Identification of Immune and Hypoxia Risk Classifier to Estimate Immune Microenvironment and Prognosis in Cervical Cancer

TGFBI: A novel therapeutic target for cancer

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

HIF-2α-dependent TGFBI promotes ovarian cancer chemoresistance by activating PI3K/Akt pathway to inhibit apoptosis and facilitate DNA repair process

Hypoxia-mediated chemoresistance plays a crucial role in the development of ovarian cancer (OC). However, the roles of hypoxia-related genes (HRGs) in chemoresistance and prognosis prediction and theirs underlying mechanisms remain to be further elucidated. We intended to identify and validate classifiers of hub HRGs for chemoresistance, diagnosis, prognosis as well as immune microenvironment of OC, and to explore the function of the most crucial HRG in the development of the malignant phenotypes. The RNA expression and clinical data of HRGs were systematically evaluated in OC training group. Univariate and multivariate Cox regression analysis were applied to construct hub HRGs classifiers for prognosis and diagnosis assessment. The relationship between classifiers and chemotherapy response and underlying pathways were detected by GSEA, CellMiner and CIBERSORT algorithm, respectively. OC cells were cultured under hypoxia or transfected with HIF-1α or HIF-2α plasmids, and the transcription levels of TGFBI were assessed by quantitative PCR. TGFBI was knocked down by siRNAs in OC cells, CCK8 and in vitro migration and invasion assays were performed to examine the changes in cell proliferation, motility and metastasis. The difference in TGFBI expression was examined between cisplatin-sensitive and -resistant cells, and the effects of TGFBI interference on cell apoptosis, DNA repair and key signaling molecules of cisplatin-resistant OC cells were explored. A total of 179 candidate HRGs were extracted and enrolled into univariate and multivariate Cox regression analysis. Six hub genes (TGFBI, CDKN1B, AKAP12, GPC1, TGM2 and ANGPTL4) were selected to create a HRGs prognosis classifier and four genes (TGFBI, AKAP12, GPC1 and TGM2) were selected to construct diagnosis classifiers. The HRGs prognosis classifier could precisely distinguish OC patients into high-risk and low-risk groups and estimate their clinical outcomes. Furthermore, the high-risk group had higher percentage of Macrophages M2 and exhibited higher expression of immunecheckpoints such as PD-L2. Additionally, the diagnosis classifiers could accurately distinguish OC from normal samples. TGFBI was further verified as a specific key target and demonstrated that its high expression was closely correlated with poor prognosis and chemoresistance of OC. Hypoxia upregulated the expression level of TGFBI. The hypoxia-induced factor HIF-2α but not HIF-1α could directly bind to the promoter region of TGFBI, and facilitate its transcription level. TGFBI was upregulated in cisplatin-sensitive and resistant ovarian cancer cells in a cisplatin time-dependent manner. TGFBI interference downregulated DNA repair-related markers (p-p95/NBS1, RAD51, p-DNA-PKcs, DNA Ligase IV and Artemis), apoptosis-related marker (BCL2) and PI3K/Akt pathway-related markers (PI3K-p110 and p-Akt) in cisplatin-resistant OC cells. In summary, the HRGs prognosis risk classifier could be served as a predictor for OC prognosis and efficacy evaluation. TGFBI, upregulated by HIF-2α as an HRG, promoted OC chemoresistance through activating PI3K/Akt pathway to reduce apoptosis and enhance DNA damage repair pathway.

Candidate Genes and Pathways in Cervical Cancer: A Systematic Review and Integrated Bioinformatic Analysis

Cervical cancer is the leading cause of cancer-related death among women in developing countries. However, no comprehensive molecular mechanism for cervical cancer has been established, as many studies were small-cohort studies conducted with small sample sizes. A thorough literature search was performed using the PubMed, Scopus, EBSCOhost, and Science Direct databases. Medical Subject Heading (MeSH) terms such as "Uterine Cervical Neoplasms" and "gene expression" were used as the keywords in all fields. A total of 4027 studies were retrieved, and only clinical studies, which used the microarray method to identify differentially expressed genes (DEGs) in the cervical tissue of cervical cancer patients, were selected. Following the screening, 6 studies were selected and 1128 DEGs were extracted from the data. Sixty-two differentially expressed genes from at least two studies were selected for further analysis by DAVID, STRING, and Cytoscape software. In cervical cancer pathogenesis, three significant clusters with high intermolecular interactions from the Protein-Protein Interaction (PPI) network complex revealed three major molecular mechanisms, including cell signaling, cell cycle, and cell differentiation. Subsequently, eight genes were chosen as the candidate genes based on their involvement in the relevant gene ontology (GO) and their interaction with other genes in the PPI network through undirected first neighbor nodes. The present systematic review improves our understanding of the molecular mechanism of cervical cancer and the proposed genes that can be used to expand the biomarker panel in the screening for cervical cancer. The targeted genes may be beneficial for the development of better treatment strategies.

Identification of Immune and Hypoxia Risk Classifier to Estimate Immune Microenvironment and Prognosis in Cervical Cancer

Purpose

Cervical cancer (CC) is one of the most common gynecologic neoplasms. Hypoxia is an essential trigger for activating immunosuppressive activity and initiating malignant tumors. However, the determination of the role of immunity and hypoxia on the clinical outcome of CC patients remains unclear.

Methods

The CC independent cohort were collected from TCGA database. Consensus cluster analysis was employed to determine a molecular subtype based on immune and hypoxia gene sets. Cox relevant analyses were utilized to set up a risk classifier for prognosis assessment. The underlying pathways of classifier genes were detected by GSEA. Moreover, we conducted CIBERSORT algorithm to mirror the immune status of CC samples.

Results

We observed two cluster related to immune and hypoxia status and found the significant difference in outcome of patients between the two clusters. A total of 251 candidate genes were extracted from the two clusters and enrolled into Cox relevant analyses. Then, seven hub genes (CCL20, CXCL2, ITGA5, PLOD2, PTGS2, TGFBI, and VEGFA) were selected to create an immune and hypoxia-based risk classifier (IHBRC). The IHBRC can precisely distinguish patient risk and estimate clinical outcomes. In addition, IHBRC was closely bound up with tumor associated pathways such as hypoxia, P53 signaling and TGF β signaling. IHBRC was also tightly associated with numerous types of immunocytes.

Conclusion

This academic research revealed that IHBRC can be served as predictor for prognosis assessment and cancer treatment estimation in CC.