Prediction of Recurrence in Cervical Cancer Using a Nine-lncRNA Signature

RNA-binding proteins signature is a favorable biomarker of prognosis, immunotherapy and chemotherapy response for cervical cancer

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) still present a huge threaten to women's health, especially the local advanced patients. Hence, developing more effectiveness prognostic signatures is urgently needed. This study constructed and verified a robust RNA-binding proteins (RBPs) related signature through a series of bioinformatics methods and explored the biological function of hub RBP in vitro experiments. As a result, the 10 RBPs signature was successfully established and could act as an independent prognostic biomarker in CESC patients, which displayed the highest sensitivity and specificity in prognosis prediction compared with other clinicopathological parameters. The risk model also presented good performance in risk stratification among CESC patients. Besides, a nomogram was constructed based on pathological stage and the risk signature and exhibited satisfactory accuracy in prognosis prediction. Functional enrichment indicated that the risk signature mainly participated in immune-related pathways and cancer-related pathways, and the infiltration level of immune cells and immune checkpoints showed a significantly higher degree in low-risk patients compared with high-risk patients. Notably, the 10 RBPs signature act as a novel biomarker in immunotherapy and chemotherapy response. In addition, PRPF40B was selected as hub RBP and its transcription and translation levels were obviously increased in CESC tissues, as well as Hela and Siha cells. Knockdown of PRPF40B inhibits the proliferation, migration and invasion of Hela and Siha cells in vitro. In conclusion, our research provides a noticeable strategy in prognostic prediction among CESC patients, which may illuminate the prospect of CESC patients' clinical outcome.

Defining new biomarkers for overcoming therapeutical resistance in cervical cancer using lncRNA

Despite improvements in cervical cancer diagnosis and treatment, the prognosis for cervical cancer patients remains dismal due to the development of drug resistance, metastasis, and invasion resulting leading to treatment failure. Long non-coding RNAs (lncRNAs), a class of RNA transcripts have been reported in mediating carcinogenesis as well as drug, and radio-resistance in tumor cells. These lncRNAs regulate various cancer hallmarks and contribute to the development of therapeutic resistance. They regulates multiple signalling pathways, recruits polycomb group, function as miRNA sponge and scaffolds. Additionally, lncRNAs can act as oncogenes or tumor suppressors in cervical cancer. This comprehensive review outlines the biogenesis of lncRNA and its role in cancer development. It delves into the mechanisms through which various lncRNAs mediate chemoresistance and radioresistance in cervical cancer. By shedding into the light of mechanism, this review will also aids researchers in understanding lncRNAs as biomarkers and latest advancements in clinically targeting them with the help of Artificial Intelligence for overcoming chemoresistance and radioresistance, thereby improving cervical cancer treatment.

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.

Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer

Background: Given the high incidence and high mortality of cervical cancer (CC) among women in developing countries, identifying reliable biomarkers for the prediction of prognosis and therapeutic response is crucial. We constructed a prognostic signature of cuproptosis-related long non-coding RNAs (lncRNAs) as a reference for individualized clinical treatment. Methods: A total of seven cuproptosis-related lncRNAs closely related to the prognosis of patients with CC were identified and used to construct a prognostic signature via least absolute shrinkage and selection operator regression analysis in the training set. The predictive performance of the signature was evaluated by Kaplan-Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, and univariate and multivariate Cox analyses. Functional enrichment analysis and single-sample gene set enrichment analysis were conducted to explore the potential mechanisms of the prognostic signature, and a lncRNA-microRNA-mRNA network was created to investigate the underlying regulatory relationships between lncRNAs and cuproptosis in CC. The associations between the prognostic signature and response to immunotherapy and targeted therapy were also assessed. Finally, the prognostic value of the signature was validated using the CC tissues with clinical information in my own center. Results: A prognostic signature was developed based on seven cuproptosis-related lncRNAs, including five protective factors (AL441992.1, LINC01305, AL354833.2, CNNM3-DT, and SCAT2) and two risk factors (AL354733.3 and AC009902.2). The ROC curves confirmed the superior predictive performance of the signature compared with conventional clinicopathological characteristics in CC. The ion transport-related molecular function and various immune-related biological processes differed significantly between the two risk groups according to functional enrichment analysis. Furthermore, we discovered that individuals in the high-risk group were more likely to respond to immunotherapy and targeted therapies including trametinib and cetuximab than those in the low-risk group. Finally, CC tissues with clinical data from my own center further verify the robustness of the seven-lncRNA risk signature. Conclusion: We generated a cuproptosis-related lncRNA risk signature that could be used to predict prognosis of CC patients. Moreover, the signature could be used to predict response to immunotherapy and chemotherapy and thus could assist clinicians in making personalized treatment plans for CC patients.

Construction of a Six-Gene Prognostic Risk Model Related to Hypoxia and Angiogenesis for Cervical Cancer

Background: The prognosis of cervical cancer (CC) is poor and not accurately reflected by the primary tumor node metastasis staging system. Our study aimed to develop a novel survival-prediction model.

Methods: Hallmarks of CC were quantified using single-sample gene set enrichment analysis and univariate Cox proportional hazards analysis. We linked gene expression, hypoxia, and angiogenesis using weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression was combined with the random forest algorithm to construct a prognostic model. We further evaluated the survival predictive power of the gene signature using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves.

Results: Hypoxia and angiogenesis were the leading risk factors contributing to poor overall survival (OS) of patients with CC. We identified 109 candidate genes using WGCNA and univariate Cox regression. Our established prognostic model contained six genes (MOCSI, PPP1R14A, ESM1, DES, ITGA5, and SERPINF1). Kaplan-Meier analysis indicated that high-risk patients had worse OS (hazard ratio = 4.63, p < 0.001). Our model had high predictive power according to the ROC curve. The C-index indicated that the risk score was a better predictor of survival than other clinicopathological variables. Additionally, univariate and multivariate Cox regressions indicated that the risk score was the only independent risk factor for poor OS. The risk score was also an independent predictor in the validation set (GSE52903). Bivariate survival prediction suggested that patients exhibited poor prognosis if they had high z-scores for hypoxia or angiogenesis and high risk scores.

Conclusions: We established a six-gene survival prediction model associated with hypoxia and angiogenesis. This novel model accurately predicts survival and also provides potential therapeutic targets.

Developing a 5-gene prognostic signature for cervical cancer by integrating mRNA and copy number variations

Background

Cervical cancer is frequently detected gynecological cancer all over the world. This study was designed to develop a prognostic signature for an effective prediction of cervical cancer prognosis.

Methods

Differentially expressed genes (DEGs) were identified based on copy number variation (CNV) data and expression profiles from different databases. A prognostic model was constructed and further optimized by stepwise Akaike information criterion (stepAIC). The model was then evaluated in three groups (training group, test group and validation group). Functional analysis and immune analysis were used to assess the difference between high-risk and low-risk groups.

Results

The study developed a 5-gene prognostic model that could accurately classify cervical cancer samples into high-risk and low-risk groups with distinctly different prognosis. Low-risk group exhibited more favorable prognosis and higher immune infiltration than high-risk group. Both univariate and multivariate Cox regression analysis showed that the risk score was an independent risk factor for cervical cancer.

Conclusions

The 5-gene prognostic signature could serve as a predictor for identifying high-risk cervical cancer patients, and provided potential direction for studying the mechanism or drug targets of cervical cancer. The integrated analysis of CNV and mRNA expanded a new perspective for exploring prognostic signatures in cervical cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09291-z.

A computational prognostic model of lncRNA signature for clear cell renal cell carcinoma with genome instability

Long non-coding RNAs (lncRNAs) play a critical role in genomic instability and prognosis of cancer patients, but the methods to identify genomic instability-related lncRNAs have yet to be established. In the present study, to assess the prognostic value of lncRNAs associated with genomic instability in clear cell renal cell carcinoma (ccRCC).A computational framework was established based on the mutation hypothesis and combined lncRNA expression and somatic mutation profiles of the ccRCC genome. Furthermore, a prognostic model was developed using the genome instability-derived lncRNA signature GILncSig based on three lncRNA genes (LINC02471, LINC01234, and LINC00460) and verified using multiple independent patient cohorts.This study established an effective computational method to study the role of lncRNAs in genomic instability, with potential applications in identifying new genomic instability-related cancer biomarkers.

An immune-associated ten-long noncoding RNA signature for predicting overall survival in cervical cancer

BACKGROUND

Several immune-associated long non-coding RNA (lncRNA) signatures have been reported as prognostic models in different types of cancers; however, the immune-associated lncRNA signature for predicting overall survival (OS) in cervical cancer is unknown.

METHODS

The lncRNA expression profiles and clinical data of cervical cancer were acquired from The Cancer Genome Atlas (TCGA) dataset. Immune-associated genes were extracted from the Molecular Signatures Database (MSigDB), and the immune-associated lncRNAs were extracted for Cox regression analysis. Principal component analysis (PCA) was used to distinguish the high and low risk status of cervical cancer patients. Gene Set Enrichment Analysis (GSEA) was used for functional analyses.

RESULTS

Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to construct an immune-associated ten-lncRNA signature (containing AL021807.1, AL109976.1, LINC02446, MIR4458HG, AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) for predicting OS in cervical cancer. The signature segregated the cervical cancer patients into 2 groups (high-risk group and low-risk group). The Kaplan-Meier survival curves of AL021807.1, AL109976.1, LINC02446, and MIR4458HG were statistically significant (P<0.05) and the others (including AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) were not statistically significant (P>0.05). The Kaplan-Meier survival curves of the signature were statistically significant (P=1.134e-10), and the 5-year survival rate was 0.444 in the high-risk group [95% confidence interval (CI): 0.334 to 0.590] and 0.884 in the low-risk group (95% CI: 0.807 to 0.969). The area under curve (AUC) of the receiver operating characteristic (ROC) curve of the signature was 0.833. The concordance index (C-index) of the signature was 0.788 (95% CI: 0.730 to 0.846, P=1.884778e-22). The PCA successfully distinguished the high-risk group and low-risk group based on the signature. The GSEA showed that the signature-related protein coding genes (PCGs) may participate in immunologic biological processes and pathways.

CONCLUSIONS

This study revealed that the immune-associated ten-lncRNA signature is an independent factor for cervical cancer prognosis prediction, providing a bright future for immunotherapy of cervical cancer patients.

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.

A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer

Cervical cancer (CC) patients have a poor prognosis due to the high recurrence rate. However, there are still no effective molecular signatures to predict the recurrence and survival rates for CC patients. Here, we aimed to identify a novel signature based on three types of RNAs [messenger RNA (mRNAs), microRNA (miRNAs), and long non-coding RNAs (lncRNAs)]. A total of 763 differentially expressed mRNAs (DEMs), 46 lncRNAs (DELs), and 22 miRNAs (DEMis) were identified between recurrent and non-recurrent CC patients using the datasets collected from the Gene Expression Omnibus (GSE44001; training) and The Cancer Genome Atlas (RNA- and miRNA-sequencing; testing) databases. A competing endogenous RNA network was constructed based on 23 DELs, 15 DEMis, and 426 DEMs, in which 15 DELs, 13 DEMis, and 390 DEMs were significantly associated with disease-free survival (DFS). A prognostic signature, containing two DELs (CD27-AS1, LINC00683), three DEMis (hsa-miR-146b, hsa-miR-1238, hsa-miR-4648), and seven DEMs (ARMC7, ATRX, FBLN5, GHR, MYLIP, OXCT1, RAB39A), was developed after LASSO analysis. The built risk score could effectively separate the recurrence rate and DFS of patients in the high- and low-risk groups. The accuracy of this risk score model for DFS prediction was better than that of the FIGO (International Federation of Gynecology and Obstetrics) staging (the area under receiver operating characteristic curve: training, 0.954 vs 0.501; testing, 0.882 vs 0.656; and C-index: training, 0.855 vs 0.539; testing, 0.711 vs 0.508). In conclusion, the high predictive accuracy of our signature for DFS indicated its potential clinical application value for CC patients.

Integrative Systems Biology Approaches to Identify Potential Biomarkers and Pathways of Cervical Cancer

Nowadays, cervical cancer (CC) is treated as the leading cancer among women throughout the world. Despite effective vaccination and improved surgery and treatment, CC retains its fatality rate of about half of the infected population globally. The major screening biomarkers and therapeutic target identification have now become a global concern. In the present study, we have employed systems biology approaches to retrieve the potential biomarkers and pathways from transcriptomic profiling. Initially, we have identified 76 of each up-regulated and down-regulated gene from a total of 4643 differentially expressed genes. The up-regulatory genes mainly concentrate on immune-inflammatory responses, and the down-regulatory genes are on receptor binding and gamma-glutamyltransferase. The involved pathways associated with these genes were also assessed through pathway enrichment, and we mainly focused on different cancer pathways, immunoresponse, and cell cycle pathways. After the subsequent enrichment of these genes, we have identified 12 hub genes, which play a crucial role in CC and are verified by expression profile analysis. From our study, we have found that genes LILRB2 and CYBB play crucial roles in CC, as reported here for the first time. Furthermore, the survivability of the hub genes was also assessed, and among them, finally, CXCR4 has been identified as one of the most potential differentially expressed genes that might play a vital role in the survival of CC patients. Thus, CXCR4 could be used as a prognostic and/or diagnostic biomarker and a drug target for CC.

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.

Identification of an immune-based mRNA-lncRNA signature for overall survival in cervical squamous cell carcinoma

Aim: To better predict the survival of cervical squamous cell carcinoma (CESC) patients, we aimed to construct a signature according to different immune infiltration. Methods: We downloaded the RNA sequences of CESC patients from the Cancer Genome Atlas database. By using single-sample gene set enrichment analysis, we separated the samples into high- and low-immunity groups. Then we separated the samples into training and testing datasets and performed the following analyses: univariate, least absolute shrinkage and selection operator analysis, multivariate Cox regression analyses and weighted gene coexpression network analysis using R software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes studies were performed using the Database for Annotation, Visualization and Integrated Discovery website. Results & conclusion: We finally identified a signature with three mRNAs and two lncRNAs: ADGRG5, HSH2D, ZMAT4, RBAKDN and LINC00200. In short, our study constructed an mRNA-lncRNA signature related to immune infiltration to better predict the survival of CESC patients.

Eight-lncRNA signature of cervical cancer were identified by integrating DNA methylation, copy number variation and transcriptome data

BACKGROUND

Copy number variation (CNV) suggests genetic changes in malignant tumors. Abnormal expressions of long non-coding RNAs (lncRNAs) resulted from genomic and epigenetic abnormalities play a driving role in tumorigenesis of cervical cancer. However, the role of lncRNAs-related CNV in cervical cancer remained largely unclear.

METHODS

The data of messenger RNAs (mRNAs), DNA methylation, and DNA copy number were collected from 292 cervical cancer specimens. The prognosis-related subtypes of cervical cancer were determined by multi-omics integration analysis, and protein-coding genes (PCGs) and lncRNAs with subtype-specific expressions were identified. The CNV pattern of the subtype-specific lncRNAs was analyzed to identify the subtype-specific lncRNAs. A prognostic risk model based on lncRNAs was established by least absolute shrinkage and selection operator (LASSO).

RESULTS

Multi-omics integration analysis identified three molecular subtypes incorporating 617 differentially expressed lncRNAs and 1395 differentially expressed PCGs. The 617 lncRNAs were found to intersect with disease-related lncRNAs. Functional enrichment showed that 617 lncRNAs were mainly involved in tumor metabolism, immunity and other pathways, such as p53 and cAMP signaling pathways, which are closely related to the development of cervical cancer. Finally, according to CNV pattern consistent with differential expression analysis, we established a lncRNAs-based signature consisted of 8 lncRNAs, namely, RUSC1-AS1, LINC01990, LINC01411, LINC02099, H19, LINC00452, ADPGK-AS1, C1QTNF1-AS1. The interaction of the 8 lncRNAs showed a significantly poor prognosis of cervical cancer patients, which has also been verified in an independent dataset.

CONCLUSION

Our study expanded the network of CNVs and improved the understanding on the regulatory network of lncRNAs in cervical cancer, providing novel biomarkers for the prognosis management of cervical cancer patients.

LncRNA ATXN8OS enhances tamoxifen resistance in breast cancer

Background

Tamoxifen (TAMR) resistance remains a massive obstacle for breast cancer (BC) management. The precise parts of long non-coding RNA ataxin 8 opposite strand (ATXN8OS) in BC TAMR resistance have not been defined.

Methods

The levels of ATXN8OS, vasodilator-stimulated phosphoprotein (VASP), and miR-16-5p were assessed by quantitative real-time polymerase chain reaction or western blot. Colony formation and cell viability were analyzed by MTT and colony formation assays, respectively. Targeted interactions among miR-16-5p, ATXN8OS, and VASP were confirmed by dual-luciferase reporter assay. Animal studies were performed to observe the role of ATXN8OS in TAMR sensitivity in vivo.

Results

ATXN8OS expression was increased in BC tissues and cells. ATXN8OS depletion promoted BC cell sensitivity to TAMR. ATXN8OS sequestered miR-16-5p by directly binding to miR-16-5p. The promotional effect of ATXN8OS knockdown on BC cell TAMR sensitivity was mediated by miR-16-5p. VASP was a direct target of miR-16-5p, and miR-16-5p overexpression enhanced TAMR sensitivity by VASP. Moreover, ATXN8OS regulated VASP expression by acting as a miR-16-5p sponge. In addition, ATXN8OS knockdown augmented BC TAMR sensitivity in vivo.

Conclusion

ATXN8OS knockdown enhanced BC TAMR sensitivity partially through the miR-16-5p/VASP axis, highlighting a potential therapeutic target for improving the clinical benefits of TAMR treatment in BC patients.

Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

Cervical cancer is one of the most common female malignancy that occurs worldwide and is reported to cause over 300,000 deaths in 2018. Autophagy controls the survival and death of cancerous cells by regulating the degradation process of cytoplasm and cellular organelle. In the present study, the differentially expressed autophagy-related genes (ARGs) between healthy and cancerous cervical tissues (squamous cell neoplasms) were obtained using data from GTEx and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology (GO) as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Next, we conducted univariate Cox regression assay and obtained 12 ARGs that were associated with the prognosis of cervical cancer patients. We carried out a multivariate Cox regression analysis and developed six ARG-related prognostic signature for the survival prediction of patients with squamous cell cervical cancer (Risk score = − 0.63*ATG3–0.42*BCL2 + 0.85*CD46–0.38*IFNG+ 0.23*NAMPT+ 0.82*TM9SF1). Following the calculation of risk score using the signature, the patients were divided into high and low-risk groups according to the median value. Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis (P < 0.001). The value for area under the curves corresponding to the receiver operating characteristic (ROC) was 0.740. As observed, the expression of IFNG was negatively associated with lymph node metastasis (P = 0.026), while a high-risk score was significantly associated with increased age (P = 0.008). To further validate the prognostic signature, we carried out a permutation test and confirmed the performance of the risk score. In conclusion, our study developed six ARG-related prognostic signature for patients with squamous cell cervical cancer, which might help in improving the prognostic predictions of such patients.

LINC01089 inhibits the progression of cervical cancer via inhibiting miR-27a-3p and increasing BTG2

BACKGROUND

Increasing evidence confirms that long non-coding RNA (lncRNA) has a vital impact on the procession of cervical cancer (CC). The present study aimed to investigate the clinical significance of LINC01089 in CC, as well as explore its biological functions and potential molecular mechanisms.

METHODS

A quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to investigate the expression of LINC01089 and miR-27a-3p in CC cells and tissues. Analysis of the correlation between the expression level of LINC01089 and the clinical pathological parameters of CC was then conducted. The human CC cell lines HeLa and SiHa were utilized for transfection to establish a gain-of-function model and loss-of-function models. Western blotting and a qRT-PCR were performed to detect B-cell translocation gene-2 (BTG2) expression in CC cells. Cell counting kit (CCK)-8 and 5-bromo-2-deoxyuridine (BrdU) assays were performed to detect the proliferation of CC cells. The transwell method was employed to evaluate the migration and invasion of CC cells. The interactions between LINC01089 and miR-27a-3p were verified by bioinformatics, a dual luciferase reporter gene experiment and a RNA immunoprecipitation experiment, respectively.

RESULTS

The expression of LINC01089 in CC was markedly down-regulated. The low expression of LINC01089 in CC was closely associated with a larger tumor size and positive lymph node metastasis. Moreover, overexpression of LINC01089 impeded the proliferation and metastasis of CC cells, whereas knockdown of LINC01089 had the opposite biological functions. In terms of mechanism, LINC01089 could sponge miR-27a-3p and indirectly up-regulate BTG2 expression.

CONCLUSIONS

LINC01089, as a tumor suppressor, impedes the development of CC by targeting miR-27a-3p to up-regulate BTG2 expression.

Long non-coding RNA RP11-284F21.9 functions as a ceRNA regulating PPWD1 by competitively binding to miR-769-3p in cervical carcinoma

Cervical carcinoma is the most common gynecological cancer in women worldwide. Emerging evidence has shown that long non-coding RNAs (lncRNAs) participate in multiple biological processes of cervical carcinoma tumorigenesis. We aimed to investigate the function of a novel lncRNA RP11-284F21.9 in cervical carcinoma. We found that RP11-284F21.9 was down-regulated in cervical carcinoma tissues and cell lines. Overexpression of RP11-284F21.9 inhibits proliferation, invasion and migration of cervical carcinoma cells in vitro. Further, we identified that RP11-284F21.9 directly interacted with miR-769-3p and functioned as the miR-769-3p sponge. Mechanistically, we showed that miR-769-3p regulated peptidylprolyl isomerase domain and WD repeat-containing protein1 (PPWD1) expression by targeting PPWD1 3'-UTR. Furthermore, xenograft tumor model revealed that overexpression of RP11-284F21.9 inhibited tumor growth of cervical carcinoma in vivo. Taken together, our results demonstrate that RP11-284F21.9 functions as tumor suppressor and regulates PPWD1 expression through competitively binding to miR-769-3p in cervical carcinoma, suggesting that RP11-284F21.9/miR-769-3p/PPWD1 axis could serve as a promising prognostic biomarker and therapeutic target for cervical carcinoma.

A 70-Gene Signature for Predicting Treatment Outcome in Advanced-Stage Cervical Cancer

Cervical cancer is the fourth most common cancer in women worldwide. The current approaches still have limitations in predicting the therapy outcome of each individual because of cancer heterogeneity. The goal of this study was to establish a gene expression signature that could help when choosing the right therapeutic method for the treatment of advanced-stage cervical cancer. The 666 patients were collected from four independent datasets. The 70-gene expression signature was established using univariate Cox proportional hazard regression analysis. The 70-gene signature was significantly different between low- and high-risk groups in the training dataset (p = 4.24e-6) and in the combined three validation datasets (p = 4.37e-3). Treatment of advanced-stage cancer patients in the high-risk group with molecular-targeted therapy combined with chemoradiotherapy yielded a better survival rate than with only chemoradiotherapy (p = 0.0746). However, treatment of the patients in the low-risk group with the combined therapy resulted in significantly lower survival (p = 0.00283). Functional classification of 70 genes revealed involvement of the angiogenesis pathway, specifically phosphatidylinositol 3-kinase signaling (p = 0.040), extracellular matrix organization (p = 0.0452), and cell adhesion (p = 0.011). The 70-gene signature could predict the prognosis and indicate an optimal therapeutic modality in molecular-targeted therapy or chemotherapy for advanced-stage cervical cancer.

A prognostic nomogram integrating novel biomarkers identified by machine learning for cervical squamous cell carcinoma

Background

Cervical cancer (CC) represents the fourth most frequently diagnosed malignancy affecting women all over the world. However, effective prognostic biomarkers are still limited for accurately identifying high-risk patients. Here, we provided a combination machine learning algorithm-based signature to predict the prognosis of cervical squamous cell carcinoma (CSCC).

Methods and materials

After utilizing RNA sequencing (RNA-seq) data from 36 formalin-fixed and paraffin-embedded (FFPE) samples, the most significant modules were highlighted by the weighted gene co-expression network analysis (WGCNA). A candidate genes-based prognostic classifier was constructed by the least absolute shrinkage and selection operator (LASSO) and then validated in an independent validation set. Finally, based on the multivariate analysis, a nomogram including the FIGO stage, therapy outcome, and risk score level was built to predict progression-free survival (PFS) probability.

Results

A mRNA-based signature was developed to classify patients into high- and low-risk groups with significantly different PFS and overall survival (OS) rate (training set: p < 0.001 for PFS, p = 0.016 for OS; validation set: p = 0.002 for PFS, p = 0.028 for OS). The prognostic classifier was an independent and powerful prognostic biomarker for PFS in both cohorts (training set: hazard ratio [HR] = 0.13, 95% CI 0.05–0.33, p < 0.001; validation set: HR = 0.02, 95% CI 0.01–0.04, p < 0.001). A nomogram that integrated the independent prognostic factors was constructed for clinical application. The calibration curve showed that the nomogram was able to predict 1-, 3-, and 5-year PFS accurately, and it performed well in the external validation cohorts (concordance index: 0.828 and 0.864, respectively).

Conclusion

The mRNA-based biomarker is a powerful and independent prognostic factor. Furthermore, the nomogram comprising our prognostic classifier is a promising predictor in identifying the progression risk of CSCC patients.

A five-mRNA signature associated with post-translational modifications can better predict recurrence and survival in cervical cancer

High mortality of patients with cervical cancer (CC) stresses the imperative of prognostic biomarkers for CC patients. Additionally, the vital status of post‐translational modifications (PTMs) in the progression of cancers has been reported by numerous researches. Therefore, the purpose of this research was to dig a prognostic signature correlated with PTMs for CC. We built a five‐mRNA (GALNTL6, ARSE, DPAGT1, GANAB and FURIN) prognostic signature associated with PTMs to predict both disease‐free survival (DFS) (hazard ratio [HR] = 3.967, 95% CI = 1.985‐7.927; P < .001) and overall survival (HR = 2.092, 95% CI = 1.138‐3.847; P = .018) for CC using data from The Cancer Genome Atlas database. Then, the robustness of the signature was validated using GSE44001 and the Human Protein Atlas (HPA) database. CIBERSORT algorithm analysis displayed that activated CD4 memory T cell was also an independent indicator for DFS (HR = 0.426, 95% CI = 0.186‐0.978; P = .044) which could add additional prognostic value to the signature. Collectively, the PTM‐related signature and activated CD4 memory T cell can provide new avenues for the prognostic predication of CC. These findings give further insights into effective treatment strategies for CC, providing opportunities for further experimental and clinical validations.

The Role of High-Risk Human Papillomavirus-Related Long Non-Coding RNAs in the Prognosis of Cervical Squamous Cell Carcinoma

Cervical cancer (CC) is a malignant tumor that could seriously endanger women's life and health, of which cervical squamous cell carcinoma (CESC) accounts for more than 80%. High-risk human papillomavirus (HR-HPV) infection is the primary cause of CC. The 5-year survival rate is low due to poor prognosis. We need to explore the pathogenesis of CC and seek effective biomarkers to improve prognosis. The purpose of this research is to construct an HR-HPV-related long non-coding RNA (lncRNA) signature for predicting the survival and finding the biomarkers related to CC prognosis. First, we downloaded the CESC data from The Cancer Genome Atlas (TCGA) database to find HR-HPV-related lncRNAs in CC. Then, the differentially expressed lncRNAs were analyzed by univariate and multivariate Cox regression. Six lncRNAs were found to be associated with the prognosis and can be used as independent prognostic factors. Next, based on these prognostic genes, we established a risk score model, which showed that patients with higher score had poorer prognosis and higher mortality. Moreover, the Kaplan-Meier curve of the model indicated that the model was statistically significant (p < 0.05). The survival-receiver operating characteristic curve showed that the model could also predict the survival of CC patients (the area under the curve, AUC = 0.65). More importantly, nomogram was drawn with clinical features and risk score, which verified the above conclusion, and its calibration curve and c-index index fully demonstrated that the prediction model could predict the progress of CC. We also validated the risk score model in head and neck cancer, and the results indicated that the model had obvious prognostic ability. Finally, we analyzed the correlation between clinical features and survival, and found that neoplasm cancer (p < 0.000) and risk score (p < 0.000) were independent prognostic factors for CC. In conclusion, the study established HR-HPV-related lncRNA signature, which provided a reliable prognostic tool, and was of great significance for finding the biomarkers related to HR-HPV infection in CC.

Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

A Four-Pseudogene Classifier Identified by Machine Learning Serves as a Novel Prognostic Marker for Survival of Osteosarcoma

Osteosarcoma is a common malignancy with high mortality and poor prognosis due to lack of predictive markers. Increasing evidence has demonstrated that pseudogenes, a type of non-coding gene, play an important role in tumorigenesis. The aim of this study was to identify a prognostic pseudogene signature of osteosarcoma by machine learning. A sample of 94 osteosarcoma patients’ RNA-Seq data with clinical follow-up information was involved in the study. The survival-related pseudogenes were screened and related signature model was constructed by cox-regression analysis (univariate, lasso, and multivariate). The predictive value of the signature was further validated in different subgroups. The putative biological functions were determined by co-expression analysis. In total, 125 survival-related pseudogenes were identified and a four-pseudogene (RPL11-551L14.1, HR: 0.65 (95% CI: 0.44–0.95); RPL7AP28, HR: 0.32 (95% CI: 0.14–0.76); RP4-706A16.3, HR: 1.89 (95% CI: 1.35–2.65); RP11-326A19.5, HR: 0.52(95% CI: 0.37–0.74)) signature effectively distinguished the high- and low-risk patients, and predicted prognosis with high sensitivity and specificity (AUC: 0.878). Furthermore, the signature was applicable to patients of different genders, ages, and metastatic status. Co-expression analysis revealed the four pseudogenes are involved in regulating malignant phenotype, immune, and DNA/RNA editing. This four-pseudogene signature is not only a promising predictor of prognosis and survival, but also a potential marker for monitoring therapeutic schedule. Therefore, our findings may have potential clinical significance.