snoRNA and piRNA expression levels modified by tobacco use in women with lung adenocarcinoma

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

Background

Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity.

Methods

We integrated DNB analysis with graph convolutional neural networks (GCN) to identify critical transitions during hepatocellular carcinoma development in a mouse model. A DNB-GCN model was constructed using transcriptomic data and gene expression levels as node features.

Results

DNB analysis identified a critical transition point at 7 weeks of age despite histological examinations being unable to detect cancerous changes at that time point. The DNB-GCN model achieved 100% accuracy in classifying healthy and cancerous mice, and was able to accurately predict the health status of newly introduced mice.

Conclusion

The integration of DNB analysis and GCN demonstrates potential for the early detection of complex diseases by capturing network structures and molecular features that conventional biomarker discovery methods overlook. The approach warrants further development and validation.

The emerging role of the piRNA/PIWI complex in respiratory tract diseases

PIWI-interacting RNA (piRNA) is a class of recently discovered small non-coding RNA molecules with a length of 18-33 nt that interacts with the PIWI protein to form the piRNA/PIWI complex. The PIWI family is a subfamily of Argonaute (AGO) proteins that also contain the AGO family which bind to microRNA (miRNA). Recently studies indicate that piRNAs are not specific to in the mammalian germline, they are also expressed in a tissue-specific manner in a variety of human tissues and participated in various of diseases, such as cardiovascular, neurological, and urinary tract diseases, and are especially prevalent in malignant tumors in these systems. However, the functions and abnormal expression of piRNAs in respiratory tract diseases and their underlying mechanisms remain incompletely understood. In this review, we discuss current studies summarizing the biogenetic processes, functions, and emerging roles of piRNAs in respiratory tract diseases, providing a reference value for future piRNA research.

The emerging diagnostic and therapeutic roles of small nucleolar RNAs in lung diseases

Small nucleolar RNAs (snoRNAs) are non-coding RNA molecules that range from 60 to 300 nucleotides in length and are primarily located in the nucleoli of cells. They play a critical role in modifying ribosomal RNA and can also regulate alternative splicing and posttranscriptional modification of mRNA. Alterations in snoRNA expression can affect numerous cellular processes, including cell proliferation, apoptosis, angiogenesis, fibrosis, and inflammation, making them a promising target for diagnostics and treatment of various human pathologies. Recent evidence suggests that abnormal snoRNA expression is strongly associated with the development and progression of several lung diseases, such as lung cancer, asthma, chronic obstructive pulmonary disease, and pulmonary hypertension, as well as COVID-19. While few studies have shown a causal relationship between snoRNA expression and disease onset, this research field presents exciting opportunities for identifying new biomarkers and therapeutic targets in lung disease. This review discusses the emerging role and molecular mechanisms of snoRNAs in the pathogenesis of lung diseases, focusing on research opportunities, clinical studies, biomarkers, and therapeutic potential.

Extracellular Vesicle (EVs) Associated Non-Coding RNAs in Lung Cancer and Therapeutics

Lung cancer is one of the most lethal forms of cancer, with a very high mortality rate. The precise pathophysiology of lung cancer is not well understood, and pertinent information regarding the initiation and progression of lung cancer is currently a crucial area of scientific investigation. Enhanced knowledge about the disease will lead to the development of potent therapeutic interventions. Extracellular vesicles (EVs) are membrane-bound heterogeneous populations of cellular entities that are abundantly produced by all cells in the human body, including the tumor cells. A defined class of EVs called small Extracellular Vesicles (sEVs or exosomes) carries key biomolecules such as RNA, DNA, Proteins and Lipids. Exosomes, therefore, mediate physiological activities and intracellular communication between various cells, including constituent cells of the tumor microenvironment, namely stromal cells, immunological cells, and tumor cells. In recent years, a surge in studying tumor-associated non-coding RNAs (ncRNAs) has been observed. Subsequently, studies have also reported that exosomes abundantly carry different species of ncRNAs and these exosomal ncRNAs are functionally involved in cancer initiation and progression. Here, we discuss the function of exosomal ncRNAs, such as miRNAs and long non-coding RNAs, in the pathophysiology of lung tumors. Further, the future application of exosomal-ncRNAs in clinics as biomarkers and therapeutic targets in lung cancer is also discussed due to the multifaceted influence of exosomes on cellular physiology.

Longitudinal change in blood DNA epigenetic signature after smoking cessation

Cigarette smoking is associated with epigenetic changes that may be reversible following smoking cessation. Whole blood DNA methylation was evaluated in Framingham Heart Study Offspring (n = 169) and Third Generation (n = 30) cohort participants at two study visits 6 years apart and in Atherosclerosis Risk in Communities (ARIC) study (n = 222) participants at two study visits 20 years apart. Changes in DNA methylation (delta β values) at 483,565 cytosine-phosphate-guanine (CpG) sites and differentially methylated regions (DMRs) were compared between participants who were current, former, or never smokers at both visits (current-current, former-former, never-never, respectively), versus those who quit in the interim (current-former). Interim quitters had more hypermethylation at four CpGs annotated to AHRR, one CpG annotated to F2RL3, and one intergenic CpG (cg21566642) compared with current-current smokers (FDR < 0.02 for all), and two significant DMRs were identified. While there were no significant differentially methylated CpGs in the comparison of interim quitters and former-former smokers, 106 DMRs overlapping with small nucleolar RNA were identified. As compared with all non-smokers, current-current smokers additionally had more hypermethylation at two CpG sites annotated to HIVEP3 and TMEM126A, respectively, and another intergenic CpG (cg14339116). Gene transcripts associated with smoking cessation were implicated in immune responses, cell homoeostasis, and apoptosis. Smoking cessation is associated with early reversion of blood DNA methylation changes at CpG sites annotated to AHRR and F2RL3 towards those of never smokers. Associated gene expression suggests a role of longitudinal smoking-related DNA methylation changes in immune response processes.

A Unified Computational Framework for a Robust, Reliable, and Reproducible Identification of Novel miRNAs From the RNA Sequencing Data

In eukaryotic cells, miRNAs regulate a plethora of cellular functionalities ranging from cellular metabolisms, and development to the regulation of biological networks and pathways, both under homeostatic and pathological states like cancer.Despite their immense importance as key regulators of cellular processes, accurate and reliable estimation of miRNAs using Next Generation Sequencing is challenging, largely due to the limited availability of robust computational tools/methods/pipelines. Here, we introduce miRPipe, an end-to-end computational framework for the identification, characterization, and expression estimation of small RNAs, including the known and novel miRNAs and previously annotated pi-RNAs from small-RNA sequencing profiles. Our workflow detects unique novel miRNAs by incorporating the sequence information of seed and non-seed regions, concomitant with clustering analysis. This approach allows reliable and reproducible detection of unique novel miRNAs and functionally same miRNAs (paralogues). We validated the performance of miRPipe with the available state-of-the-art pipelines using both synthetic datasets generated using the newly developed miRSim tool and three cancer datasets (Chronic Lymphocytic Leukemia, Lung cancer, and breast cancer). In the experiment over the synthetic dataset, miRPipe is observed to outperform the existing state-of-the-art pipelines (accuracy: 95.23% and F1-score: 94.17%). Analysis on all the three cancer datasets shows that miRPipe is able to extract more number of known dysregulated miRNAs or piRNAs from the datasets as compared to the existing pipelines.

Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.

Finding differentially expressed sRNA-Seq regions with srnadiff

Small RNAs (sRNAs) encompass a great variety of molecules of different kinds, such as microRNAs, small interfering RNAs, Piwi-associated RNA, among others. These sRNAs have a wide range of activities, which include gene regulation, protection against virus, transposable element silencing, and have been identified as a key actor in determining the development of the cell. Small RNA sequencing is thus routinely used to assess the expression of the diversity of sRNAs, usually in the context of differentially expression, where two conditions are compared. Tools that detect differentially expressed microRNAs are numerous, because microRNAs are well documented, and the associated genes are well defined. However, tools are lacking to detect other types of sRNAs, which are less studied, and whose precursor RNA is not well characterized. We present here a new method, called srnadiff, which finds all kinds of differentially expressed sRNAs. To the extent of our knowledge, srnadiff is the first tool that detects differentially expressed sRNAs without the use of external information, such as genomic annotation or additional sequences of sRNAs.

PIWI‑interacting RNA in cancer: Molecular mechanisms and possible clinical implications (Review)

PIWI‑interacting RNA is a class of non‑coding small RNA that is ~30 nt long and is primarily found in mammalian germ cells from mice and humans. In cooperation with the members of PIWI protein family, this macromolecule participates in germ cell development, inhibits DNA self‑-replication and maintains genomic stability. Increasing evidence has demonstrated that PIWI‑interacting RNA (piRNAs) are abnormally expressed in various human cancers, such as liver cancer, stomach cancer, colorectal cancer, osteosarcoma, breast cancer, lung cancer, prostate cancer, etc. piRNAs abnormal expression is also associated with the occurrence and development of human cancers, such as liver cancer, stomach cancer, colorectal cancer, etc. Despite their unclear molecular mechanisms, piRNAs may act as oncogenes or tumor suppressors by interacting with multiple cancer‑related signal pathways including STAT3/Bcl‑xl or coding genes, such as heat shock transcription factor‑1. Hence, piRNAs may be potential markers and targets and provide new opportunities for cancer diagnosis, treatment or prognosis monitoring. The current review mainly aims to highlight the latest research progress made in the biological functions and regulation of piRNAs in mammals, their involvement in various cancer forms and their potential clinical applications.

Targeting Epigenetics in Lung Cancer

The epigenetic landscape, which in part includes DNA methylation, chromatin organization, histone modifications, and noncoding RNA regulation, greatly contributes to the heterogeneity that makes developing effective therapies for lung cancer challenging. This review will provide an overview of the epigenetic alterations that have been implicated in all aspects of cancer pathogenesis and progression as well as summarize clinical applications for targeting epigenetics in the treatment of lung cancer.

MiRNAs: A Powerful Tool in Deciphering Gynecological Malignancies

Accumulated evidence on the clinical roles of microRNAs (miRNAs) in cancer prevention and control has revealed the emergence of new genetic techniques that have improved the understanding of the mechanisms essential for pathology induction and progression. Comprehension of the modifications and individual differences of miRNAs and their interactions in the pathogenesis of gynecological malignancies, together with an understanding of the phenotypic variations have considerably improved the management of the diagnosis and personalized treatment for different forms of cancer. In recent years, miRNAs have emerged as signaling molecules in biological pathways involved in different categories of cancer and it has been demonstrated that these molecules could regulate cancer-relevant processes, our focus being on malignancies of the gynecologic tract. The aim of this paper is to summarize novel research findings in the literature regarding the parts that miRNAs play in cancer-relevant processes, specifically regarding gynecological malignancy, while emphasizing their pivotal role in the disruption of cancer-related signaling pathways.

H/ACA snoRNA levels are regulated during stem cell differentiation

H/ACA small nucleolar RNAs (snoRNAs) guide pseudouridylation as part of a small nucleolar ribonucleoprotein complex (snoRNP). Disruption of H/ACA snoRNA levels in stem cells impairs pluripotency, yet it remains unclear how H/ACA snoRNAs contribute to differentiation. To determine if H/ACA snoRNA levels are dynamic during differentiation, we comprehensively profiled H/ACA snoRNA abundance in multiple murine cell types and during differentiation in three cellular models, including mouse embryonic stem cells and mouse myoblasts. We determined that the profiles of H/ACA snoRNA abundance are cell-type specific, and we identified a subset of snoRNAs that are specifically regulated during differentiation. Additionally, we demonstrated that a decrease in Snora27 abundance upon differentiation corresponds to a decrease in pseudouridylation of its target site within the E-site transfer RNA (tRNA) binding region of the 28S ribosomal RNA (rRNA) in the large ribosomal subunit. Together, these data point toward a potential model in which H/ACA snoRNAs are specifically regulated during differentiation to alter pseudouridylation and fine tune ribosome function.

PIWI-interacting RNAs in human cancer

P-element-induced wimpy testis (PIWI) interacting RNAs (piRNAs) are a class of small regulatory RNAs mechanistically similar to but much less studied than microRNAs and small interfering RNAs. Today the best understood function of piRNAs is transposon control in animal germ cells, which has earned them the name 'guardians of the germline'. Several molecular/cellular characteristics of piRNAs, including high sequence diversity, lack of secondary structures, and target-oriented generation seem to serve this purpose. Recently, aberrant expressions of piRNAs and PIWI proteins have been implicated in a variety of malignant tumors and associated with cancer hallmarks such as cell proliferation, inhibited apoptosis, invasion, metastasis and increased stemness. Researchers have also demonstrated multiple mechanisms of piRNA-mediated target deregulation associated with cancer initiation, progression or dissemination. We review current research findings on the biogenesis, normal functions and cancer associations of piRNAs, highlighting their potentials as cancer diagnostic/prognostic biomarkers and therapeutic tools. Whenever applicable, we draw connections with other research fields to encourage intercommunity conversations. We also offer recommendations and cautions regarding the general process of cancer-related piRNA studies and the methods/tools used at each step. Finally, we call attention to some issues that, if left unsolved, might impede the future development of this field.

Exosomal piRNA profiling revealed unique circulating piRNA signatures of cholangiocarcinoma and gallbladder carcinoma

Cholangiocarcinoma (CCA) and gallbladder carcinoma (GBC) are biliary tract cancers with poor five-year survival and high recurrence rates. Both CCA and GBC patients suffer from lack of circulating diagnostic biomarkers at the early stage. Extracellular vesicles, especially exosomes, have been emerged as promising diagnostic sources for cancers due to easy and quick accessibility. Hence, identification of exosomal biomarkers provides a novel strategy for CCA and GBC diagnosis. Here, five CCA patients and four GBC patients were enrolled for exosomal small RNA sequencing. Our data showed that exosomal piwi-interacting RNA (piRNA) populations were altered in the plasma of CCA and GBC patients. In comparison to healthy individuals, 694 and 323 piRNAs were upregulated in CCA and GBC, respectively, while 36 and 191 piRNAs were downregulated. Interestingly, sequencing results predicted that piR-2660989, piR-10506469, piR-20548188, piR-10822895, piR-hsa-23209, and piR-18044111 were upregulated in both CCA and GBC plasma. Importantly, we further included blood samples from 50 health individuals, 40 CCA patients, and 25 GBC patients and found that piR-10506469 were significantly increased in the exosomes of plasma from both CCA and GBC patients. Moreover, we analyzed the expression levels of differentially expressed exosomal piRNAs in the plasma of CCA and GBC patient before and after surgeries and found that piR-10506469 and piR-20548188 were significantly decreased in patients underwent surgeries. Taken together, our data revealed that exosomal piRNAs those are differentially expressed in CCA and GBC plasma may serve as potential biomarkers for the diagnosis of CCA and GBC.

Computer-aided biomarker discovery for precision medicine: data resources, models and applications

Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.

Prognostic value of small nucleolar RNAs (snoRNAs) for colon adenocarcinoma based on RNA sequencing data

Although the molecular studies of single gastrointestinal tumors have been widely reported by media, it is not clear about the function of small nucleolar RNA (snoRNA) in the progression, development and prognostic significance in colon adenocarcinoma, and its certain molecular mechanisms and functions remain to be studied. This study aims to dig out the gene expression data profile of colon adenocarcinoma and construct the prognostic molecular pathology prediction-evaluation, ultimately revealing the clinical prognostic value of snoRNA in colon adenocarcinoma. 932 differentially expressed snoRNAs of the colon adenocarcinoma were obtained by edgeR R package. Only 4 prognostically-significant snoRNAs (SNORD14E, SNORD67, SNORD12C, and SNORD17) (P < 0.05) were discovered after univariate COX regression mode analysis. Moreover, through multivariate COX regression mode analysis, 2 prognostically-significant snoRNAs (SNORD14E and SNORD67) (P < 0.05) were obtained. Using the above 473 COAD samples, a prognostic model of risk score was constructed. The inflection point of the prognostic risk score acted as a boundary to divide the patients into high-risk and low-risk groups. The K-M survival curve of the prognostic model of risk score revealed that high risk group has a lower survival rate (P < 0.05). The research has successfully provided valuable prognostic factors and prognostic models for patients with malignant colon tumor.

snoRNAs Offer Novel Insight and Promising Perspectives for Lung Cancer Understanding and Management

Small nucleolar RNAs (snoRNAs) are non-coding RNAs localized in the nucleolus, where they participate in the cleavage and chemical modification of ribosomal RNAs. Their biogenesis and molecular functions have been extensively studied since their identification in the 1960s. However, their role in cancer has only recently started to emerge. In lung cancer, efforts to profile snoRNA expression have enabled the definition of snoRNA-related signatures, not only in tissues but also in biological fluids, exposing these small RNAs as potential non-invasive biomarkers. Moreover, snoRNAs appear to be essential actors of lung cancer onset and dissemination. They affect diverse cellular functions, from regulation of the cell proliferation/death balance to promotion of cancer cell plasticity. snoRNAs display both oncogenic and tumor suppressive activities that are pivotal in lung cancer tumorigenesis and progression. Altogether, we review how further insight into snoRNAs may improve our understanding of basic lung cancer biology and the development of innovative diagnostic tools and therapies.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

The Function of Non-Coding RNAs in Lung Cancer Tumorigenesis

Lung cancer is the most prevalent and deadliest cancer worldwide. A significant part of lung cancer studies is dedicated to the expression alterations of non-coding RNAs. The non-coding RNAs are transcripts that cannot be translated into proteins. While the study of microRNAs and siRNAs in lung cancer received a lot of attention over the last decade, highly efficient therapeutic option or the diagnostic methods based on non-coding RNAs are still lacking. Because of this, it is of utmost importance to direct future research on lung cancer towards analyzing other RNA types for which the currently available data indicates that are essential at modulating lung tumorigenesis. Through our review of studies on this subject, we identify the following non-coding RNAs as tumor suppressors: ts-46, ts-47, ts-101, ts-53, ts-3676, ts-4521 (tRNA fragments), SNORD116-26, HBII-420, SNORD15A, SNORA42 (snoRNAs), piRNA-like-163, piR-35127, the piR-46545 (piRNAs), CHIAP2, LOC100420907, RPL13AP17 (pseudogenes), and uc.454 (T-UCR). We also found non-coding RNAs with tumor-promoting function: tRF-Leu-CAG, tRNA-Leu, tRNA-Val (tRNA fragments), circ-RAD23B, circRNA 100146, circPVT1, circFGFR3, circ_0004015, circPUM1, circFLI1, circABCB10, circHIPK3 (circRNAs), SNORA42, SNORA3, SNORD46, SNORA21, SNORD28, SNORA47, SNORD66, SNORA68, SNORA78 (snoRNAs), piR-65, piR-34871, piR-52200, piR651 (piRNAs), hY4 5’ fragments (YRNAs), FAM83A-AS1, WRAP53, NKX2-1-AS1 (NATs), DUXAP8, SFTA1P (pseudogene transcripts), uc.338, uc.339 (T-UCRs), and hTERC.

The nucleolus, an ally, and an enemy of cancer cells

The rates of ribosome production by a nucleolus and of protein biosynthesis by ribosomes are tightly correlated with the rate of cell growth and proliferation. All these processes must be matched and appropriately regulated to provide optimal cell functioning. Deregulation of certain factors, including oncogenes, controlling these processes, especially ribosome biosynthesis, can lead to cell transformation. Cancer cells are characterized by intense ribosome biosynthesis which is advantageous for their growth and proliferation. On the other hand, this feature can be engaged as an anticancer strategy. Numerous nucleolar factors such as nucleolar and ribosomal proteins as well as different RNAs, in addition to their role in ribosome biosynthesis, have other functions, including those associated with cancer biology. Some of them can contribute to cell transformation and cancer development. Others, under stress evoked by different factors which often hamper function of nucleoli and thus induce nucleolar/ribosomal stress, can participate in combating cancer cells. In this sense, intentional application of therapeutic agents affecting ribosome biosynthesis can cause either release of these molecules from nucleoli or their de novo biosynthesis to mediate the activation of pathways leading to elimination of harmful cells. This review underlines the role of a nucleolus not only as a ribosome constituting apparatus but also as a hub of both positive and negative control of cancer development. The article is mainly based on original papers concerning mechanisms in which the nucleolus is implicated directly or indirectly in processes associated with neoplasia.

Comprehensive Profiling of lincRNAs in Lung Adenocarcinoma of Never Smokers Reveals Their Roles in Cancer Development and Prognosis

Long intergenic non-coding RNA (lincRNA) is a family of gene transcripts, the functions of which are largely unknown. Although cigarette smoking is the main cause for lung cancer, lung cancer in non-smokers is a separate entity and its underlying cause is little known. Growing evidence suggests lincRNAs play a significant role in cancer development and progression; however, such data is lacking for lung cancer in non-smokers, or those who have never smoked. This study conducted comprehensive profiling of lincRNAs from RNA sequencing (RNA-seq) data of non-smoker patients with lung adenocarcinoma. Both known and novel lincRNAs distinctly segregated tumors from normal tissues. Approximately one third of lincRNAs were differentially expressed between tumors and normal samples and most of them were coordinated with their putative protein gene targets. More importantly, lincRNAs defined two clusters of tumors that were associated with tumor aggressiveness and patient survival. We identified a subset of lincRNAs that were differentially expressed and also associated with patient survival. Very high concordance (R² = 0.9) was observed for the differentially expressed lincRNAs in the Cancer Genome Atlas (TCGA) validation set of 85 transcriptomes and the lincRNAs associated with survival from the discovery set were similarly predictive in the validation set. These lincRNAs warrant further investigation as potential diagnostic and prognostic markers.