Knockdown of MIR9‑3HG inhibits proliferation and promotes apoptosis of cervical cancer cells by miR‑498 via EP300

Pathogenomics model for personalized medicine in cervical cancer: Cross-talk of gene expressions and pathological images related to oxidative stress

An increasing number of studies have shown that oxidative stress plays an important role in the development and progression of cancer. Cervical cancer (CC) is a disease of unique complexity that tends to exhibit high heterogeneity in molecular phenotypes. We aim here to characterize molecular features of cervical cancer by developing a classification system based on oxidative stress-related gene expression profiles. In this study, we obtained gene expression profiling data for cervical cancer from the TCGA (The Cancer Genome Atlas) and GEO (Gene Expression Omnibus) (GSE44001) databases. Oxidative stress-related genes used for clustering were obtained from GeneCards. Patients with cervical cancer were divided into two subtypes (C1 and C2) by non-negative matrix factorization (NMF) classification. By performing Kaplan-Meier survival analysis, differential expression analysis, and gene set enrichment analysis (GSEA) between the two subtypes, we found that subtype C2 had a worse prognosis and was highly enriched for immune-related pathways as well as epithelial-mesenchymal transition (EMT) pathways. Subsequently, we performed metabolic pathway analysis, gene mutation landscape analysis, immune microenvironment analysis, immunotherapy response analysis, and drug sensitivity analysis of the two isoforms. The results showed that the isoforms were significantly different between metabolic pathway enrichment and the immune microenvironment, and the chromosomes of subtype C1 were more unstable. In addition, we found that subtype C2 tends to respond to treatment with anti-CTLA4 agents, a conclusion that coincides with high chromosomal variation in C1, as well as C2 enrichment of immune-related pathways. Then, we screened 10 agents that were significantly susceptible to C2 subtype. Finally, we constructed pathogenomics models based on pathological features and linked them to molecular subtypes. This study establishes a novel CC classification based on gene expression profiles of oxidative stress-related genes and elucidates differences between immune microenvironments between CC subtypes, contributing to subtype-specific immunotherapy and drug therapy.

A cuproptosis-related lncRNA signature predicts the prognosis and immune cell status in head and neck squamous cell carcinoma

Introduction

The incidence of head and neck squamous cell carcinoma (HNSCC), one of the most prevalent tumors, is increasing rapidly worldwide. Cuproptosis, as a new copper-dependent cell death form, was proposed recently. However, the prognosis value and immune effects of cuproptosis-related lncRNAs (CRLs) have not yet been elucidated in HNSCC.

Methods

In the current study, the expression pattern, differential profile, clinical correlation, DNA methylation, functional enrichment, univariate prognosis factor, and the immune effects of CRLs were analyzed. A four-CRL signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm.

Results

Results showed that 20 CRLs had significant effects on the stage progression of HNSCC. Sixteen CRLs were tightly correlated with the overall survival (OS) of HNSCC patients. Particularly, lnc-FGF3-4 as a single risk factor was upregulated in HNSCC tissues and negatively impacted the prognosis of HNSCC. DNA methylation probes of cg02278768 (MIR9-3HG), cg07312099 (ASAH1-AS1), and cg16867777 (TIAM1-AS1) were also correlated with the prognosis of HNSCC. The four-CRL signature that included MAP4K3-DT, lnc-TCEA3-1, MIR9-3HG, and CDKN2A-DT had a significantly negative effect on the activation of T cells follicular helper and OS probability of HNSCC. Functional analysis revealed that cell cycle, DNA replication, and p53 signal pathways were enriched.

Discussion

A novel CRL-related signature has the potential of prognosis prediction in HNSCC. Targeting CRLs may be a promising therapeutic strategy for HNSCC.

5-Methylcytosine-related lncRNAs: predicting prognosis and identifying hot and cold tumor subtypes in head and neck squamous cell carcinoma

BACKGROUND

5-Methylcytosine (m⁵C) methylation is recognized as an mRNA modification that participates in biological progression by regulating related lncRNAs. In this research, we explored the relationship between m⁵C-related lncRNAs (mrlncRNAs) and head and neck squamous cell carcinoma (HNSCC) to establish a predictive model.

METHODS

RNA sequencing and related information were obtained from the TCGA database, and patients were divided into two sets to establish and verify the risk model while identifying prognostic mrlncRNAs. Areas under the ROC curves were assessed to evaluate the predictive effectiveness, and a predictive nomogram was constructed for further prediction. Subsequently, the tumor mutation burden (TMB), stemness, functional enrichment analysis, tumor microenvironment, and immunotherapeutic and chemotherapeutic responses were also assessed based on this novel risk model. Moreover, patients were regrouped into subtypes according to the expression of model mrlncRNAs.

RESULTS

Assessed by the predictive risk model, patients were distinguished into the low-MLRS and high-MLRS groups, showing satisfactory predictive effects with AUCs of 0.673, 0.712, and 0.681 for the ROCs, respectively. Patients in the low-MLRS groups exhibited better survival status, lower mutated frequency, and lower stemness but were more sensitive to immunotherapeutic response, whereas the high-MLRS group appeared to have higher sensitivity to chemotherapy. Subsequently, patients were regrouped into two clusters: cluster 1 displayed immunosuppressive status, but cluster 2 behaved as a hot tumor with a better immunotherapeutic response.

CONCLUSIONS

Referring to the above results, we established a m⁵C-related lncRNA model to evaluate the prognosis, TME, TMB, and clinical treatments for HNSCC patients. This novel assessment system is able to precisely predict the patients' prognosis and identify hot and cold tumor subtypes clearly for HNSCC patients, providing ideas for clinical treatment.

Identification and validation of cuproptosis-related LncRNA signatures as a novel prognostic model for head and neck squamous cell cancer

Background

Head and neck squamous cell cancer (HNSCC) is a common malignant cancer. We aimed to explore prognostic cuproptosis-related lncRNAs (CRLs) and prognostic risk models for HNSCC.

Methods

The transcriptome profiles and clinical data were obtained from the TCGA database, and 19-cuproptosis-related genes (CRGs) were acquired from previous studies. Then, the prognostic model based on seven CRLs was established. We analysed its value to evaluate the prognosis, drug sensitivity, and tumour immune functions of patients with HNSCC. Finally, we used quantitative reverse transcription polymerase chain reaction (qRT‒PCR) to validate the seven CRLs.

Results

We established a 7-CRL signature. Kaplan‒Meier (K–M) curve analysis demonstrated a significantly preferable prognosis in the low-risk group. Multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor. Nomogram, ROC curve, and principal component analysis indicated that the signature presented significant predictive capability. Moreover, most of the high-risk group showed lower levels of IC50 for certain chemotherapy drugs, such as cisplatin, cytarabine, docetaxel, doxorubicin, etoposide, gemcitabine, methotrexate, paclitaxel, and dasatinib. Finally, the expression of AP001372.2, MIR9-3HG, AL160314.2, POLH-AS1, and AL109936.2 was upregulated, while AC090587.1 and WDFY3-AS2 were downregulated in HNSCC cell lines compared with normal cell lines by qRT‒PCR.

Conclusions

The 7-CRL signature was presented to be a novel biomarker for predicting prognosis for HNSCC.

Development and validation of an autophagy-related long non-coding RNA prognostic signature for cervical squamous cell carcinoma and endocervical adenocarcinoma

Background

In this study, we aimed to investigate the signature of the autophagy-related lncRNAs (ARLs) and perform integrated analysis with immune infiltration in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).

Methods and results

The UCSC Xena and HADb databases provided the corresponding data. The ARLs were selected via constructing a co-expression network of autophagy-related genes (ARGs) and lncRNAs. Univariate Cox regression analysis combined with LASSO regression and multivariate Cox regression analysis were utilized to screen lncRNAs. The ARL risk signature was established by Cox regression and tested if it was an independent element bound up with patient prognosis. We used the xCell algorithm and ssGSEA to clarify the pertinence between immune infiltration and the expression of ARLs. Finally, we predicted the sensitivity of drug treatment as well as the immune response. Results indicated that the three prognostic ARLs (SMURF2P1, MIR9-3HG, and AC005332.4) possessed significant diversity and constituted the ARL signature. Risk score was an individual element (HR = 2.82, 95% CI = 1.87–4.30; p < 0.001). Immune infiltration analysis revealed significant increases in central memory CD8+ T cells, endothelial cells, CD8+ naive T cells, and preadipocytes in the high-risk group (p < 0.05). There were 10 therapeutic agents that varied significantly in their estimated half-maximal inhibitory concentrations in the two groups. According to the experimental validation, we found that SMURF2P1 belongs to the co-stimulatory genes and might assume greater importance in the development of cervical adenocarcinoma. MIR9-3HG and AC005332.4 belonged to the tumor-suppressor genes and they may play a more positive role in cervical squamous cell carcinoma.

Conclusions

This research explored and validated a novel signature of the ARLs, which can be applied to forecast the prognosis of patients with CESC and is closely associated with immune infiltration.

Prognostic signature of endoplasmic reticulum stress-related long noncoding RNAs in head and neck squamous cell carcinoma: Association with somatic mutation and tumor immune microenvironment

Background/purpose

Analysis of endoplasmic reticulum stress (ERS)-related long noncoding RNAs (LncRNAs) may enable prognostic stratification in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to comprehensively analyze the ERS-related LncRNAs signature and its effects on the prognosis, tumorigenesis, and tumor immune microenvironment in HNSCC.

Materials and methods

The transcriptome data of HNSCC were obtained from TCGA. Least absolute shrinkage selection operator algorithm, and multivariate Cox regression were used to screen LncRNAs for the signature construction. Somatic mutation, gene enrichment, and immune infiltration analyses were further performed.

Results

458 ERS-related LncRNAs were identified and 55 of which were correlated with HNSCC prognosis. Ten ERS-related LncRNAs were selected to establish a risk prediction signature. When dividing patients into high-risk and low-risk groups by signature score, high-risk group correlated with worse survival rates (hazard ratio = 1.211; 95% confidence interval 1.123-1.306, P < 0.001). The area under the curve was 0.751 and 0.716 in the training and validation cohorts at 3-year. Moreover, high-risk group have increased somatic mutation rates and reduced infiltration abundancy of B cells and CD8+ T cells.

Conclusion

The prognostic signature based on ERS-related LncRNAs may serve as a predictor of altered oncogene mutations and immune microenvironment, which provided an insight into the relationship between ERS, LncRNAs, and tumor progression.

Exploration of the Immune-Related Long Noncoding RNA Prognostic Signature and Inflammatory Microenvironment for Cervical Cancer

Purpose: Our research developed immune-related long noncoding RNAs (lncRNAs) for risk stratification in cervical cancer (CC) and explored factors of prognosis, inflammatory microenvironment infiltrates, and chemotherapeutic therapies. Methods: The RNA-seq data and clinical information of CC were collected from the TCGA TARGET GTEx database and the TCGA database. lncRNAs and immune-related signatures were obtained from the GENCODE database and the ImPort database, respectively. We screened out immune-related lncRNA signatures through univariate Cox, LASSO, and multivariate Cox regression methods. We established an immune-related risk model of hub immune-related lncRNAs to evaluate whether the risk score was an independent prognostic predictor. The xCell and CIBERSORTx algorithms were employed to appraise the value of risk scores which are in competition with tumor-infiltrating immune cell abundances. The estimation of tumor immunotherapy response through the TIDE algorithm and prediction of innovative recommended medications on the target to immune-related risk model were also performed on the basis of the IC50 predictor. Results: We successfully established six immune-related lncRNAs (AC006126.4, EGFR-AS1, RP4-647J21.1, LINC00925, EMX2OS, and BZRAP1-AS1) to carry out prognostic prediction of CC. The immune-related risk model was constructed in which we observed that high-risk groups were strongly linked with poor survival outcomes. Risk scores varied with clinicopathological parameters and the tumor stage and were an independent hazard factor that affect prognosis of CC. The xCell algorithm revealed that hub immune-related signatures were relevant to immune cells, especially mast cells, DCs, megakaryocytes, memory B cells, NK cells, and Th1 cells. The CIBERSORTx algorithm revealed an inflammatory microenvironment where naive B cells (p < 0.01), activated dendritic cells (p < 0.05), activated mast cells (p < 0.0001), CD8⁺ T cells (p < 0.001), and regulatory T cells (p < 0.01) were significantly lower in the high-risk group, while macrophages M0 (p < 0.001), macrophages M2 (p < 0.05), resting mast cells (p < 0.0001), and neutrophils (p < 0.01) were highly conferred. The result of TIDE indicated that the number of immunotherapy responders in the low-risk group (124/137) increased significantly (p = 0.00000022) compared to the high-risk group (94/137), suggesting that the immunotherapy response of CC patients was completely negatively correlated with the risk scores. Last, we compared differential IC50 predictive values in high- and low-risk groups, and 12 compounds were identified as future treatments for CC patients. Conclusion: In this study, six immune-related lncRNAs were suggested to predict the outcome of CC, which is beneficial to the formulation of immunotherapy.

Signature involved in immune-related lncRNA pairs for predicting the immune landscape of cervical cancer

Background

Immune-related long non-coding RNAs (irlncRNAs) are known to hold great promise as superior biomarkers for cervical cancer-related immunotherapeutic response and the tumor immune microenvironment. Here, we constructed a prognostic signature based on irlncRNA pairs (IRLPs).

Methods

The samples were downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. The least absolute shrinkage and selection operator Cox regression was performed to construct the prognostic model. Receiver operating characteristic (ROC) curve and nomogram were plotted to validate accuracy of the model. Next, we estimated the immune cell infiltration and the correlation between risk score and the expression of genes related to immune checkpoint. Finally, we calculated the score of the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the half maximal inhibitory concentration of the chemotherapeutic agent to evaluate the response to immunotherapy and chemotherapy.

Results

We constructed a prognostic signature that consisted of 11 irlncRNAs. The area under the curve values of the 1-, 3-, and 5-year ROC curves were 0.844, 0.891, and 0.871, respectively. The expression of CTLA-4, HAVCR2, IDO1, LAG3, and PDCD1 were negatively correlated with risk scores. The score of TIDE in the high-risk group was significantly higher than in the low-risk group ( P < 0.01). Patients in the low-risk subgroup were more sensitive to chemotherapeutic agents, such as axitinib and docetaxel, whereas patients in the low-risk subgroup were more sensitive to mitomycin C.

Conclusion

Our study highlighted the value of the 11 IRLPs signatures to predict the prognosis and the response to immunotherapy and chemotherapeutics for patients with cervical cancer.