A three microRNA-based prognostic signature for small cell lung cancer overall survival

Non-Coding RNAs as Key Regulators in Lung Cancer

For several decades, most lung cancer investigations have focused on the search for mutations in candidate genes; however, in the last decade, due to the fact that most of the human genome is occupied by sequences that do not code for proteins, much attention has been paid to non-coding RNAs (ncRNAs) that perform regulatory functions. In this review, we principally focused on recent studies of the function, regulatory mechanisms, and therapeutic potential of ncRNAs including microRNA (miRNA), long ncRNA (lncRNA), and circular RNA (circRNA) in different types of lung cancer.

MicroRNA biomarkers and their use in evaluating the prognosis of lung cancer

OBJECTIVE

We aim to use the microRNA (miRNA, micro-ribonucleic acid) data of lung cancer tissues to establish a miRNA biomarker database for lung cancer that can be used for marker screening and analysis of lung cancer prognosis.

METHODS

We obtained lung cancer-related data from The Cancer Genome Atlas (TCGA) and analyzed the miRNA expression profiles of lung cancer tissues and normal tissues using bioinformatics techniques to develop a new composite miRNA-based model for the prognostic assessment of lung cancer. The predictive power of this model was verified and evaluated based on grouping of data. We also performed RT-qPCR using lung cancer tissues from patients diagnosed with lung cancer.

RESULTS

There was a significant difference between the miRNA expression profiles of lung cancer tissues and normal tissues adjacent to the cancerous lesions. The prognostic survival of patients with lung cancer was closely related to onset age and staging (p = 0.012) but was not related to gender (p = 0.39) and race (p = 0.51). Using three methods of survival model construction, we identified three miRNA composites, namely hsa-mir-21, hsa-mir-141, and has-mir-490, as markers for the prognosis of lung cancer. As confirmed by RT-qPCR, the expressions of hsa-miR-21-5p and hsa-miR-141-5p were upregulated, whereas hsa-miR-490-3p expression was downregulated in lung cancer lesion tissues.

CONCLUSION

The three miRNA composites identified, namely hsa-mir-21, hsa-mir-141, and hsa-mir-490, have the potential to serve as novel prognostic biomarkers and therapeutic targets for lung cancer.

A novel analytical framework for risk stratification of real-world data using machine learning: A small cell lung cancer study

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans' Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA-IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.

MiRNAs in Lung Cancer: Diagnostic, Prognostic, and Therapeutic Potential

Lung cancer is the dominant emerging factor in cancer-related mortality around the globe. Therapeutic interventions for lung cancer are not up to par, mainly due to reoccurrence/relapse, chemoresistance, and late diagnosis. People are currently interested in miRNAs, which are small double-stranded (20–24 ribonucleotides) structures that regulate molecular targets (tumor suppressors, oncogenes) involved in tumorigeneses such as cell proliferation, apoptosis, metastasis, and angiogenesis via post-transcriptional regulation of mRNA. Many studies suggest the emerging role of miRNAs in lung cancer diagnostics, prognostics, and therapeutics. Therefore, it is necessary to intensely explore the miRNOME expression of lung tumors and the development of anti-cancer strategies. The current review focuses on the therapeutic, diagnostic, and prognostic potential of numerous miRNAs in lung cancer.

Biological Functions and Molecular Mechanisms of MiR-608 in Cancer

In recent years, microRNAs (miRNAs) have attracted much attention because of their prominent role in cancer. An increasing number of studies have shown that miRNAs play an important role in a variety of tumors. miR-608 has been reported to be decreased in cancers, especially in solid tumors. miR-608 is regarded as a tumor suppressor, which has been verified through a large number of experiments both in vivo and in vitro. miR-608 participates in many biological processes, including cell proliferation, invasion, migration, and apoptosis, by inhibiting transmembrane proteins and many signaling pathways. Here, we summarize the expression profile and biological functions and mechanism of miR-608, suggesting that miR-608 is an ideal diagnostic and prognostic biomarker and a treatment target for cancer.

The Role of MiRNA in Cancer: Pathogenesis, Diagnosis, and Treatment

Cancer is also determined by the alterations of oncogenes and tumor suppressor genes. These gene expressions can be regulated by microRNAs (miRNA). At this point, researchers focus on addressing two main questions: "How are oncogenes and/or tumor suppressor genes regulated by miRNAs?" and "Which other mechanisms in cancer cells are regulated by miRNAs?" In this work we focus on gathering the publications answering these questions. The expression of miRNAs is affected by amplification, deletion or mutation. These processes are controlled by oncogenes and tumor suppressor genes, which regulate different mechanisms of cancer initiation and progression including cell proliferation, cell growth, apoptosis, DNA repair, invasion, angiogenesis, metastasis, drug resistance, metabolic regulation, and immune response regulation in cancer cells. In addition, profiling of miRNA is an important step in developing a new therapeutic approach for cancer.

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Background

Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients.

Methods

Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model.

Results

There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients.

Conclusion

The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

Role of microRNAs in regulating cell proliferation, metastasis and chemoresistance and their applications as cancer biomarkers in small cell lung cancer

Small cell lung cancer (SCLC), a smoking-related highly aggressive neuroendocrine cancer, is characterized by rapid cell proliferation, early metastatic dissemination, and early relapse due to chemoresistance to first-line platinum-doublet chemotherapy. Genomically, SCLC tumors show nearly universal loss of TP53 and RB1 tumor suppressor genes, while gene expression signature classifies them into 4 distinct subgroups based on the expression patterns of lineage transcription factors - ASCL1/ASH1, NEUROD1, YAP-1, and POU2F3. Due to the lack of targetable molecular alterations and clinically useful diagnostic, prognostic and predictive biomarker, there is insignificant progress in the therapeutic management of SCLC patients. Numerous studies have shown a significant involvement of non-coding RNAs in the regulation of cell proliferation, invasion and migration, apoptosis, metastasis, and chemoresistance in various human cancers. In this review, we comprehensively discuss the role of microRNAs (miRNAs) in regulating the aforementioned biological process in SCLC. For this, we searched the scientific literature and selected studies that have evaluated the role of miRNAs in the disease pathogenesis or as a cancer biomarker in SCLC. Our review suggests that several miRNAs are involved in the pathogenesis of SCLC mainly by regulating cell proliferation, metastasis, and chemoresistance. Few studies have also demonstrated the clinical utility of miRNAs in monitoring response to chemotherapy as well as in predicting survival outcomes. However, more in-depth mechanistic studies utilizing in vivo models and multicentric studies with larger patient cohorts are needed before the applications of miRNAs as therapeutic targets or as biomarkers are translated from the laboratory into clinics.

Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

MicroRNAs: Emerging oncogenic and tumor-suppressive regulators, biomarkers and therapeutic targets in lung cancer

Lung cancer is one of the most common solid tumors worldwide and the leading cause of cancer-related deaths, causing a devastating impact on human health. The clinical prognosis of lung cancer is usually restricted by delayed diagnosis and resistance to anticancer therapies. MicroRNAs, a range of small endogenous noncoding RNAs 22 nucleotides in length, have emerged as one of the most important players in cancer initiation and progression in recent decades. Current evidence reveals pivotal roles of microRNAs in regulating cell proliferation, migration, invasion and metastasis in lung cancer. An increasing number of preclinical and clinical studies have also explored the potential of microRNAs as promising biomarkers and new therapeutic targets for lung cancer. The current review summarizes the most recent progress on the functional mechanisms of microRNAs involved in lung cancer development and progression and further discusses the clinical application of miRNAs as putative therapeutic targets for molecular diagnosis and prognostic prediction in lung cancer.

Identification of prognostic signature of non-small cell lung cancer based on TCGA methylation data

Non–small lung cancer (NSCLC) is a common malignant disease with very poor outcome. Accurate prediction of prognosis can better guide patient risk stratification and treatment decision making, and could optimize the outcome. Utilizing clinical and methylation/expression data in The Cancer Genome Atlas (TCGA), we conducted comprehensive evaluation of early-stage NSCLC to identify a methylation signature for survival prediction. 349 qualified cases of NSCLC with curative surgery were included and further grouped into the training and validation cohorts. We identified 4000 methylation loci with prognostic influence on univariate and multivariate regression analysis in the training cohort. KEGG pathway analysis was conducted to identify the key pathway. Hierarchical clustering and WGCNA co-expression analysis was performed to classify the sample phenotype and molecular subtypes. Hub 5′-C-phosphate-G-3′ (CpG) loci were identified by network analysis and then further applied for the construction of the prognostic signature. The predictive power of the prognostic model was further validated in the validation cohort. Based on clustering analysis, we identified 6 clinical molecular subtypes, which were associated with different clinical characteristics and overall survival; clusters 4 and 6 demonstrated the best and worst outcomes. We identified 17 hub CpG loci, and their weighted combination was used for the establishment of a prognostic model (RiskScore). The RiskScore significantly correlated with post-surgical outcome; patients with a higher RiskScore have worse overall survival in both the training and validation cohorts (P < 0.01). We developed a novel methylation signature that can reliably predict prognosis for patients with NSCLC.

miR-9 Does Not Regulate Lamin A Expression in Metastatic Cells from Lung Adenocarcinoma

In lung adenocarcinoma, low lamin A expression in pleural metastatic cells has been proposed as a pejorative factor. miR-9 physiologically inhibits the expression of lamin A in neural cells and seems to be a central actor in the carcinogenesis and the metastatic process in lung cancer. Thus, it could be a good candidate to explain the reduction of lamin A expression in lung adenocarcinoma cells. miR-9 expression was analyzed in 16 pleural effusions containing metastatic cells from lung adenocarcinoma and was significantly reduced in patients from the 'Low lamin A expression' group compared to patients from the 'High lamin A expression' group. Then, carcinoma cells selection by fluorescence-activated cell sorting (FACS) was performed according to epithelial membrane antigen (EMA) expression, reflecting lamin A expression. miR-9 was underexpressed in lamin A- carcinoma cells compared to lamin A+ carcinoma cells in patients from the 'Low lamin A expression' group, whereas there was no difference of miR-9 expression between lamin A+ and lamin A- carcinoma cells in patients from the 'High lamin A expression' group. These results suggest that miR-9 does not regulate lamin A expression in metastatic cells from lung adenocarcinoma. On the contrary, miR-9 expression was shown to be reduced in lamin A-negative carcinoma cells.

Identifying Cancer Targets Based on Machine Learning Methods via Chou's 5-steps Rule and General Pseudo Components

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of 'big data' derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.