Comprehensive Analysis of Cancer-Proteogenome to Identify Biomarkers for the Early Diagnosis and Prognosis of Cancer

One-pot method for preparing DNA, RNA, and protein for multiomics analysis

Typical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe a method for preparing high-quality, sequencing-ready DNA and RNA, and either intact proteins or mass-spectrometry-ready peptides for whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver, and lung tissue.

Expression of ZNF281 in colorectal cancer correlates with response to radiotherapy and survival

BACKGROUND

The treatment of Colorectal cancer (CRC) is extremely complex and survival rates vary depending on the stage of the disease at the time of diagnosis. Neoadjuvant chemoradiotherapy (NACRT), is the conventional treatment for locally advanced rectal cancer (LARC); however, the resistance to chemoradiotherapy in LARC is difficult to predict.

MATERIALS AND METHODS

In this study, clinical data of 126 LARC patients were collected and analyzed, and relevant validation was performed using GEO database and in vitro and in vivo experiments, including Western blotting and Real-time quantitative PCR, immunohistochemistry, immunofluorescence, clonogenic cell survival assays, and nude-mouse xenograft models.

RESULTS

In patients with LARC who were treated with neoadjuvant radiotherapy (NART), higher ZNF281 expression in malignant tissue was associated with a poorer prognosis and lesser degree of tumor regression. Cell and mouse experiments have shown that ZNF281 reduces the damage caused by X-rays to CRC cells and tumors grown in mice.

CONCLUSION

We found that the expression of ZNF281 predicted the radiation response of CRC cells and suggested the prognosis of patients with LARC who received neoadjuvant radiation therapy.

Comprehensive analysis of novel prognosis-related proteomic signature effectively improve risk stratification and precision treatment for patients with cervical cancer

OBJECTIVE

Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict the survival of patients with common CC.

METHODS AND RESULTS

The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas and The Cancer Genome Atlas database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan-Meier analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the area under curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins.

CONCLUSION

A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.

Dynamic Network Construction for Identifying Early Warning Signals Based On a Data-Driven Approach: Early Diagnosis Biomarker Discovery for Gastric Cancer

During the development of complex diseases, there is a critical transition from one status to another at a tipping point, which can be an early indicator of disease deterioration. To effectively enhance the performance of early risk identification, a novel dynamic network construction algorithm for identifying early warning signals based on a data-driven approach (EWS-DDA) was proposed. In EWS-DDA, the shrunken centroid was introduced to measure dynamic expression changes in assumed pathway reactions during the progression of complex disease for network construction and to define early warning signals by means of a data-driven approach. We applied EWS-DDA to perform a comprehensive analysis of gene expression profiles of gastric cancer (GC) from The Cancer Genome Atlas database and the Gene Expression Omnibus database. Six crucial genes were selected as potential biomarkers for the early diagnosis of GC. The experimental results of statistical analysis and biological analysis suggested that the six genes play important roles in GC occurrence and development. Then, EWS-DDA was compared with other state-of-the-art network methods to validate its performance. The theoretical analysis and comparison results suggested that EWS-DDA has great potential for a more complete presentation of disease deterioration and effective extraction of early warning information.

The genetic profile and molecular subtypes of human pseudomyxoma peritonei and appendiceal mucinous neoplasms: a systematic review

Pseudomyxoma peritonei (PMP) is a rare, progressive, slowly growing neoplastic condition which is poorly understood, with a 5-year progression-free survival rate as low as 48%. PMP is most commonly caused by appendiceal mucinous neoplasms (AMN), and understanding their genetic biology and pathogenicity may allow for the development of better novel systemic treatments to target key deleterious mutations and the implicated pathways. The primary aim of this systematic review was to identify the genetic profile of histologically confirmed human PMP or AMN samples. The secondary aim was to identify whether genetic marks could be used to predict patient survival. Ovid EMBASE, Ovid MEDLINE, PubMed, and Web of Science were searched to identify studies investigating the genetic profile of histologically-confirmed human PMP or AMN samples. We review findings of 46 studies totalling 2181 tumour samples. The most frequently identified somatic gene mutations in patients with PMP included KRAS (38-100%), GNAS (17-100%), and TP53 (5-23%); however, there were conflicting results of their effect on survival. Three studies identified molecular subtypes based on gene expression profiles classifying patients into oncogene-enriched, immune-enriched, and mixed molecular subtypes with prognostic value. This review summarises the current literature surrounding genetic aberrations in PMP and AMNs and their potential utility for targeted therapy. Given the recent advances in clinical trials to directly target KRAS and GNAS mutations in other cancers, we propose a rationale to explore these mutations in future pre-clinical studies in PMP with a view for a future clinical trial.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Tumor suppressive effect of scavenger receptor class A member 5 overexpression in colorectal cancer by regulating PI3K/AKT/mTOR pathway

Background

Colorectal cancer (CRC) exhibits high risks of morbidity and mortality.

Objective

To investigate the effect of scavenger receptor class A member 5 (SCRAR5) on CRC and its mechanism on modulation of cancer development.

Methods

The SCRAR5 expression in four kinds of CRC cell lines (SW620, SW480, HT29, and HCT116) was measured by quantitative PCR and western blotting, respectively. The effects of SCRAR5 abnormal expression on cell proliferation, apoptosis, and migration were analyzed by CCK-8 assay, EdU assay, colony-forming assay, flow cytometry assay, Transwell assay and wound healing assay, respectively. Meanwhile, the involvements of PI3K/AKT/mTOR pathway with the role of SCRAR5 were investigated by western blotting. Afterwards, the in vivo effects of SCRAR5 abnormal expression on CRC xenograft mice were finally investigated by evaluating tumor volume, apoptosis and Ki67 expression.

Results

SCRAR5 was lowly expressed in CRC cell lines, especially SW480 cells. Up-regulation of SCRAR5 significantly promoted cell apoptosis, reduced cell proliferation and migration in SW480 cells. Notably, SCRAR5 overexpression obviously inhibited the phosphorylation levels of PI3K, AKT, and mTOR. Reversely, SCRAR5 silence exhibited promoting effects on HT29 cells. Consistently, in vivo experiments also revealed that SCRAR5 overexpression remarkably suppressed tumor volume and Ki67 expression, as well as promoted cell apoptosis.

Conclusions

Overall, up-regulating of SCRAR5 obviously inhibited CRC tumor growth in vitro and in vivo, which might be related to PI3K/AKT/mTOR pathway.

Topic Evolution Analysis for Omics Data Integration in Cancers

One of the vital challenges for cancer diseases is efficient biomarkers monitoring formation and development are limited. Omics data integration plays a crucial role in the mining of biomarkers in the human condition. As the link between omics study on biomarkers discovery and cancer diseases is deepened, defining the principal technologies applied in the field is a must not only for the current period but also for the future. We utilize topic modeling to extract topics (or themes) as a probabilistic distribution of latent topics from the dataset. To predict the future trend of related cases, we utilize the Prophet neural network to perform a prediction correction model for existing topics. A total of 2,318 pieces of literature (from 2006 to 2020) were retrieved from MEDLINE with the query on “omics” and “cancer.” Our study found 20 topics covering current research types. The topic extraction results indicate that, with the rapid development of omics data integration research, multi-omics analysis (Topic 11) and genomics of colorectal cancer (Topic 10) have more studies reported last 15 years. From the topic prediction view, research findings in multi-omics data processing and novel biomarker discovery for cancer prediction (Topic 2, 3, 10, 11) will be heavily focused in the future. From the topic visuallization and evolution trends, metabolomics of breast cancer (Topic 9), pharmacogenomics (Topic 15), genome-guided therapy regimens (Topic 16), and microRNAs target genes (Topic 17) could have more rapidly developed in the study of cancer treatment effect and recurrence prediction.

Can conventional clinical chemistry tests help doctors in the monitoring of oncology patients?

The use of laboratory assays in the diagnostic care of oncology patients can markedly increase the efficacy of cancer treatments. Many cancer-specific biomarker assays have been developed. However, the use of these has some limitations due to their cost. Moreover, not every diagnostic laboratory can perform a complete set of these assays. On the other hand, the smart use of conventional clinical chemistry tests could improve the management of cancer. They could be especially valuable tools in the long-term care of patients with a verified diagnosis. In this review, we discuss the utilization of the conventional clinical chemistry assays for the diagnosis, monitoring and prognosis of various oncological diseases. The use of conventional blood tests to assess the levels of chemical elements, metabolites and proteins (including enzymatic activity measurements) in the care of oncology patients is discussed. We have shown that some clinical chemistry assays could be used in the management of distinct kinds of cancer.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

Epigenomics-Guided Drug Development: Recent Advances in Solving the Cancer Treatment "jigsaw puzzle"

The human epigenome plays a key role in determining cellular identity and eventually function. Drug discovery undertakings have focused mainly on the role of genomics in carcinogenesis, with the focus turning to the epigenome recently. Drugs targeting DNA and histone modifications are under development with some such as 5-azacytidine, decitabine, vorinostat, and panobinostat already approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This expert review offers a critical analysis of the epigenomics-guided drug discovery and development and the opportunities and challenges for the next decade. Importantly, the coupling of epigenetic editing techniques, such as clustered regularly interspersed short palindromic repeat (CRISPR)-CRISPR-associated protein-9 (Cas9) and APOBEC-coupled epigenetic sequencing (ACE-seq) with epigenetic drug screens, will allow the identification of small-molecule inhibitors or drugs able to reverse epigenetic changes responsible for many diseases. In addition, concrete and sustainable innovation in cancer treatment ought to integrate epigenome targeting drugs with classic therapies such as chemotherapy and immunotherapy.

Biomarkers for Early Cancer Diagnosis: Prospects for Success through the Lens of Tumor Genetics

Thousands of candidate cancer biomarkers have been proposed, but so far, few are used in cancer screening. Failure to implement these biomarkers is attributed to technical and design flaws in the discovery and validation phases, but a major obstacle stems from cancer biology itself. Oncogenomics has revealed broad genetic heterogeneity among tumors of the same histology and same tissue (or organ) from different patients, while tumors of different tissue origins also share common genetic mutations. Moreover, there is wide intratumor genetic heterogeneity among cells within any single neoplasm. These findings seriously limit the prospects of finding a single biomarker with high specificity for early cancer detection. Current research focuses on developing biomarker panels, with data assessment by machine-learning algorithms. Whether such approaches will overcome the inherent limitations posed by tumor biology and lead to tests with true clinical value remains to be seen.

Molecular and Genetic Markers in Appendiceal Mucinous Tumors: A Systematic Review

INTRODUCTION

The role of somatic mutation profiling in the management of appendiceal mucinous tumors (AMTs) is evolving. Using a systematic review, we identified somatic alterations (SAs) that comprise histopathologic types of AMTs and those associated with aggressive clinical phenotypes.

METHODS

MEDLINE/PubMed was searched for studies on AMTs including molecular markers or genomic alterations, published between 1990 and 2018. Studies were grouped under low- and high-grade histological type for primary and metastatic tumors.

RESULTS

Twenty-one studies involving 1099 tumors (primary/metastatic) were identified. Seven studies involving 101 primary low-grade AMTs identified KRAS (76.5%) as the predominant SA. Four studies noted GNAS in 45.2% of 42 low-grade appendiceal mucinous neoplasms, and KRAS was identified in 74.4% of 14 studies with 238 low-grade pseudomyxoma peritonei (PMP). GNAS was noted in 56% of 101 tumors and TP53 was noted in only 9.7% of 31 tumors. Primary high-grade tumors demonstrated lower SAs in KRAS (50.4% of 369 tumors) and GNAS (27.8% of 97 tumors), and higher SAs in TP53 (26.0% of 123 tumors). In high-grade PMP, SAs were noted in KRAS (55.0% of 200 tumors), GNAS (35.0% of 60 tumors), and TP53 (26.3% of 19 tumors). No clear association was noted between SAs and survival.

CONCLUSIONS

KRAS and GNAS are frequently altered in low-grade AMTs, while TP53 is frequently altered in high-grade AMTs, with no apparent change in expression between primary and metastatic tumors. Although SAs may provide valuable insights into variability in tumor biology, larger studies utilizing clinically annotated genomic databases from multi-institutional consortiums are needed to improve their identification and clinical applicability.

Neurotheranostics as personalized medicines

The discipline of neurotheranostics was forged to improve diagnostic and therapeutic clinical outcomes for neurological disorders. Research was facilitated, in largest measure, by the creation of pharmacologically effective multimodal pharmaceutical formulations. Deployment of neurotheranostic agents could revolutionize staging and improve nervous system disease therapeutic outcomes. However, obstacles in formulation design, drug loading and payload delivery still remain. These will certainly be aided by multidisciplinary basic research and clinical teams with pharmacology, nanotechnology, neuroscience and pharmaceutic expertise. When successful the end results will provide "optimal" therapeutic delivery platforms. The current report reviews an extensive body of knowledge of the natural history, epidemiology, pathogenesis and therapeutics of neurologic disease with an eye on how, when and under what circumstances neurotheranostics will soon be used as personalized medicines for a broad range of neurodegenerative, neuroinflammatory and neuroinfectious diseases.

Identification of Novel MicroRNAs and Their Diagnostic and Prognostic Significance in Oral Cancer

Background: Oral cancer is one of the most prevalent cancers worldwide. Despite that the oral cavity is easily accessible for clinical examinations, oral cancers are often not promptly diagnosed. Furthermore, to date no effective biomarkers are available for oral cancer. Therefore, there is an urgent need to identify novel biomarkers able to improve both diagnostic and prognostic strategies. In this context, the development of innovative high-throughput technologies for molecular and epigenetics analyses has generated a huge amount of data that may be used for the identification of new cancer biomarkers. Methods: In the present study, GEO DataSets and TCGA miRNA profiling datasets were analyzed in order to identify miRNAs with diagnostic and prognostic significance. Furthermore, several computational approaches were adopted to establish the functional roles of these miRNAs. Results: The analysis of datasets allowed for the identification of 11 miRNAs with a potential diagnostic role for oral cancer. Additionally, eight miRNAs associated with patients' prognosis were also identified; six miRNAs predictive of patients' overall survival (OS) and one, hsa-miR-let.7i-3p, associated with tumor recurrence. Conclusion: The integrated analysis of different miRNA expression datasets allows for the identification of a set of miRNAs that, after validation, may be used for the early detection of oral cancers.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Origins and clinical relevance of proteoforms in pediatric malignancies

INTRODUCTION

Cancer changes the proteome in complex ways that reach well beyond simple changes in protein abundance. Genomic and transcriptional variations and post-translational protein modification create functional variants of a protein, known as proteoforms. Childhood cancers have fewer genomic alterations but show equally dramatic phenotypic changes as malignant cells in adults. Therefore, unraveling the complexities of the proteome is even more important in pediatric malignancies. Areas covered: In this review, the biological origins of proteoforms and technological advancements in the study of proteoforms are discussed. Particular emphasis is given to their implication in childhood malignancies and the critical role of cancer-specific proteoforms for the next generation of cancer therapies and diagnostics. Expert opinion: Recent advancements in technology have led to a better understanding of the underlying mechanisms of tumorigenesis. This has been critical for the development of more effective and less harmful treatments that are based on direct targeting of altered proteins and deregulated pathways. As proteome coverage and the ability to detect complex proteoforms increase, the most need for change is in data compilation and database availability to mediate high-level data analysis and allow for better functional annotation of proteoforms.

Proteogenomics in the context of the Human Proteome Project (HPP)

INTRODUCTION

The technological and scientific progress performed in the Human Proteome Project (HPP) has provided to the scientific community a new set of experimental and bioinformatic methods in the challenging field of shotgun and SRM/MRM-based Proteomics. The requirements for a protein to be considered experimentally validated are now well-established, and the information about the human proteome is available in the neXtProt database, while targeted proteomic assays are stored in SRMAtlas. However, the study of the missing proteins continues being an outstanding issue. Areas covered: This review is focused on the implementation of proteogenomic methods designed to improve the detection and validation of the missing proteins. The evolution of the methodological strategies based on the combination of different omic technologies and the use of huge publicly available datasets is shown taking the Chromosome 16 Consortium as reference. Expert commentary: Proteogenomics and other strategies of data analysis implemented within the C-HPP initiative could be used as guidance to complete in a near future the catalog of the human proteins. Besides, in the next years, we will probably witness their use in the B/D-HPP initiative to go a step forward on the implications of the proteins in the human biology and disease.

Prognostic value of spleen tyrosine kinase in human solid tumors

Background

Spleen tyrosine kinase (SYK) was reported to be dysregulated in solid tumors and played an important role in cancer progression. However, the clinical and prognostic values of SYK in solid tumors remain unclear. This meta-analysis investigated the association between SYK expression and clinical outcomes in the patients with solid tumors.

Methods

A comprehensive literature search was conducted by screening the online electronic databases of PubMed, Embase, and the China National Knowledge Infrastructure. The hazard ratio (HR) with its corresponding 95% CI was used to explore the prognostic value of SYK.

Results

We analyzed a total of 1,075 patients from 10 studies, which met the criteria for this meta-analysis. Our pooled results demonstrated that a low expression of SYK did not correlate significantly with shorter overall survival (OS; HR=0.64, 95% CI: 0.34-1.21, P=0.169) or poorer disease-free survival (HR=0.51, 95% CI: 0.13-2.02, P=0.338). However, in a subgroup analysis based on tumor type and test method, under-expression of SYK was positively associated with worse OS in the groups of breast cancer (BC; HR=0.51, 95% CI: 0.32-0.80, P=0.003), hepatocellular carcinoma (HCC; HR=0.44, 95% CI: 0.29-0.69, P<0.001), methylation (HR=0.39, 95% CI: 0.30-0.51, P<0.001), and quantitative reverse transcription polymerase chain reaction (HR=0.24, 95% CI: 0.09-0.65, P=0.005).

Conclusion

This meta-analysis demonstrated that under-expression of SYK may serve as a predictive biomarker for poor prognosis in BC and HCC patients. In other solid tumors, the clinical usefulness should be confirmed by large-scale studies.

Signal-Targeted Therapies and Resistance Mechanisms in Pancreatic Cancer: Future Developments Reside in Proteomics

For patients with metastatic pancreatic cancer that are not eligible for surgery, signal-targeted therapies have so far failed to significantly improve survival. These therapeutic options have been tested in phase II/III clinical trials mostly in combination with the reference treatment gemcitabine. Innovative therapies aim to annihilate oncogenic dependency, or to normalize the tumoural stroma to allow immune cells to function and/or re-vascularisation to occur. Large scale transcriptomic and genomic analysis revealed that pancreatic cancers display great heterogeneity but failed to clearly delineate specific oncogene dependency, besides oncogenic Kras. Beyond these approaches, proteomics appears to be an appropriate approach to classify signal dependency and to identify specific alterations at the targetable level. However, due to difficulties in sampling, proteomic data for this pathology are scarce. In this review, we will discuss the current state of clinical trials for targeted therapies against pancreatic cancer. We will then highlight the most recent proteomic data for pancreatic tumours and their metastasis, which could help to identify major oncogenic signalling dependencies, as well as provide future leads to explain why pancreatic tumours are intrinsically resistant to signal-targeted therapies. We will finally discuss how studies on phosphatidylinositol-3-kinase (PI3K) signalling, as the paradigmatic pro-tumoural signal downstream of oncogenic Kras in pancreatic cancer, would benefit from exploratory proteomics to increase the efficiency of targeted therapies.

Integrated analysis of long non‑coding RNA competing interactions revealed potential biomarkers in cervical cancer: Based on a public database

Cervical cancer (CC) is a common gynecological malignancy in women worldwide. Using an RNA sequencing profile from The Cancer Genome Atlas (TCGA) and the CC patient information, the aim of the present study was to identify potential long non‑coding RNA (lncRNA) biomarkers of CC using bioinformatics analysis and building a competing endogenous RNA (ceRNA) co‑expression network. Results indicated several CC‑specific lncRNAs, which were associated with CC clinical information and selected some of them for validation and evaluated their diagnostic values. Bioinformatics analysis identified 51 CC‑specific lncRNAs (fold‑change >2 and P<0.05), and 42 of these were included in ceRNA network consisting of lncRNA‑miRNA‑mRNA interactions. Further analyses revealed that differential expression levels of 19 lncRNAs were significantly associated with different clinical features (P<0.05). A total of 11 key lncRNAs in the ceRNA network for reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) analysis to detect their expression levels in 31 pairs of CC clinical samples. The results indicated that 7 lncRNAs were upregulated and 4 lncRNAs were downregulated in CC patients. The fold‑changes between the RT‑qPCR experiments and the TCGA bioinformatics analyses were the same. Furthermore, the area under the receiver operating characteristic (ROC) curve of four lncRNAs (EMX20S, MEG3, SYS1‑DBNDD2 and MIR9‑3HG) indicated that their combined use may have a significant diagnostic value in CC (P<0.05). To the best of our knowledge, the present study is the first to have identified CC‑specific lncRNAs to construct a ceRNA network and has also provided new insights for further investigation of a lncRNA‑associated ceRNA network in CC. In additon, the verification results suggested that the method of bioinformatics analysis and screening of lncRNAs was accurate and reliable. To conclude, the use of multiple lncRNAs may thus improve diagnostic efficacy in CC. In addition, these specific lncRNAs may serve as new candidate biomarkers for clinical diagnosis, classification and prognosis of CC.

Integrated TCGA analysis implicates lncRNA CTB-193M12.5 as a prognostic factor in lung adenocarcinoma

Background

Lung cancer is a malignant tumor with the highest incidence and mortality around the world. Recent advances in RNA sequencing technology have enabled insights into long non-coding RNAs (lncRNAs), a previously largely overlooked species in dissecting lung cancer pathology.

Methods

In this study, we used a comprehensive bioinformatics analysis strategy to identify lncRNAs closely associated with lung adenocarcinoma, using the RNA sequencing datasets collected from more than 500 lung adenocarcinoma patients and deposited at The Cancer Genome Atlas (TCGA) database.

Results

Differential expression analysis highlighted lncRNAs CTD-2510F5.4 and CTB-193M12.5, both of which were significantly upregulated in cancerous specimens. Moreover, network analyses showed highly correlated expression levels of both lncRNAs with those of differentially expressed protein-coding genes, and suggested central regulatory roles of both lncRNAs in the gene co-expression network. Importantly, expression of CTB-193M12.5 showed strong negative correlation with patient survival.

Conclusions

Our study mined existing TCGA datasets for novel factors associated with lung adenocarcinoma, and identified a largely unknown lncRNA as a potential prognostic factor. Further investigation is warranted to characterize the roles and significance of CTB-193M12.5 in lung adenocarcinoma biology.

Electronic supplementary material

The online version of this article (10.1186/s12935-018-0513-3) contains supplementary material, which is available to authorized users.