Constructe a novel 5 hypoxia genes signature for cervical cancer

Construction of a ferroptosis and hypoxia-related gene signature in cervical cancer to assess tumour immune microenvironment and predict prognosis

BACKGROUND

This study aimed to investigate the potential role of ferroptosis/hypoxia-related genes in cervical cancer to improve early management and treatment of cervical cancer.

METHODS

All data were downloaded from public databases. Ferroptosis/hypoxia-related genes associated with cervical cancer prognosis were selected to construct a risk score model. The relationship between risk score and clinical features, immune microenvironment and prognosis were analysed.

RESULTS

Risk score model was constructed based on eight signature genes. Drug prediction analysis showed that bevacizumab and cisplatin were related to vascular endothelial growth factor A. Risk score, as an independent prognostic factor of cervical cancer, had a good survival prediction effect. The two groups differed significantly in degree of immune cell infiltration, gene expression, tumour mutation burden and somatic variation.

CONCLUSIONS

We developed a novel prognostic gene signature combining ferroptosis/hypoxia-related genes, which provides new ideas for individual treatment of cervical cancer.

Identification of an inflammatory response-related gene prognostic signature and immune microenvironment for cervical cancer

Background: Cervical cancer (CC) is the fourth most common cancer among women worldwide. As part of the brisk cross-talk between the host and the tumor, prognosis can be affected through inflammatory responses or the tumor microenvironment. However, further exploration of the inflammatory response-related genes that have prognostic value, microenvironment infiltration, and chemotherapeutic therapies in CC is needed. Methods: The clinical data and mRNA expression profiles of CC patients were downloaded from a public database for this study. In the TCGA cohort, a multigene prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) and Cox analyses. CC patients from the GEO cohort were used for validation. K‒M analysis was used to compare overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors of OS. The immune cell infiltration and immune-related functional score were calculated by single-sample gene set enrichment analysis (GSEA). Immunohistochemistry was utilized to validate the protein expression of prognostic genes in CC tissues. Results: A genetic signature model associated with the inflammatory response was built by LASSO Cox regression analysis. Patients in the high-risk group had a significantly lower OS rate. The predictive ability of the prognostic genes was evaluated by means of receiver operating characteristic (ROC) curve analysis. The risk score was confirmed to be an independent predictor of OS by univariate and multivariate Cox analyses. The immune status differed between the high-risk and low-risk groups, and the cancer-related pathways were enriched in the high-risk group according to functional analysis. The risk score was significantly related to tumor stage and immune infiltration type. The expression levels of five prognostic genes (LCK, GCH1, TNFRSF9, ITGA5, and SLC7A1) were positively related to sensitivity to antitumor drugs. Additionally, the expression of prognostic genes was significantly different between CC tissues and myoma patient cervix (non-tumorous) tissues in the separate sample cohort. Conclusion: A model consisting of 5 inflammation-related genes can be used to predict prognosis and influence immune status in CC patients. Furthermore, the inhibition or enhancement of these genes may become a novel alternative therapy.

Mechanistic analysis of Th2-type inflammatory factors in asthma

Background

The main pathological features of asthma are widespread chronic inflammation of the airways and restricted ventilation due to airway remodeling, which involves changes in a range of regulatory pathways. While the role of T helper type 2 (Th2)-related inflammatory factors in this process is known, the detailed understanding of how genes affect protein functions during airway remodeling is still lacking. This study aims to fill this knowledge gap by integrating gene expression data and protein function analysis, providing new scientific insights for a deeper understanding of the mechanisms of airway remodeling and for further development of asthma treatment strategies.

Methods

In this study, the mechanism of Th2-related inflammatory factors in tracheal remodeling was studied through differentially expressed gene (DEG) screening, enrichment analysis, protein-protein interaction (PPI) network construction, machine learning, and the construction of a line graph model.

Results

Our study revealed that S100A14, KRT6A, S100A2, ABCA13, UBE2C, RASSF10, PSCA, PLAT, and TIMP1 may be the key genes for airway remodeling; epithelial-mesenchymal transition (EMT)-related genes GEM, TPM4, SLC6A8, and SNTB1 may be involved in airway remodeling due to asthma; IL6 may affect the occurrence of airway remodeling by binding to UBE2C protein or by regulating GEM genes, respectively; IL6 and IL9 may affect the occurrence of airway remodeling by regulating the downstream Toll-like receptor (TLR) signaling pathway and thus IL6 and IL9 may influence the occurrence of tracheal remodeling by regulating downstream TLR signaling pathways.

Conclusions

This study further mined the asthma gene microarray database through bioinformatics analysis and identified key genes and important pathways affecting airway remodeling in asthma patients, providing new ideas to uncover the mechanism of airway remodeling due to asthma and then seek new therapeutic targets.

The Role and Potential Mechanism of Complement Factor D in Fibromyalgia Development

Purpose

Fibromyalgia (FM) is a syndrome characterized by chronic musculoskeletal pain. Its clinical symptoms include both somatic and psychiatric symptoms, making the treatment of FM extremely challenging. The cause of FM is still unknown, and some patients do not improve their symptoms even after long-term active treatment. Thus, the development of new targeted therapies is important to reduce pain and improve quality of life for FM patients.

Methods

In this study, we screened genes and secreted factors that play key roles in FM through bioinformatics and big data analysis. Furthermore, we performed CCK-8, qRT-PCR, glucose, ATP and lactate content testing, dual luciferase reporter gene assay to investigate the potential mechanism of complement factor D in fibromyalgia development.

Results

In bioinformatics and big data analysis, we identified CFD was negatively correlated with the pro-inflammatory factor IL-6 and positively correlated with the anti-inflammatory factor IL-4, which suggested that CFD may be an anti-inflammatory factor. Through cellular assays, we verified that CFD reversed the decrease in IL-4 expression and the increase in IL-6 expression in BV2 cells caused by ATP.

Conclusion

In summary, based on bioinformatic methods and big data mining we obtained a new target CFD for FM, and further experiments verified that CFD has significant inhibition of ATP-induced neuropathic pain. These findings provide a new theoretical basis for the clinical translation of CFD.

Glioblastoma glycolytic signature predicts unfavorable prognosis, immunological heterogeneity, and ENO1 promotes microglia M2 polarization and cancer cell malignancy

Glioblastomas are the most malignant brain tumors, whose progress was promoted by aberrate aerobic glycolysis. The immune environment was highly engaged in glioblastoma formation, while its interaction with aerobic glycolysis remained unclear. Herein, we build a 7-gene Glycolytic Score (GS) by Elastic Net in the training set and two independent validating sets. The GS predicted malignant features and poor survival with good performances. Immune functional analyses and Cibersort calculation identified depressed T cells, B cells, natural killer cells immunity, and high immunosuppressive cell infiltration in the high-GS group. Also, high expressions of the immune-escape genes were discovered. Subsequently, the single-cell analyses validated the glycolysis-related immunosuppression. The functional results manifested the high-GS neoplastic cells' association with T cells, NK cells, and macrophage function regulation. The intercellular cross-talk showed strong associations between high-GS neoplastic cells and M2 macrophages/microglia in several immunological pathways. We finally confirmed that ENO1, the key gene of the GS, promoted M2 microglia polarization and glioblastoma cell malignant behaviors via immunofluorescence, clone formation, CCK8, and transwell rescue experiments. These results indicated the interactions between cancerous glycolysis and immunosuppression and glycolysis' role in promoting glioblastoma progression. Conclusively, we built a robust model and discovered strong interaction between GS and immune, shedding light on prognosis management improvement and therapeutic strategies development for glioblastoma patients.

Autophagy-related IFNG is a prognostic and immunochemotherapeutic biomarker of COAD patients

Background

Numerous studies have shown autophagy affects cellular immune responses. This study aims to explore prognosis and immunotherapeutic biomarkers related to autophagy in colon adenocarcinoma (COAD).

Methods

Based on R software, we performed the ssGSEA, differential expression analysis, Kaplan-Meier survival analysis, correlation analysis, and enrichment analysis. For wet experiment, we did qRT-PCR, immunohistochemistry and CCK-8 experiments.

Results

Using autophagy-related genes (ARGs) and the ssGSEA, COAD patients were divided into low and high autophagy groups. For immune score, stromal score, tumor purity, tumor infiltrating immune cells, co-signaling molecules, tumor mutational burden, microsatellite instability, mismatch repair, immune-related pathways, immune signatures, somatic mutations and subtype analysis, high autophagy group might benefit more from immunotherapy. Among 232 ARGs, IFNG was generally significantly correlated with tumor immunotherapy biomarkers (PD-L1, CD8A and cytotoxic T lymphocytes (CTL)). The disease-free survival of high IFNG group was significantly longer than that of low group. On above-mentioned immune-related research, the high IFNG group reached the same conclusion. The qRT-PCR and IHC analysis confirmed that IFNG was significantly higher expressed in dMMR samples compared to pMMR samples. For chemotherapy, the autophagy and IFNG were significantly negatively related to the chemosensitivity to cisplatin; IFNG inhibitor glucosamine increased cisplatin chemoresistance while IFNG increased cisplatin chemosensitivity; IFNG could reverse glucosamine induced chemoresistance. The functional enrichment analysis of IFNG, PD-L1, CD8A and 20 similar proteins were related to the activation of the immune system. The GSEA and ceRNA network partly described interaction mechanisms of IFNG with PD-L1 and CD8A.

Conclusion

Autophagy score and IFNG expression were novel immunotherapy predictive biomarkers, which might play predictive effects through the JAK-STAT signaling pathway. IFNG might be a potential targeted therapy for cisplatin resistant colon cancer. Besides, IFNG was also a prognostic indicator.

E6E7 regulates the HK2 expression in cervical cancer via GSK3β/FTO signal

BACKGROUND

Cervical cancer is one of the most common cancers in women worldwide. Hexokinase 2 (HK2) is responsible for phosphorylating glucose into glucose-6-phosphate, which is required for tumorigenesis and metastasis.

METHODS

E6E7 and FTO were exogenously expressed, and their effects on HK2 mRNA and protein levels were detected by RT-qPCR and Western blot.

RESULTS

The exogenous expression of E6E7 in SiHa and C33A cells up-regulated the mRNA and protein levels of intracellular HK2, up-regulated the total m⁶A levels, changed the expression of m⁶A proteins and activated the GSK3β transcription. The expression levels of METTL3 and WTAP were enhanced, whereas the expression of FTO and ALKBH5 were decreased. In addition, FTO down-regulated the mRNA and protein levels of HK2. FTO overexpression partially inhibited the up-regulated expression of HK2 caused by E6E7. Furthermore, FTO overexpression increased the level of HK2 pre-mRNA in the nucleus and decreased the level of mature HK2 mRNA in the cytoplasm. We also found that GSK3β overexpression enhanced FTO ubiquitination and decreased FTO protein levels.

CONCLUSION

This study found that E6E7 oncogene activates the transcription of GSK3β; GSK3β can promote the ubiquitination-proteasomal degradation of FTO and reduce the level of FTO protein; FTO inhibits the maturation and translation of HK2 mRNA by retaining HK2 pre-mRNA in the nucleus.

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Human leukocyte antigen G (HLA-G) is a potential checkpoint molecule that plays a key role in cervical carcinogenesis. The purpose of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of cervical cancer patients, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, namely, SiHa and HeLa, with stable overexpression of HLA-G by RNA sequencing (RNA-seq). The biological functions of these HLA-G-driven DEGs were analysed by GO enrichment and KEGG pathway using the “clusterProfiler” package. The protein-protein interactions (PPIs) were assessed using the STRING database. The prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. After the TCGA-CESC cohort was randomly divided into training set and testing set, and a prognostic risk model was constructed by LASSO and stepwise multivariate Cox regression analysis in training set and validated in testing set or in different types of cervical cancer set. The predictive ability of the prognostic risk model or nomogram was evaluated by a series of bioinformatics methods. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB pathway, steroid biosynthesis, and MAPK pathway. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The receiver operating characteristic (ROC) curve of this prognostic model with an area under the curve (AUC) was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer, including the keratinizing squamous cell carcinoma, non-keratinizing squamous cell carcinoma, squamous cell neoplasms, non-squamous cell neoplasms set. Single-sample gene set enrichment (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis confirmed that this signature influence tumour microenvironment and immune checkpoint blockade. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

Establishment of a risk model by integrating hypoxia genes in predicting prognosis of esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) has a dismal prognosis, and hypoxia plays a key role in metastasis and proliferation of ESCC. Thus, we aimed to develop a hypoxia-based gene signature to assist in the treatment decisions and prognosis.

METHODS

We performed consensus clustering analysis on samples from GSE53625 dataset from the Gene Expression Omnibus (GEO) database and used weighted gene co-expression network analysis to filter out candidate modules, which were then intersected with differentially expressed genes from clustered subgroups to obtain hypoxia-related genes (HRGs). After that, the aforementioned genes were used to construct risk score models and validated in The Cancer Genome Atlas (TCGA) database and Cox regression analysis were used to construct a nomogram. Immunohistochemical was used to detect protein expression levels of relevant genes. Moreover, the relationship between risk scores and tumor microenvironment was explored.

RESULTS

A hypoxia risk model containing six genes (PNPLA1, CARD18, IL-18, SLC37A2, ADAMTS18, and FAM83C) was constructed by screening key HRGs. Poorer prognosis in the high-risk group than in the low-risk group. And Cox regression analysis showed that risk score was an independent prognostic factor. The nomogram based on risk scores could well predict 1-, 3-, and 5-year survival. P53, Wnt, and hypoxia signaling pathways may be some regulatory mechanisms of hypoxia associated with the tumor microenvironment. In addition, we confirmed the high expression of BGN and low expression of IL-18 in ESCC tissues.

CONCLUSIONS

Our study determined the prognostic value of a 6-hypoxia gene signature and a prognostic model, providing potential prognostic predictors and therapeutic targets for ESCC.

Identification and validation of a prognostic signature related to hypoxic tumor microenvironment in cervical cancer

Background

Hypoxia is a common microenvironment condition in most malignant tumors and has been shown to be associated with adverse outcomes of cervical cancer patients. In this study, we investigated the effects of hypoxia-related genes on tumor progress to characterize the tumor hypoxic microenvironment.

Methods

We retrieved a set of hypoxia-related genes from the Molecular Signatures Database and evaluated their prognostic value for cervical cancer. A hypoxia-based prognostic signature for cervical cancer was then developed and validated using tumor samples from two independent cohorts (TCGA-CESC and CGCI-HTMCP-CC cohorts). Finally, we validated the hypoxia prediction of ccHPS score in eight human cervical cancer cell lines treated with the hypoxic and normoxic conditions, and 286 tumor samples with hypoxic category (more or less) from Gene Expression Omnibus (GEO) database with accession GSE72723.

Results

A risk signature model containing nine hypoxia-related genes was developed and validated in cervical cancer. Further analysis showed that this risk model could be an independent prognosis factor of cervical cancer, which reflects the condition of the hypoxic tumor microenvironment and its remodeling of cell metabolism and tumor immunity. Furthermore, a nomogram integrating the novel risk model and lymphovascular invasion status was developed, accurately predicting the 1-, 3- and 5-year prognosis with AUC values of 0.928, 0.916 and 0.831, respectively. These findings provided a better understanding of the hypoxic tumor microenvironment in cervical cancer and insights into potential new therapeutic strategies in improving cancer therapy.

Construction of hypoxia-immune-related prognostic model and targeted therapeutic strategies for cervical cancer

Emerging evidence indicates that hypoxia and immunity play important roles in tumorigenesis and development. However, the hypoxia-immune-related prognostic risk model has not been established in cervical cancer (CC). We aimed to construct a hypoxia-immune-based prognostic risk model with potential application in CC patient prognosis and predicting response to targeted therapy. The RNA-seq data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Hypoxia and immune status of CC patients were evaluated using the Consensus Clustering method and single sample gene set enrichment analysis (ssGSEA), respectively. The univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to establish the prognostic risk model of CC. The chemotherapy response for six chemotherapeutic agents of each CC patient was calculated according to the Genomics of Drug Sensitivity in Cancer (GDSC). And the Connectivity Map (CMap) database was performed to screen candidate small molecule drugs. In this study, we identified 7 gene signatures (P4HA2, MSMO1, EGLN1, ZNF316, IKZF3, ISCU, MYO1B) with prognostic values. And the survival time of patients with low-risk was significantly longer than those with high-risk. Meanwhile, CC patients in the high-risk group yielded higher sensitivity to five chemotherapeutic agents. And we listed ten candidate small-molecules drugs that exhibited a high correlation with the prognosis of cervical cancer. Thus, the prognostic model can accurately predict the prognosis of patients with CC and may be helpful for the development of new hypoxia-immune prognostic markers and therapeutic strategies for CC.

Comprehensive Molecular Analyses of a TNF Family-Based Gene Signature as a Potentially Novel Prognostic Biomarker for Cervical Cancer

Background

Increasing evidence suggests that tumour necrosis factor (TNF) family genes play important roles in cervical cancer (CC). However, whether TNF family genes can be used as prognostic biomarkers of CC and the molecular mechanisms of TNF family genes remain unclear.

Methods

A total of 306 CC and 13 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. We identified differentially expressed TNF family genes between CC and normal samples and subjected them to univariate Cox regression analysis for selecting prognostic TNF family genes. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to screen genes to establish a TNF family gene signature. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the TNF family gene signature. Finally, methylation and copy number variation data of CC were used to analyse the potential molecular mechanisms of TNF family genes.

Results

A total of 26 differentially expressed TNF family genes were identified between the CC and normal samples. Next, a TNF family gene signature, including CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 was constructed based on univariate Cox, LASSO, and multivariate Cox regression analyses. The TNF family gene signature was related to age, pathological stages M and N, and could predict patient survival independently of clinical factors. Moreover, KEGG enrichment analysis suggested that the TNF family gene signature was mainly involved in the TGF-β signaling pathway, and the TNF family gene signature could affect the immunotherapy response. Finally, we confirmed that the mRNA expressions of CD27, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 were upregulated in CC, while that of EDA was downregulated. The mRNA expressions of CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 might be influenced by gene methylation and copy number variation.

Conclusion

Our study is the first to demonstrate that CD27, EDA, TNF, TNFRSF12A, TNFRSF13C, and TNFRSF9 might be used as prognostic biomarkers of CC and are associated with the immunotherapy response of CC.

Construction of novel hypoxia-related gene model for prognosis and tumor microenvironment in endometrial carcinoma

INTRODUCTION

Endometrial cancer is currently one of the three most common female reproductive cancers, which seriously threatens women's lives and health. Hypoxia disrupts the tumor microenvironment, thereby affecting tumor progression and drug resistance.

METHODS

We established hypoxia-related gene model to predict patient prognosis and 1-, 3-, and 5-year overall survival rates. Then, the expression level of hypoxia-related genes and survival data were extracted for comprehensive analysis by Cox regression analysis, and the model was established.

RESULTS

We analyzed the survival and prognosis of patients in the high and low-risk groups. The Kaplan-Meier curve showed that the low-risk group is associated with a better survival rate. The 1-, 3-, and 5-year AUC values of the model were 0.680, 0.698, and 0.687, respectively. Finally, we found that LAG3 may be a potential immune checkpoint for endometrial cancer.

CONCLUSION

We found four hypoxia-related genes (ANXA2, AKAP12, NR3C1, and GPI) associated with prognosis. The hypoxia-related gene model can also predict prognosis and tumor microenvironment in endometrial cancer.