Identification and validation of a six-gene signature associated with glycolysis to predict the prognosis of patients with cervical cancer

Cross-talk between cuproptosis and ferroptosis to identify immune landscape in cervical cancer for mRNA vaccines development

Messenger RNA (mRNA)-based vaccines present a promising avenue for cancer immunotherapy; however, their application in cervical cancer remains unexplored. This study investigated the interplay between the regulated cell death pathways of cuproptosis and ferroptosis to advance the development of mRNA vaccines for cervical cancer. We identified key cuproptosis-related and ferroptosis-related genes (CFRGs) from public mRNA profiles and determined their prognostic significance, mutation frequencies, and effect on the immune landscape. Our analysis revealed two distinct subtypes of cervical cancer associated with CFRGs, with differences in prognosis and immune characteristics. Using LASSO, XGBoost, and SVM-RFE methods, we established a 4-gene prognostic signature (TSC22D3, SQLE, ZNF419, and TFRC) to stratify patients based on their risk and determine its correlation with immune microenvironment, mutation profiles, and treatment responses. RT-qPCR validation confirmed the differential expression of these genes in clinical samples. Our findings identify TSC22D3, SQLE, ZNF419, and TFRC as candidate targets for mRNA vaccine development and offer a potential prognostic tool for personalized cervical cancer treatment.

A hypoxia-derived gene signature to suggest cisplatin-based therapeutic responses in patients with cervical cancer

Cervical cancer remains a significant global public health concern, often exhibits cisplatin resistance in clinical settings. Hypoxia, a characteristic of cervical cancer, substantially contributes to cisplatin resistance. To evaluate the therapeutic efficacy of cisplatin in patients with cervical cancer and to identify potential effective drugs against cisplatin resistance, we established a hypoxia-inducible factor-1 (HIF-1)-related risk score (HRRS) model using clinical data from patients treated with cisplatin. Cox and LASSO regression analyses were used to stratify patient risks and prognosis. Through qRT-PCR, we validated nine potential prognostic HIF-1 genes that successfully predict cisplatin responsiveness in patients and cell lines. Subsequently, we identified fostamatinib, an FDA-approved spleen tyrosine kinase inhibitor, as a promising drug for targeting the HRRS-high group. We observed a positive correlation between the IC50 values of fostamatinib and HRRS in cervical cancer cell lines. Moreover, fostamatinib exhibited potent anticancer effects on high HRRS groups in vitro and in vivo. In summary, we developed a hypoxia-related gene signature that suggests cisplatin response prediction in cervical cancer and identified fostamatinib as a potential novel treatment approach for resistant cases.

Oncogenic HPV-induced high expression of ESM1 predicts poor prognosis and regulates aerobic glycolysis in cervical cancer

The impact of endothelial cell-specific molecule 1 (ESM1) on the initiation and progression of diverse cancers has been extensively studied, yet its regulatory mechanisms in relation to cervical cancer remain insufficiently understood. Through bioinformatics analysis, we revealed that ESM1 was highly expressed in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and correlated with dismal clinicopathological features. The activation of ESM1 is facilitated by the presence of oncogenic HPV E6 and E7. HPV E6 and E7 enhance the expression of ESM1 by diminishing the levels of miR-205-5p, which specifically targets the 3' untranslated region of ESM1 mRNA. In addition, we demonstrated that ESM1 facilitates aerobic glycolysis of cervical cancer cells via the Akt/mTOR pathway. Suppression of ESM1 led to a reduction in the expression of HIF-1α and multiple glycolytic enzymes. Taken together, our findings provide insights into the mechanisms by which HPV infections regulate oncogenes, thereby contributing to cervical carcinogenesis.

Predictive Value and Immunological Role of the HSPA5 Gene in Cervical Cancer

Cervical cancer (CC) ranks fourth among women's malignancies worldwide and seriously affects women's health. HSPA5 is a heat shock protein, also known as glucose regulatory protein 78 (GRP78). Upregulation of HSPA5 has been reported to be closely associated with multiple types of tumors. However, the specific role of HSPA5 in cervical cancer has not been discovered. In our study, we explored the prognostic value of HSPA5 in CC. Here, we analyzed the (TCGA) and (UCSC) databases, the analysis of HSPA5 in many tumors types was conducted with the "wilcox. test" method. A False Discovery Rate (FDR) value < 0.05 and Log2 | (fold change, FC) |> 1 were set as the cutoffs. "*", "**", and "***" indicate FDR < 0.05, < 0.01, and < 0.001, respectively, and further used human cervical cancer cells for q-PCR and western blotting detection. q-PCR and western blotting results showed that HSPA5 was highly expressed in cervical cancer cells, while it was expressed at low levels in normal cells (P < 0.05).We also analyzed the immunohistochemical data. immunohistochemical analysis results showed that HSPA5 was highly expressed in human cervical cancer, while it was expressed at low levels in normal tissues (P < 0.05). Analysis in TCGA-UCSC showed that the proportion of G3 in the group with high expression of HSPA5 was relatively high (P < 0.05). Enrichment analysis and survival analysis showed that the increased expression of HSPA5 in cervical cancer was related to the survival of CC and was involved in the regulation of biological behavior and molecular signaling pathways of cervical cancer. The correlation analysis of immune checkpoint and immune infiltration showed that HSPA5 was involved in the regulation of immune process of cervical cancer (P < 0.05). Drug sensitivity correlation analysis showed that HSPA5 was a sensitive target for tumor drugs (P < 0.05). In brief, those results suggest that HSPA5 can act as an oncogene of CC development and can serve as an effective predictive biomarker in cervical cancer.

Increased Gene Expression of C1orf74 Is Associated with Poor Prognosis in Cervical Cancer

C1orf74, also known as URCL4, has been reported to have higher expression and be associated with poor prognosis in lung adenocarcinoma patients, and its role in regulation of the EGFR/AKT/mTORC1 pathway has been recently elucidated. In the current study, we used publicly available data and experimental validation of C1orf74 gene expression and its association with prognosis in cervical cancer patients. qRT-PCR was performed using RNA from cervical cancer cell lines and twenty-five cervical cancer patients. Data from TNMplot revealed that mRNA expression of the C1orf74 gene in primary tumor tissues, as well as metastatic tissues from cervical cancer patients, was significantly higher compared to normal cervical tissues. HPV-positive tumors had higher expression of this gene compared to HPV-negative tumors. qPCR analysis also demonstrated higher expression of C1orf74 in HPV-positive cervical cancer cell lines and most cervical cancer patients. The promoter methylation levels of the C1orf74 gene in cervical cancer tissues were lower compared to normal cervical tissues (p < 0.05). Collectively, our study indicates that higher expression of the C1orf74 gene caused by hypomethylation of its promoter is associated with poor overall survival in cervical cancer patients. Thus, C1orf74 is a novel prognostic marker in cervical cancer.

Metabolism-associated molecular classification of cervical cancer

OBJECTIVE

This study aimed to explore metabolic abnormalities in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) for metabolism-related genes.

METHODS

We downloaded expression data for metabolism-related genes, performed differential expression analysis, and applied weighted gene co-expression network analysis (WGCNA) to identify metabolism-related functional modules. We obtained normalised miRNA expression data and identified master methylation regulators for metabolism-related genes. Cox regression of data on metabolism-related genes was performed to screen for genes that affect the prognosis of patients with CESC. Furthermore, we selected key genes for validation.

RESULTS

Our results identified 3620 metabolism-related genes in CESC, 2493 of which contained related mutations. The co-occurrence of CUBN, KALRN, and HERC1 was related to the prognosis of CESC. The fraction of genome altered (FGA) closely correlated with overall survival. In expression analysis, 374 genes were related to the occurrence and prognosis of CESC. We then identified four metabolic pathway modules in WGCNA. Further analysis revealed that glycolysis/gluconeogenesis was related to endothelial cells and that arachidonic acid metabolism was related to cell proliferation. These four modules were also related to the prognosis of CESC. Among CESC-related metabolic genes, two genes were found to be regulated by microRNAs (miRNAs) and methylation, whereas another two genes were coregulated by miRNAs and mutations.

CONCLUSIONS

Among metabolism-related genes, 15 genes were related to the prognosis of CESC. The co-occurrence of CUBN/KALRN/HERC1 was associated with CESC prognosis. Glycolysis/gluconeogenesis was related to endothelial cells, and arachidonic acid metabolism was related to cell proliferation.

Research progress on the GRP78 gene in the diagnosis, treatment and immunity of cervical cancer

BACKGROUND

GRP78 is a molecular chaperone protein in the endoplasmic reticulum that is involved in protein assembly and quality control, and it participates in ER stress regulation of endoplasmic reticulum stress pathways. Studies have confirmed that GRP78 gene is highly expressed in a variety of tumors and is involved in different biological functions.

PURPOSE

The present review highlights the involvement of the GRP78 gene in regulating the development of cervical cancer by promoting the proliferation and invasion of cervical cancer cells as well as by inhibiting apoptosis and promoting the Warburg effect. High expression of GRP78 is positively correlated with chemotherapy resistance in cervical cancer. GRP78 plays an anticancer role in cervical cancer by regulating autophagy and apoptosis. Mediated immune CD8 + T cells regulate tumor cell immunity and play a role in the application of the HPV vaccine.

CONCLUSIONS

GRP78 plays a multifunctional role in cervical cancer and has important therapeutic and diagnostic value.

The mRNA and protein levels of the glycolytic enzymes lactate dehydrogenase A (LDHA) and phosphofructokinase platelet (PFKP) are good predictors of survival time, recurrence, and risk of death in cervical cancer patients

INTRODUCTION

Patients with cervical cancer (CC) may experience local recurrence very often after treatment; when only clinical parameters are used, most cases are diagnosed in late stages, which decreases the chance of recovery. Molecular markers can improve the prediction of clinical outcome. Glycolysis is altered in 70% of CCs, so molecular markers of this pathway associated with the aggressiveness of CC can be identified.

METHODS

The expression of 14 glycolytic genes was analyzed in 97 CC and 29 healthy cervical tissue (HCT) with microarray; only LDHA and PFKP were validated at the mRNA and protein levels in 36 of those CC samples and in 109 new CC samples, and 31 HCT samples by qRT-PCR, Western blotting, or immunohistochemistry. A replica analysis was performed on 295 CC from The Cancer Genome Atlas (TCGA) database.

RESULTS

The protein expression of LDHA and PFKP was associated with poor overall survival [OS: LDHA HR = 4.0 (95% CI = 1.4-11.1); p = 8.0 × 10-3 ; PFKP HR = 3.3 (95% CI = 1.1-10.5); p = 4.0 × 10-2 ] and disease-free survival [DFS: LDHA HR = 4.5 (95% CI = 1.9-10.8); p = 1.0 × 10-3 ; PFKP HR = 3.2 (95% CI = 1.2-8.2); p = 1.8 × 10-2 ] independent of FIGO clinical stage, and the results for mRNA expression were similar. The risk of death was greater in patients with overexpression of both biomarkers than in patients with advanced FIGO stage [HR = 8.1 (95% CI = 2.6-26.1; p = 4.3 × 10-4 ) versus HR = 7 (95% CI 1.6-31.1, p = 1.0 × 10-2 )] and increased exponentially as the expression of LDHA and PFKP increased.

CONCLUSIONS

LDHA and PFKP overexpression at the mRNA and protein levels was associated with poor OS and DFS and increased risk of death in CC patients regardless of FIGO stage. The measurement of these two markers could be very useful for evaluating clinical evolution and the risk of death from CC and could facilitate better treatment decision making.

Characterization of glycometabolism and tumor immune microenvironment for predicting clinical outcomes in gastric cancer

Recent evidence demonstrates that the reprogramming of energy metabolism can interact with the tumor immune microenvironment, thereby participating in the progression of cancer. In this study, multi-omics data of 2471 gastric cancer samples were used to identify tumor glycometabolism and its correlation with tumor immune microenvironment. A series of bioinformatic approaches were performed to establish a scoring system to predict the survival and response of chemotherapy and immunotherapy. Three glycometabolic subtypes and two immune clustering subgroups of gastric cancer were determined. We further established a Gluco-Immune Scoring system to quantify the cancer glycometabolic status and immune infiltration of individual patients. Patients with low Gluco-Immune Score were sensitive to adjuvant chemotherapy, while patients with high Gluco-Immune Score may benefit from immunotherapy. Our results indicate that in gastric cancer, the assessment of tumor glucose metabolism and immune microenvironment has application value for the prediction of curative effects and the formulation of combined treatment strategies.

Cystatin B increases autophagic flux by sustaining proteolytic activity of cathepsin B and fuels glycolysis in pancreatic cancer: CSTB orchestrates autophagy and glycolysis in PDAC

BACKGROUND

Both autophagy and glycolysis are essential for pancreatic ductal adenocarcinoma (PDAC) survival due to desmoplasia. We investigated whether targeting a hub gene which participates in both processes could be an efficient strategy for PDAC treatment.

METHODS

The expression pattern of glycolysis signatures (GS) and autophagy signatures (AS) and their correlation with cystatin B (CSTB) in PDAC were analysed. It was discovered how CSTB affected the growth, glycolysis, and autophagy of PDAC cells. We assessed competitive binding to cathepsin B (CTSB) between CSTB and cystatin C (CSTC) via immunoprecipitation (IP) and immunofluorescence (IF). Chromatin immunoprecipitation quantitative polymerase chain reaction (ChIP-qPCR) and luciferase reporter gene assays were used to unveil the mechanism underlying CSTB upregulation. The expression pattern of CSTB was examined in clinical samples and KrasG12D/+, Trp53R172H/+, Pdx1-Cre (KPC) mice.

RESULTS

GS and AS were enriched and closely associated in PDAC tissues. CSTB increased autophagic flux and provided substrates for glycolysis. CSTB knockdown attenuated the proliferation of PDAC cells and patient-derived xenografts. The liquid chromatography-tandem mass spectrometry assay indicated CSTB interacted with CTSB and contributed to the proteolytic activity of CTSB in lysosomes. IF and IP assays demonstrated that CSTB competed with CSTC to bind to CTSB. Mutation of the key sites of CSTB abolished the interaction between CSTB and CTSB. CSTB was highly expressed in PDAC due to H3K27acetylation and SP1 expression. High expression of CSTB in PDAC was observed in tissue microarray and patients' serum samples.

CONCLUSIONS

Our work demonstrated the tumorigenic roles of autophagy and glycolysis in PDAC. CSTB is a key role in orchestrating these processes to ensure energy supply of PDAC cells.

Loss of PFKFB4 induces cell cycle arrest and glucose metabolism inhibition by inactivating MEK/ERK/c-Myc pathway in cervical cancer cells

6-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 (PFKFB4) was reported to be necessary for tumour growth in several cancers. However, the function of PFKFB4 in cervical cancer has not been clearly elucidated. Bioinformatics analysis was applied to detect the expression of PFKFB4 in cervical cancer and the association with survival prognosis. The effect of PFKFB4 on cervical cancer cells growth, cycle, invasion, migration and glucose metabolism was investigated by loss-of-function approaches in vitro. The association between PFKFB4 and MEK/ERK/c-Myc pathway was identified by western blot assay. We found that PFKFB4 was highly expressed in cervical cancer samples and its overexpression led to a poor prognosis of cervical cancer patients. Knock down of PFKFB4 reduced cell growth, blocked cell cycle, inhibited cell invasion and migration, and blocked glucose metabolism in cervical cancer cells. Our findings afforded a theoretical basis for further research on the treatment of cervical cancer based on the control of PFKFB4 expression. Impact StatementWhat is already known on this subject? PFKFB4 was overexpressed in several kinds of cancers and its requirement for tumour growth has been confirmed in cancers such as glioma and breast cancer. However, the function of PFKFB4 in cervical cancer cells has not been clearly elucidated. A bioinformatics study showed that PFKFB4 was a member of a six-gene signature associated with glycolysis to predict the prognosis of patients with cervical cancer. However, the relationship between PFKFB4 and glucose metabolism in cervical cancer has not been revealed.What do the results of this study add? Our results showed that PFKFB4 was highly expressed in cervical cancer samples and its overexpression led to a poor prognosis of cervical cancer patients. Moreover, the administration of si-PFKFB4 significantly reduced cell growth ability, blocked cell cycle, restrained the mobility and suppressed the glucose metabolism in cervical cancer cells partially by inactivating MEK/ERK/c-Myc pathway.What are the implications of these findings for clinical practice and/or further research? Our findings afforded a theoretical basis for further research on the treatment of cervical cancer based on the control of PFKFB4 expression.

Construction and Verification of a Combined Hypoxia and Immune Index for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most aggressive malignancies in humans. Hypoxia-related genes are now recognized as a reflection of poor prognosis in cancer patients with cancer. Meanwhile, immune-related genes play an important role in the occurrence and progression of ccRCC. Nevertheless, reliable prognostic indicators based on hypoxia and immune status have not been well established in ccRCC. The aims of this study were to develop a new gene signature model using bioinformatics and open databases and to validate its prognostic value in ccRCC. The data used for the model structure can be accessed from The Cancer Genome Atlas database. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the hypoxia- and immune-related genes associated with prognostic risk, which were used to develop a characteristic model of prognostic risk. Kaplan-Meier and receiver-operating characteristic curve analyses were performed as well as independent prognostic factor analyses and correlation analyses of clinical characteristics in both the training and validation cohorts. In addition, differences in tumor immune cell infiltrates were compared between the high and low risk groups. Overall, 30 hypoxia- and immune-related genes were identified, and five hypoxia- and immune-related genes (EPO, PLAUR, TEK, TGFA, TGFB1) were ultimately selected. Survival analysis showed that the high-risk score on the hypoxia- and immune-related gene signature was significantly associated with adverse survival outcomes. Furthermore, clinical ccRCC samples from our medical center were used to validate the differential expression of the five genes in tumor tissue compared to normal tissue through quantitative real-time polymerase chain reaction (qRT-PCR). However, more clinical trials are needed to confirm these results, and future experimental studies must verify the potential mechanism behind the predictive value of the hypoxia- and immune-related gene signature.

Establishment of A Nomogram for Predicting the Prognosis of Soft Tissue Sarcoma Based on Seven Glycolysis-Related Gene Risk Score

Background: Soft tissue sarcoma (STS) is a group of tumors with a low incidence and a complex type. Therefore, it is an arduous task to accurately diagnose and treat them. Glycolysis-related genes are closely related to tumor progression and metastasis. Hence, our study is dedicated to the development of risk characteristics and nomograms based on glycolysis-related genes to assess the survival possibility of patients with STS.

Methods: All data sets used in our research include gene expression data and clinical medical characteristics in the Genomic Data Commons Data Portal (National Cancer Institute) Soft Tissue Sarcoma (TCGA SARC) and GEO database, gene sequence data of corresponding non-diseased human tissues in the Genotype Tissue Expression (GTEx).Next, transcriptome data in TCGA SARC was analyzed as the training set to construct a glycolysis-related gene risk signature and nomogram, which were confirmed in external test set.

Results: We identified and verified the 7 glycolysis-related gene signature that is highly correlated with the overall survival (OS) of STS patients, which performed excellently in the evaluation of the size of AUC, and calibration curve. As well as, the results of the analysis of univariate and multivariate Cox regression demonstrated that this 7 glycolysis-related gene characteristic acts independently as an influence predictor for STS patients. Therefore, a prognostic-related nomogram combing 7 gene signature with clinical influencing features was constructed to predict OS of patients with STS in the training set that demonstrated strong predictive values for survival.

Conclusion: These results demonstrate that both glycolysis-related gene risk signature and nomogram were efficient prognostic indicators for patients with STS. These findings may contribute to make individualize clinical decisions on prognosis and treatment.

Identifying a cervical cancer survival signature based on mRNA expression and genome-wide copy number variations

Cervical cancer mortality is the second highest in gynecological cancers. This study developed a new model based on copy number variation data and mRNA data for overall survival prediction of cervical cancer. Differentially expressed genes from The Cancer Genome Atlas dataset detected by univariate Cox regression analysis were further simplified to six by least absolute shrinkage and selection operator (Lasso) and stepwise Akaike information criterion (stepAIC). The study developed a six-gene signature, which was further verified in independent dataset. Association between immune infiltration and risk score was investigated by immune score. The relation between the signature and functional pathways was examined by gene set enrichment analysis. Ninety-nine differentially expressed genes were detected, and C11orf80, FOXP3, GSN, HCCS, PGAM5, and RIBC2 were identified as key genes to construct a six-gene signature. The prognostic signature showed a significant correlation with overall survival (hazard ratio, HR = 3.45, 95% confidence interval (CI) = 2.08-5.72, p < 0.00001). Immune score showed a negative correlation with the risk score calculated by the signature (p < 0.05). Four immune-related pathways were closely associated with risk score (p < 0.0001). The six-gene prognostic signature was an effective tool to predict overall survival of cervical cancer. In conclusion, the newly identified six genes may be considered as new drug targets for cervical cancer treatment.

Comprehensive Proteomic Analysis of Colon Cancer Tissue Revealed the Reason for the Worse Prognosis of Right-Sided Colon Cancer and Mucinous Colon Cancer at the Protein Level

To clarify the molecular mechanisms underlying the poor prognosis of right-sided and mucinous colon cancer at the proteomic level. A tandem mass tag-proteomics approach was used to identify differentially expressed proteins (DEPs) in colon carcinoma tissues from different locations and with different histological types to reveal the underlying mechanisms of these differences at the protein level. In additional, the DEPs were analyzed using bioinformatics methods. The proteomics profiles among colon cancers with different tumor locations and histological types were dramatically distinguished. In terms of tumor locations, the right-sided carcinoma specific DEPs may promote the tumor progression via activating inflammation, metastasis associated pathways. When referring to histological types, the mucinous colon cancers perhaps increased the invasion and metastasis through distinct mechanisms in different tumor locations. For mucinous cancer located in right-sided colon, the mucinous specific DEPs were mainly associated with ECM-related remodeling and the IL-17 signal pathway. For mucinous cancer located in left-sided colon, the mucinous specific DEPs showed a strong relationship with ACE2/Ang-(1–7)/MasR axis. The proteomics profiles of colon cancers showed distinct differences related to locations and histological types. These results suggested a distinct mechanism underlying the diverse subtypes of colon cancers.

Metabolism-Relevant Molecular Classification Identifies Tumor Immune Microenvironment Characterization and Immunotherapeutic Effect in Cervical Cancer

Cervical cancer (CESC) is a gynecologic malignant tumor associated with high incidence and mortality rates because of its distinctive management complexity. Herein, we characterized the molecular features of CESC based on the metabolic gene expression profile by establishing a novel classification system and a scoring system termed as METAscore. Integrative analysis was performed on human CESC samples from TCGA dataset. Unsupervised clustering of RNA sequencing data on 2,752 formerly described metabolic genes identified three METAclusters. These METAclusters for overall survival time, immune characteristics, metabolic features, transcriptome features, and immunotherapeutic effectiveness existed distinct differences. Then we analyzed 207 DEGs among the three METAclusters and as well identified three geneclusters. Correspondingly, these three geneclusters also differently expressed among the aforementioned features, supporting the reliability of the metabolism-relevant molecular classification. Finally METAscore was constructed which emerged as an independent prognostic biomarker, related to CESC transcriptome features, metabolic features, immune characteristics, and linked to the sensitivity of immunotherapy for individual patient. These findings depicted a new classification and a scoring system in CESC based on the metabolic pattern, thereby furthering the understanding of CESC genetic signatures and aiding in the prediction of the effectiveness to anticancer immunotherapies.

Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

Background

Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer.

Methods

We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration.

Results

We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer.

Conclusions

This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC.

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.

Identification of Hypoxia Signature to Assess the Tumor Immune Microenvironment and Predict Prognosis in Patients with Ovarian Cancer

BACKGROUND

The 5-year overall survival rate of ovarian cancer (OC) patients is less than 40%. Hypoxia promotes the proliferation of OC cells and leads to the decline of cell immunity. It is crucial to find potential predictors or risk model related to OC prognosis. This study aimed at establishing the hypoxia-associated gene signature to assess tumor immune microenvironment and predicting the prognosis of OC.

METHODS

The gene expression data of 378 OC patients and 370 OC patients were downloaded from datasets. The hypoxia risk model was constructed to reflect the immune microenvironment in OC and predict prognosis.

RESULTS

8 genes (AKAP12, ALDOC, ANGPTL4, CITED2, ISG20, PPP1R15A, PRDX5, and TGFBI) were included in the hypoxic gene signature. Patients in the high hypoxia risk group showed worse survival. Hypoxia signature significantly related to clinical features and may serve as an independent prognostic factor for OC patients. 2 types of immune cells, plasmacytoid dendritic cell and regulatory T cell, showed a significant infiltration in the tissues of the high hypoxia risk group patients. Most of the immunosuppressive genes (such as ARG1, CD160, CD244, CXCL12, DNMT1, and HAVCR1) and immune checkpoints (such as CD80, CTLA4, and CD274) were upregulated in the high hypoxia risk group. Gene sets related to the high hypoxia risk group were associated with signaling pathways of cell cycle, MAPK, mTOR, PI3K-Akt, VEGF, and AMPK.

CONCLUSION

The hypoxia risk model could serve as an independent prognostic indicator and reflect overall immune response intensity in the OC microenvironment.