERSORT algorithm. Its expression was verified in osteosarcoma tissues and cells by RT-qPCR and western blot. The functions of C3AR1 were investigated by a series of experiments.

Results: Low stromal and immune scores were both indicative of unfavorable outcomes for osteosarcoma patients. Eighty-eight up-regulated and seven down-regulated stromal and immune mRNAs were identified. Among 30 hub mRNAs, low expression of C3AR1 mRNA indicated worse outcomes than its high expression. There was a lower mRNA expression of C3AR1 in metastatic than non-metastatic osteosarcoma. C3AR1 mRNA was closely correlated to various immune cells such as macrophages. C3AR1 was verified to be down-regulated in osteosarcoma tissues and cells. Its overexpression suppressed proliferation, migration and invasion and induced apoptosis in osteosarcoma cells.

Conclusion: C3AR1 mRNA could be a promising therapeutic target for osteosarcoma, linked with prognosis and tumor microenvironment.

Ferroptosis-Related Gene Model to Predict Overall Survival of Ovarian Carcinoma

Background

Ovarian cancer (OC) is the eighth most common cause of cancer death and the second cause of gynecologic cancer death in women around the world. Ferroptosis, an iron-dependent regulated cell death, plays a vital role in the development of many cancers. Applying expression of ferroptosis-related gene to forecast the cancer progression is helpful for cancer treatment. However, the relationship between ferroptosis-related genes and OC patient prognosis is still vastly unknown, making it still a challenge for developing ferroptosis therapy for OC.

Methods

The Cancer Genome Atlas (TCGA) data of OC were obtained and the datasets were randomly divided into training and test datasets. A novel ferroptosis-related gene signature associated with overall survival (OS) was constructed according to the training cohort. The test dataset and ICGC dataset were used to validate this signature.

Results

We constructed a model containing nine ferroptosis-related genes, namely, LPCAT3, ACSL3, CRYAB, PTGS2, ALOX12, HSBP1, SLC1A5, SLC7A11, and ZEB1, and predicted the OS of OC in TCGA. At a suitable cutoff, patients were divided into low risk and high risk groups. The OS curves of the two groups of patients had significant differences, and the time-dependent receiver operating characteristics (ROCs) were as high as 0.664, respectively. Then, the test dataset and the ICGC dataset were used to evaluate our model, and the ROCs of test dataset were 0.667 and 0.777, respectively. In addition, functional analysis and correlation analysis showed that immune-related pathways were significantly enriched. Meanwhile, we also integrated with other clinical factors and we found the synthesized clinical factors and ferroptosis-related gene signature improved prognostic accuracy relative to the ferroptosis-related gene signature alone.

Conclusion

The ferroptosis-related gene signature could predict the OS of OC patients and improve therapeutic decision-making.