Immune Cell Infiltration Landscape of Ovarian Cancer to Identify Prognosis and Immunotherapy-Related Genes to Aid Immunotherapy

Proteomics and Bioinformatics Investigations Link Overexpression of FGF8 and Associated Hub Genes to the Progression of Ovarian Cancer and Poor Prognosis

Ovarian cancer's asymptomatic nature, high recurrence rate, and resistance to platinum-based chemotherapy highlight the need to find and characterize new diagnostic and therapeutic targets. While prior studies have linked aberrant expression of fibroblast growth factor 8 (FGF8) to various cancer types, its precise role has remained elusive. Recently, we observed that FGF8 silencing reduces the cancer-promoting properties of ovarian cancer cells, and thus, this study aimed to understand how FGF8 regulates the development of ovarian cancer. LC-MS/MS-based quantitative proteomics analysis identified 418 DEPs, and most of them were downregulated in FGF8-silenced ovarian cancer cells. Many of these DEPs are associated with cancer progression and unfavorable prognosis. To decipher the biological significance of DEPs, bioinformatics analyses encompassing gene ontology, pathway analysis, protein-protein interaction networks, and expression analysis of hub genes were carried out. Hub genes identified in the FGF8 protein network were upregulated in ovarian cancer compared to controls and were linked to poor prognosis. Subsequently, the expression of hub genes was correlated with patient survival and regulation of the tumor microenvironment. Conclusively, FGF8 and associated hub genes help in the progression of ovarian cancer, and their overexpression may lead to higher immune infiltration, poor prognosis, and poor survival.

The intratumoral microbiota biomarkers for predicting survival and efficacy of immunotherapy in patients with ovarian serous cystadenocarcinoma

BACKGROUND

Ovarian serous cystadenocarcinoma, accounting for about 90% of ovarian cancers, is frequently diagnosed at advanced stages, leading to suboptimal treatment outcomes. Given the malignant nature of the disease, effective biomarkers for accurate prediction and personalized treatment remain an urgent clinical need.

METHODS

In this study, we analyzed the microbial contents of 453 ovarian serous cystadenocarcinoma and 68 adjacent non-cancerous samples. A univariate Cox regression model was used to identify microorganisms significantly associated with survival and a prognostic risk score model constructed using LASSO Cox regression analysis. Patients were subsequently categorized into high-risk and low-risk groups based on their risk scores.

RESULTS

Survival analysis revealed that patients in the low-risk group had a higher overall survival rate. A nomogram was constructed for easy visualization of the prognostic model. Analysis of immune cell infiltration and immune checkpoint gene expression in both groups showed that both parameters were positively correlated with the risk level, indicating an increased immune response in higher risk groups.

CONCLUSION

Our findings suggest that microbial profiles in ovarian serous cystadenocarcinoma may serve as viable clinical prognostic indicators. This study provides novel insights into the potential impact of intratumoral microbial communities on disease prognosis and opens avenues for future therapeutic interventions targeting these microorganisms.

Prognostic and immunological role of FDX1 in pan-cancer: an in-silico analysis

Previous research has demonstrated that ferredoxin 1 (FDX1) contributes to the accumulation of toxic lipoylated dihydrolipoamide S-acetyltransferase (DLAT) and results in cuproptotic cell death. However, the role that FDX1 plays in human cancer prognosis and immunology is still not well understood. The original data was obtained from TCGA and GEO databases and integrated using R 4.1.0. The TIMER2.0, GEPIA, and BioGPS databases were used to explore FDX1 expression. The impact of FDX1 on prognosis was analyzed using the GEPIA and Kaplan-Meier Plotter databases. External validation will be performed using the PrognoScan database. FDX1 expression in different immune and molecular subtypes of human cancers was evaluated using the TISIDB database. The correlation between FDX1 expression and immune checkpoints (ICP), microsatellite instability (MSI), and tumor mutational burden (TMB) in human cancers was analyzed using R 4.1.0. The TIMER2.0 and GEPIA databases were used to study the relationship between FDX1 expression and tumor-infiltrating immune cells. With the c-BioPortal database, we investigated the genomic alterations of FDX1. Pathway analysis and assessment of the sensitivity potential of FDX1-related drugs were also performed. Using the UALCAN database, we analyzed the differential expression of FDX1 in KIRC (kidney renal clear cell carcinoma) with different clinical features. Coexpression networks of FDX1 were analyzed using LinkedOmics. In general, FDX1 was expressed differently in different types of cancer in humans. Expression of FDX1 was strongly correlated with patient prognosis, ICP, MSI, and TMB. FDX1 was also participated in immune regulation and the tumor microenvironment. Coexpression networks of FDX1 were primarily involved in oxidative phosphorylation regulation. Pathway analysis revealed that the expression of FDX1 was correlated to cancer-related and immune-related pathways. FDX1 has the potential to serve as a biomarker for pan-cancer prognosis and immunology, as well as a novel target for tumor therapy.

Cuproptosis key gene FDX1 is a prognostic biomarker and associated with immune infiltration in glioma

Recent studies have found that the protein encoded by the FDX1 gene is involved in mediating Cuproptosis as a regulator of protein lipoylation and related to immune response process of tumors. However, the specific biological function of FDX1 in glioma is currently unclear. To explore the potential function of FDX1, this study explored the correlation between the expression of FDX1 in cancers and survival prognosis by analyzing the public databases of GEPIA and Cbioportal. Immune infiltration was analyzed by the TIMER2.0 database in tumors. The possible biological processes and functions of FDX1-related in glioma were annotated through gene enrichment. Relationship between Cuproptosis and autophagy was explored through gene co-expression studies. Summary and conclusions of this study: (1) FDX1 is highly expressed in gliomas and associated with poor prognosis in low-grade gliomas (LGG). (2) Gene annotation indicates that FDX1 is mainly involved in the tumor protein lipoylation and cell death. (3) FDX1 expression is positively correlated with the infiltration of immune cells. (4) LIPT2 and NNAT, two other genes involved in lipoylation, may be unidentified marker gene for Cuproptosis. And the Cuproptosis genes related to FDX1 were positively correlated with the expression of autophagy marker genes Atg5, Atg12, and BECN-1. This evidence suggests that there may be some interaction between FDX1 mediated Cuproptosis and autophagy. In summary, FDX1 may serve as a potential immunotherapy target and prognostic marker for Glioma.

The current landscape of predictive and prognostic biomarkers for immune checkpoint blockade in ovarian cancer

Immune checkpoint blockade (ICB) therapy has evoked a prominent shift in anticancer therapy. Durable clinical antitumor activity to ICB has been observed in patients with ovarian cancer (OC). However, only a subset of patients derive clinical benefit, and immune-related adverse events (irAEs) caused by ICB therapy can lead to permanent tissue damage and even fatal consequences. It is thus urgent to develop predictive biomarkers to optimize patient outcomes and minimize toxicity risk. Herein, we review current predictive and prognostic biomarkers for checkpoint immunotherapy in OC and highlight emerging biomarkers to guide treatment with ICB. The prevalent biomarkers, such as PD-L1 expression status, tumor-infiltrating lymphocytes, mutational burden, and immune gene signatures, are further discussed. We provide a state-of-the-art survey on prognostic and predictive biomarkers for checkpoint immunotherapy and offer valuable information for guiding precision immunotherapy

Identification of Tumor Microenvironment Scoring Scheme Based on Bioinformatics Analysis of Immune Cell Infiltration Pattern of Ovarian Cancer

Background

Tumor microenvironment (TME) is the crucial mediator of tumor progression, and the TME model based on immune cell infiltration to characterize ovarian cancer is considered to be a promising strategy.

Methods

Sample data of three ovarian cancer cohorts were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The scores of 22 kinds of immune cells were calculated based on CIBERSORT, and the TME clusters (TMECs) of ovarian cancer was determined by ConsensusClusterPlus. Genomic subtype was identified by non-negative matrix factorization (NMF). A TME scoring scheme was constructed using k-means algorithm and principal component analysis (PCA) to quantify the TME infiltration pattern of individuals.

Results

Four TME subtypes of ovarian cancer samples were defined: TMEC1, TMEC2, TMEC3, and TMEC4. There were also significant differences in overall survival (OS) among the four TMEC, and the OS of TMEC3 was the longest. The difference analysis of TMEC3 and the other three TMECs respectively identified the DEGs and took the intersection, and 585 DEGs were obtained. Two genomic subtypes were identified by NMF based on the expression of 585 genes, which were called GeneC1 and GeneC2. The TME scoring scheme constructed by k-means and PCA algorithm was used to calculate the TME score of ovarian cancer in TCGA. High-TME score was significantly correlated with shorter survival time, older age, lower immunoactivated molecules, and immune checkpoint gene expression.

Conclusions

This study highlighted the complexity and diversity of TME infiltration patterns in ovarian cancer and constructed a set of TME scoring scheme to reveal TME infiltration patterns and provided new insights into the landscape of TME.

Exploration of the Immunotyping Landscape and Immune Infiltration-Related Prognostic Markers in Ovarian Cancer Patients

Background

Increasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.

Methods

The ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.

Results

Three immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.

Conclusions

Our study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.