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

Aberrant overexpression of myosin 1b in glioblastoma promotes angiogenesis via VEGF-myc-myosin 1b-Piezo1 axis

Glioblastoma (GBM) is the most aggressive intracranial malignancy with poor prognosis. Enhanced angiogenesis is an essential hallmark of GBM, which demonstrates extensive microvascular proliferation and abnormal vasculature. Here, we uncovered the key role of myosin 1b in angiogenesis and vascular abnormality in GBM. Myosin 1b is upregulated in GBM endothelial cells (ECs) compared to the paired nonmalignant brain tissue. In our study, we found that myosin 1b promotes migration, proliferation, and angiogenesis of human/mouse brain ECs. We also found that myosin 1b expression in ECs can be regulated by vascular endothelial growth factor (VEGF) signaling through myc. Moreover, myosin 1b promotes angiogenesis via Piezo1 by enhancing Ca2+ influx, in which process VEGF can be the trigger. In conclusion, our results identified myosin 1b as a key mediator in promoting angiogenesis via mechanosensitive ion channel component 1 (Piezo1) and suggested that VEGF/myc signaling pathway could be responsible for driving the changes of myosin 1b overexpression in GBM ECs.

Genomic nursing science revealed the prolyl 4-hydroxylase subunit alpha 2 as a significant biomarker involved in osteosarcoma

Backgrounds

This study aims to explore the clinical value of P4HA2 (prolyl 4-hydroxylase subunit alpha 2) in Osteosarcoma (OSC), and assess its potential to provide directions and clues for the practice of precision nursing.

Methods

The GSE73166 and GSE16088 datasets were used to explore the P4HA2 expression in OSC. We then used the clinical data of patients obtaining from TARGET database to assess the prognostic value of P4HA2 in OSC. We also evaluated the predictive value of prognostic model based on P4HA2-related genes. Further, GSEA analysis was performed to explore related pathways.

Results

The P4HA2 mRNA expression was higher in OSC than that in normal tissues and other bone cancer samples. Survival analysis found that P4HA2 high expression caused poor overall survival (OS) of patients with OSC and P4HA2 presented a favorable performance for predicting OS. Specifically, P4HA2 high expression statistically influenced the OS of patients with age≥15 years old and those with or without metastasis. Cox regression analysis indicated the independent prognostic value of P4HA2 in OSC, and nomogram analysis revealed its significant contribution to the survival probability of patients. We further established a prognostic model based on P4HA2-related genes, finding that prognostic model had a good prediction ability on OS. These results supported the clinical significance of P4HA2 in OSC. GSEA analysis suggested that P4HA2 was significantly related to the MAPK signaling pathway. In addition, P4HA2-associated natural killer cell-mediated cytotoxicity and T cell receptor signaling pathway were also predicted.

Conclusions

This study revealed that P4HA2 can serve as an important prognostic biomarker for OSC patients, and it may become a promising therapeutic target in OSC treatment.

Integrating Single-Cell RNA-Seq and Bulk RNA-Seq to Construct a Novel γδT Cell-Related Prognostic Signature for Human Papillomavirus-Infected Cervical Cancer

BACKGROUND

Gamma delta (γδ) T cells play dual roles in human tumors, with both antitumor and tumor-promoting functions. However, the role of γδT cells in HPV-infected cervical cancer is still undetermined. Therefore, we aimed to identify γδT cell-related prognostic signatures in the cervical tumor microenvironment.

METHODS

Single-cell RNA-sequencing (scRNA-seq) data, bulk RNA-seq data, and corresponding clinical information of cervical cancer patients were obtained from the TCGA and GEO databases. The Seurat R package was used for single-cell analysis, and machine learning algorithms were used to screen and construct a γδT cell-related prognostic signature. Real-time quantitative PCR (RT-qPCR) was performed to detect the expression of prognostic signature genes.

RESULTS

Single-cell analysis indicated distinct populations of γδT cells between HPV-positive (HPV+) and HPV-negative (HPV-) cervical cancers. A trajectory analysis indicated γδT cells clustered into differential clusters with the pseudotime. High-dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) identified the key γδT cell-related gene modules. Bulk RNA-seq analysis also demonstrated the heterogeneity of immune cells, and the γδT-score was positively associated with inflammatory response and negatively associated with MYC stemness. Eight γδT cell-related hub genes (GTRGs), including ITGAE, IKZF3, LSP1, NEDD9, CLEC2D, RBPJ, TRBC2, and OXNAD1, were selected and validated as a prognostic signature for cervical cancer.

CONCLUSION

We identified γδT cell-related prognostic signatures that can be considered independent factors for survival prediction in cervical cancer.

Identification and Validation of the Anoikis-Related Gene Signature as a Novel Prognostic Model for Cervical Squamous Cell Carcinoma, Endocervical Adenocarcinoma, and Revelation Immune Infiltration

Background and Objectives: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are malignant disorders with adverse prognoses for advanced patients. Anoikis, which is involved in tumor metastasis, facilitates the survival and separation of tumor cells from their initial site. Unfortunately, it is rarely studied, and in the literature, studies have only addressed the prognosis character of anoikis for patients with CESC. Materials and Methods: We utilized anoikis-related genes (ANRGs) to construct a prognostic signature in CESC patients that were selected from the Genecards and Harmonizome portals. Furthermore, we revealed the underlying clinical value of this signature for clinical maneuvers by providing clinical specialists with an innovative nomogram on the basis of ANRGs. Finally, we investigated the immune microenvironment and drug sensitivity in different risk groups. Results: We screened six genes from fifty-eight anoikis-related differentially expressed genes in the TCGA-CESC cohort, and we constructed a prognostic signature. Then, we built a nomogram combined with CESC clinicopathological traits and risk scores, which demonstrated that this model may improve the prognosis of CESC patients in clinical therapy. Next, the prognostic risk scores were confirmed to be an independent prognostic indicator. Additionally, we programmed a series of analyses, which included immune infiltration analysis, therapy-related analysis, and GSVA enrichment analysis, to identify the functions and mechanisms of the prognostic models during the progression of cancer in CESC patients. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the six ANRGs. Conclusions: The present discovery verified that the predictive 6-anoikis-related gene (6-ANRG) signature and nomogram serve as imperative factors that might notably impact a CESC patient's prognosis, and they may be able to provide new clinical evidence to assume the role of underlying biological biomarkers and thus become indispensable indicators for prospective diagnoses and advancing therapy.

A risk prediction model mediated by genes of APOD/APOC1/SQLE associates with prognosis in cervical cancer

Cervical cancer is one of the most common gynecological malignancies. Due to the high heterogeneity of cervical cancer accelerating cancer progression, it is necessary to identify new prognostic markers and treatment regimens for cervical cancer to improve patients' survival rates. We purpose to construct and verify a risk prediction model for cervical cancer patients. Based on the analysis of data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), differences of genes in normal and cancer samples were analyzed and then used analysis of WGCNA along with consistent clustering to construct single-factor + multi-factor risk models. After regression analysis, the target genes were obtained as prognostic genes and prognostic risk models were constructed, and the validity of the risk model was confirmed using the receiver operating characteristic curve (ROC) and Kaplan-Meier curve. Subsequently, the above model was verified on the GSE44001 data validation followed by independent prognostic analysis. Enrichment analysis was conducted by grouping the high and low risks of the model. In addition, differences in immune analysis (immune infiltration, immunotherapy), drug sensitivity, and other levels were counted by the high and low risks groups. In our study, three prognostic genes including APOD, APOC1, and SQLE were obtained, and a risk model was constructed along with validation based on the above-mentioned analysis. According to the model, immune correlation and immunotherapy analyses were carried out, which will provide a theoretical basis and reference value for the exploration and treatment of cervical cancer.

A novel hypoxia- and lactate metabolism-related signature to predict prognosis and immunotherapy responses for breast cancer by integrating machine learning and bioinformatic analyses

Background

Breast cancer is the most common cancer worldwide. Hypoxia and lactate metabolism are hallmarks of cancer. This study aimed to construct a novel hypoxia- and lactate metabolism-related gene signature to predict the survival, immune microenvironment, and treatment response of breast cancer patients.

Methods

RNA-seq and clinical data of breast cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Hypoxia- and lactate metabolism-related genes were collected from publicly available data sources. The differentially expressed genes were identified using the “edgeR” R package. Univariate Cox regression, random survival forest (RSF), and stepwise multivariate Cox regression analyses were performed to construct the hypoxia-lactate metabolism-related prognostic model (HLMRPM). Further analyses, including functional enrichment, ESTIMATE, CIBERSORTx, Immune Cell Abundance Identifier (ImmuCellAI), TIDE, immunophenoscore (IPS), pRRophetic, and CellMiner, were performed to analyze immune status and treatment responses.

Results

We identified 181 differentially expressed hypoxia-lactate metabolism-related genes (HLMRGs), 24 of which were valuable prognostic genes. Using RSF and stepwise multivariate Cox regression analysis, five HLMRGs were included to establish the HLMRPM. According to the medium-risk score, patients were divided into high- and low-risk groups. Patients in the high-risk group had a worse prognosis than those in the low-risk group (P < 0.05). A nomogram was further built to predict overall survival (OS). Functional enrichment analyses showed that the low-risk group was enriched with immune-related pathways, such as antigen processing and presentation and cytokine-cytokine receptor interaction, whereas the high-risk group was enriched in mTOR and Wnt signaling pathways. CIBERSORTx and ImmuCellAI showed that the low-risk group had abundant anti-tumor immune cells, whereas in the high-risk group, immunosuppressive cells were dominant. Independent immunotherapy datasets (IMvigor210 and GSE78220), TIDE, IPS and pRRophetic analyses revealed that the low-risk group responded better to common immunotherapy and chemotherapy drugs.

Conclusions

We constructed a novel prognostic signature combining lactate metabolism and hypoxia to predict OS, immune status, and treatment response of patients with breast cancer, providing a viewpoint for individualized treatment.