Identification of a novel microRNA recurrence-related signature and risk stratification system in breast cancer

Prognostic value of miR-219-5p in relation to mortality in patients with small cell lung cancer: a retrospective, observational cohort study in China

OBJECTIVES

Small cell lung cancer (SCLC) is a lethal human malignancy, and previous studies support the contribution of microRNA to cancer progression. The prognostic value of miR-219-5p in patients with SCLC remains unclear. This study aimed to evaluate the predictive value of miR-219-5p with respect to mortality in patients with SCLC and to incorporate miR-219-5p level into a prediction model and nomogram for mortality.

DESIGN

Retrospective observational cohort study.

SETTING AND PARTICIPANTS

Our main cohort included data from 133 patients with SCLC between 1 March 2010 and 1 June 2015 from the Suzhou Xiangcheng People's Hospital. Data from 86 patients with non-SCLC at Sichuan Cancer Hospital and the First Affiliated Hospital of Soochow University were used for external validation.

OUTCOME MEASURES

Tissue samples were taken during admission and stored, and miR-219-5p levels were measured at a later date. A Cox proportional hazard model was used for survival analyses and for analysing risk factors to create a nomogram for mortality prediction. The accuracy of the model was evaluated by C-index and calibration curve.

RESULTS

Mortality in patients with a high level of miR-219-5p (≥1.50) (n=67) was 74.6%, while mortality in the low-level group (n=66) was 100.0%. Based on univariate analysis, we included significant factors (p<0.05) in a multivariate regression model: patients with high level of miR-219-5p (HR 0.39, 95% CI 0.26-0.59, p<0.001), immunotherapy (HR 0.44, 95% CI 0.23-0.84, p<0.001) and prognostic nutritional index score >47.9 (HR=0.45, 95% CI 0.24-0.83, p=0.01) remained statistically significant factors for improved overall survival. The nomogram had good accuracy in estimating the risk, with a bootstrap-corrected C-index of 0.691. External validation indicated an area under the curve of 0.749 (0.709-0.788).

CONCLUSIONS

The miR-219-5p level was associated with a reduced risk of mortality in patients with SCLC. A nomogram incorporating MiR-219-5p level and clinical factors demonstrated good accuracy in estimating the risk of overall mortality. Prospective validation of the prognostic nomogram is needed.

Systematic analysis competing endogenous RNA coexpression network as a potentially prediction prognostic biomarker for colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common types of colon cancer, represents a major public health issue due to its high incidence and mortality. Competing endogenous RNAs (ceRNAs) hypothesis has generated a great interest in the study of molecular biological mechanisms of cancer progression. The aim of this study was to identify potential prediction prognostic biomarker associated with progression of COAD and illuminate regulatory mechanisms. Two RNA sequencing datasets downloaded from the Genotype-Tissue Expression and TCGA. The differentially expressed RNAs were analyzed. Weighted correlation network analysis was used to analyze the similarity of genes model with a trait in the network. Interactions between lncRNAs, miRNAs, and target mRNAs were predicted by MiRcode, starBase, miRTarBase, miRDB, and TargetScan, and the risk score of mRNAs was established. Based on the identified prognostic signature and independent clinical factors, then the nomogram survival model was built. Totally, we identified 3537 differentially expressed mRNAs, 2379 lncRNAs, and 449 microRNAs. Based on the 8 prognosis-associated mRNAs (CCNA2 + CEBPA + NEBL + SOX9 + DLG4 + RIMKLB + TCF7L1 + TUB), the risk score was proposed. After the independent clinical prognostic factors were identified, the nomogram survival model was built. LncRNA-miRNA-mRNA ceRNA network was built by 68 lncRNAs, 4 miRNAs, and 6 mRNAs, which might serve as prognostic biomarkers of COAD. These findings suggest several genes in ceRNA network might be novel important prognostic biomarkers and potential targets for COAD. CeRNA networks could provide further insight into the mRNA-related regulatory mechanism and COAD prognosis.

Prognostic value and immune infiltration of novel signatures in colon cancer microenvironment

Background

Growing evidence has shown that the prognosis for colon cancer depends on changes in microenvironment. The purpose of this study was to elucidate the prognostic value of long noncoding RNAs (lncRNAs) related to immune microenvironment (IM) in colon cancer.

Methods

Single sample gene set enrichment analysis (ssGSEA) was used to identify the subtypes of colon cancer based on the immune genomes of 29 immune signatures. Cox regression analysis identified a lncRNA signatures associated with immune infiltration. The Tumor Immune Estimation Resource database was used to analyze immune cell content.

Results

Colon cancer samples were divided into three subtypes by unsupervised cluster analysis. Cox regression analysis identified an immune infiltration-related 5-lncRNA signature. This signature combined with clinical factors can effectively improve the predictive ability for the overall survival (OS) of colon cancer. At the same time, we found that the expression of H19 affects the content of B cells and macrophages in the microenvironment of colon cancer and affects the prognosis of colon cancer. Finally, we constructed the H19 regulatory network and further analyzed the possible mechanisms. We found that knocking down the expression of H19 can significantly inhibit the expression of CCND1 and VEGFA. At the same time, the immunohistochemical assay found that the expression of CCND1 and VEGFA protein was significantly positively correlated with the infiltration of M2 type macrophages.

Conclusion

The findings may help to formulate clinical strategies and understand the underlying mechanisms of H19 regulation. H19 may be a biomarker for targeted treatment of colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02342-8.

Role of Immune Cell-Specific Hypermethylation Signatures in Classification and Risk Stratification of Breast Cancer

Background: Epigenetic regulation, including DNA methylation, plays a major role in shaping the identity and function of immune cells. Innate and adaptive immune cells recruited into tumor tissues contribute to the formation of the tumor immune microenvironment (TIME), which is closely involved in tumor progression in breast cancer (BC). However, the specific methylation signatures of immune cells have not been thoroughly investigated yet. Additionally, it remains unknown whether immune cells-specific methylation signatures can identify subgroups and stratify the prognosis of BC patients. Methods: DNA methylation profiles of six immune cell types from eight datasets downloaded from the Gene Expression Omnibus were collected to identify immune cell-specific hypermethylation signatures (IC-SHMSs). Univariate and multivariate cox regression analyses were performed using BC data obtained from The Cancer Genome Atlas to identify the prognostic value of these IC-SHMSs. An unsupervised clustering analysis of the IC-SHMSs with prognostic value was performed to categorize BC patients into subgroups. Multiple Cox proportional hazard models were constructed to explore the role of IC-SHMSs and their relationship to clinical characteristics in the risk stratification of BC patients. Integrated discrimination improvement (IDI) was performed to determine whether the improvement of IC-SHMSs on clinical characteristics in risk stratification was statistically significant. Results: A total of 655 IC-SHMSs of six immune cell types were identified. Thirty of them had prognostic value, and 10 showed independent prognostic value. Four subgroups of BC patients, which showed significant heterogeneity in terms of survival prognosis and immune landscape, were identified. The model incorporating nine IC-SHMSs showed similar survival prediction accuracy as the clinical model incorporating age and TNM stage [3-year area under the curve (AUC): 0.793 vs. 0.785; 5-year AUC: 0.735 vs. 0.761]. Adding the IC-SHMSs to the clinical model significantly improved its prediction accuracy in risk stratification (3-year AUC: 0.897; 5-year AUC: 0.856). The results of IDI validated the statistical significance of the improvement (p < 0.05). Conclusions: Our study suggests that IC-SHMSs may serve as signatures of classification and risk stratification in BC. Our findings provide new insights into epigenetic signatures, which may help improve subgroup identification, risk stratification, and treatment management.

CDKN1C as a prognostic biomarker correlated with immune infiltrates and therapeutic responses in breast cancer patients

Breast cancer (BC) prognosis and therapeutic sensitivity could not be predicted efficiently. Previous evidence have shown the vital roles of CDKN1C in BC. Therefore, we aimed to construct a CDKN1C‐based model to accurately predicting overall survival (OS) and treatment responses in BC patients. In this study, 995 BC patients from The Cancer Genome Atlas database were selected. Kaplan‐Meier curve, Gene set enrichment and immune infiltrates analyses were executed. We developed a novel CDKN1C‐based nomogram to predict the OS, verified by the time‐dependent receiver operating characteristic curve, calibration curve and decision curve. Therapeutic response prediction was followed based on the low‐ and high‐nomogram score groups. Our results indicated that low‐CDKN1C expression was associated with shorter OS and lower proportion of naïve B cells, CD8 T cells, activated NK cells. The predictive accuracy of the nomogram for 5‐year OS was superior to the tumour‐node‐metastasis stage (area under the curve: 0.746 vs. 0.634, p < 0.001). The nomogram exhibited excellent predictive performance, calibration ability and clinical utility. Moreover, low‐risk patients were identified with stronger sensitivity to therapeutic agents. This tool can improve BC prognosis and therapeutic responses prediction, thus guiding individualized treatment decisions.

Integrated analysis of cell cycle-related genes in HR+/HER2- breast cancer

PURPOSE

This study aimed to explore the mutational characteristics and significance of cell cycle-related genes (CCGs) in hormone-receptor positive, human epidermal growth factor receptor 2 negative breast cancer (HR+/HER2- BC).

METHODS

A total of 1668 HR+/HER2- BC patients from the Guangdong Provincial People's Hospital (GDPH) cohort (n = 321) and METABRIC cohort (n = 1347) were included. Tumor samples from HR+/HER2- BC patients were collected for a next-generation sequencing assay in GDPH cohort, including 15 key CCGs. The association between CCGs alterations and overall survival were identified via the Cox regression analysis. The functional roles of the CCGs were explored via the Metascape database.

RESULTS

Based on multivariate Cox regression analysis, a set of five key CCGs (CDK4, CCND1, CDKN1A, CDKN1C, and CHEK2) were identified as independent prognostic variables in HR+/HER2- BC patients. Besides, the five-CCGs-based risk score was used to effectively classify patients into the low-risk and high-risk groups (P < 0.0001). The potential functional pathways of the CCGs included cell cycle, cyclin D associated events in G1, and regulation of G1/S transition of mitotic cell cycle.

CONCLUSION

We performed the integrated analysis of the CCGs in HR+/HER2- BC patients. It has the potential to guide individualized precision oncology therapeutic schemes in HR+/HER2- BC patients.

Identification and validation of a robust autophagy-related molecular model for predicting the prognosis of breast cancer patients

Despite a relatively low mortality rate, high recurrence rates represent a significant problem for breast cancer (BC) patients. Autophagy affects the development, progression, and prognosis of various cancers, including BC. The aim of the present study was to identify candidate autophagy-related genes (ARGs) and construct a molecular-clinicopathological signature to predict recurrence risk in BC. A 10-ARG-based signature was established in a training cohort (GEO-BC dataset GSE25066) with LASSO Cox regression and assessed in an independent validation cohort (GEO-BC GSE22219). Significant differences in recurrence-free survival were observed for high- and low-risk patients segregated based on their signature-based risk score. Time-dependent receiver operating characteristic (tdROC) analysis of signature performance demonstrated satisfactory accuracy and predictive power in both the training and validation cohorts. Moreover, we developed a nomogram to predict 3- and 5-year recurrence-free survival by combining the autophagy-related risk score and clinicopathological data. Both the tdROC and calibration curves indicated high discriminating ability for the nomogram. This study indicates that our ARG-based signature is an independent prognostic classifier for recurrence-free survival in BC. In addition, individualized survival risk assessment and treatment decisions might be effectively improved by implementing the proposed nomogram.

Down-regulation of miR-219-5p increase the risk of cancer-related mortality in patients with prostate cancer

INTRODUCTION

Prostate cancer is a common malignancy in men that is difficult to treat and carries a high risk of death. miR-219-5p is expressed in reduced amounts in many malignancies. However, the prognostic value of miR-219-5p for patients with prostate cancer remains unclear.

METHODS

We retrospectively analysed data from 213 prostate cancer patients from 10 June 2012 to 9 May 2015. Overall survival was assessed by Kaplan-Meier analysis and Cox regression models. Besides, a prediction model was constructed, and calibration curves evaluated the model's accuracy.

RESULTS

Of the 213 patients, a total of 72 (33.8%) died and the median survival time was 60.0 months. We found by multifactorial analysis that miR-219-5p deficiency increased the risk of death by nearly fourfold (HR: 3.86, 95% CI): 2.01 to 7.44, p<0.001) and the risk of progression by twofold (HR: 2.79, 95% CI: 1.68 to 4.64, p<0.001). To quantify each covariate's weight on prognosis, we screened variables by cox model to construct a predictive model. The Nomogram showed excellent accuracy in estimating death's risk, with a corrected C-index of 0.778.

CONCLUSIONS

miR-219-5p can be used as a biomarker to predict death risk in prostate cancer patients. The mortality risk prediction model constructed based on miR-219-5p has good consistency and validity in assessing patient prognosis.

Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

Background

Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients.

Methods

We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA).

Results

A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001).

Conclusions

A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.

A meta-analysis of microRNA networks regulated by melatonin in cancer: Portrait of potential candidates for breast cancer treatment

Melatonin is a ubiquitous molecule with a broad spectrum of functions including widespread anti-cancer activities. Identifying how melatonin intervenes in complex molecular signaling at the gene level is essential to guide proper therapies. Using meta-analysis approach, herein we examined the role of melatonin in regulating the expression of 46 microRNAs (miRNAs) and their target genes in breast, oral, gastric, colorectal, and prostate cancers, and glioblastoma. The deregulated miRNA-associated target genes revealed their involvement in the regulation of cellular proliferation, differentiation, apoptosis, senescence, and autophagy. Melatonin changes the expression of miRNA-associated genes in breast, gastric, and oral cancers. These genes are associated with cellular senescence, the hedgehog signaling pathway, cell proliferation, p53 signaling, and the hippo signaling pathway. Conversely, colorectal and prostate cancers as well as glioblastoma and oral carcinoma present a clear pattern of less pronounced changes in the expression of miRNA-associated genes. Most notably, colorectal cancer displayed a unique molecular change in response to melatonin. Considering breast cancer network complexity, we compared the genes found during the meta-analysis with RNA-Seq data from breast cancer-bearing mice treated with melatonin. Mechanistically, melatonin upregulated genes associated with immune responses and apoptotic processes, whereas it downregulated genes involved in cellular aggressiveness/metastasis (eg, mitosis, telomerase activity, and angiogenesis). We further characterized the expression profile of our gene subsets with human breast cancer and found eight upregulated genes and 16 downregulated genes that were appositively correlated with melatonin. Our results pose a multi-dimension network of tumor-associated genes regulated by miRNAs potentially targeted by melatonin.

Comprehensive analysis of autophagy-related prognostic genes in breast cancer

Accumulating evidence revealed that autophagy played vital roles in breast cancer (BC) progression. Thus, the aim of this study was to investigate the prognostic value of autophagy-related genes (ARGs) and develop a ARG-based model to evaluate 5-year overall survival (OS) in BC patients. We acquired ARG expression profiling in a large BC cohort (N = 1007) from The Cancer Genome Atlas (TCGA) database. The correlation between ARGs and OS was confirmed by the LASSO and Cox regression analyses. A predictive model was established based on independent prognostic variables. Thus, time-dependent receiver operating curve (ROC), calibration plot, decision curve and subgroup analysis were conducted to determine the predictive performance of ARG-based model. Four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were identified using the LASSO and multivariate Cox regression analyses. A ARG-based model was constructed based on the four ARGs and two clinicopathological risk factors (age and TNM stage), dividing patients into high-risk and low-risk groups. The 5-year OS of patients in the low-risk group was higher than that in the high-risk group (P < 0.0001). Time-dependent ROC at 5 years indicated that the four ARG-based tool had better prognostic accuracy than TNM stage in the training cohort (AUC: 0.731 vs 0.640, P < 0.01) and validation cohort (AUC: 0.804 vs 0.671, P < 0.01). The mutation frequencies of the four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were 0.9%, 2.8%, 8% and 1.3%, respectively. We built and verified a novel four ARG-based nomogram, a credible approach to predict 5-year OS in BC, which can assist oncologists in determining effective therapeutic strategies.

Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

Identification and validation of hub microRNAs dysregulated in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest cancers worldwide, and its morbidity is exacerbated by the lack of early symptoms. Bioinformatics analyses enable discovery of differentially expressed genes and non-protein-coding RNAs of potential prognostic and/or therapeutic relevance in ESCC and other cancers. Using bioinformatics tools, we searched for dysregulated miRNAs in two ESCC microarray datasets from the Gene Expression Omnibus (GEO) database. After identification of three upregulated and five downregulated miRNAs shared between databases, protein-protein interaction (PPI) network analysis was used to identify the top 10 hub-gene targets. Thereafter, a miRNA-gene interaction network predicted that most hub genes are regulated by miR-196a-5p and miR-1-3p, which are respectively upregulated and downregulated in ESCC. Functional enrichment analyses in the GO and KEGG databases indicated the potential involvement of these miRNAs in tumorigenesis-related processes and pathways, while both differential expression and correlation with T stage were demonstrated for each miRNA in a cohort of ESCC patients. Overexpression showed that miR-196a-5p increased, whereas miR-1-3p attenuated, proliferation and invasion in human ESCC cell lines grown in vitro. These findings suggest miR-196a-5p and miR-1-3p jointly contribute to ESCC tumorigenesis and are potential targets for diagnosis and treatment.

Identification of a nomogram based on long non-coding RNA to improve prognosis prediction of esophageal squamous cell carcinoma

PURPOSE

Esophageal squamous cell carcinoma (ESCC) remains a common aggressive malignancy in the world. Several long non-coding RNAs (lncRNAs) are reported to predict the prognosis of ESCC. Therefore, an in-depth research is urgently needed to further investigate the prognostic value of lncRNAs in ESCC.

RESULTS

From the training set, we identified a eight-lncRNA signature (including AP000487, AC011997, LINC01592, LINC01497, LINC01711, FENDRR, AC087045, AC137770) which separated the patients into two groups with significantly different overall survival (hazard ratio, HR = 3.79, 95% confidence interval, 95% CI [2.56-5.62]; P < 0.001). The signature was applied to the validation set (HR = 2.73, 95%CI [1.65-4.53]; P < 0.001) and showed similar prognostic values. Stratified, univariate and multivariate Cox regression analysis indicated that the signature was an independent prognostic factor for patients with ESCC. A nomogram based on the lncRNAs signature, age, grade and stage was developed and showed good accuracy for predicting 1-, 3- and 5-year survival probability of ESCC patients. We found a strong correlation between the gene significance for the survival time and T stage. Eight modules were constructed, among which the key module most closely associated with clinical information was identified.

CONCLUSIONS

Our eight-lincRNA signature and nomogram could be practical and reliable prognostic tools for esophageal squamous cell carcinoma.

METHODS

We downloaded the lncRNA expression profiles of ESCC patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets and separated to training and validation cohort. The univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identify a lncRNA-based signature. The predictive value of the signature was assessed using the Kaplan-Meier method, receiver operating characteristic (ROC) curves and area under curve (AUC). Weighted gene co-expression network analysis (WGCNA) was applied to predict the intrinsic relationship between gene expressions. In addition, we further explored the combination of clinical information and module construction.