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Background: Clinical biomarkers, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application. Objective: Accordingly, in this study, we emphasize the potential of in silico approaches and have proposed and applied 3 novel sequential in silico pre-clinical validation steps to better identify the biomarkers that are truly desirable for clinical investment. Design: As protein biomarkers are becoming increasingly important in the clinic alongside other molecular biomarkers and lung cancer is the most common cause of cancer-related deaths, we used protein biomarkers for lung cancer as an illustrative example to apply our in silico pre-clinical validation approach. Methods: We collected the reported protein biomarkers for 3 cases (lung adenocarcinoma-LUAD, squamous cell carcinoma-LUSC, and unspecified lung cancer) and evaluated whether the protein biomarkers have cancer altering properties (i.e., act as tumor suppressors or oncoproteins and represent cancer hallmarks), are expressed in body fluids, and can be targeted by FDA-approved drugs. Results: We collected 3008 protein biomarkers for lung cancer, 1189 for LUAD, and 182 for LUSC. Of these protein biomarkers for lung cancer, LUAD, and LUSC, only 28, 25, and 6 protein biomarkers passed the 3 in silico pre-clinical validation steps examined, and of these, only 5 and 2 biomarkers were specific for lung cancer and LUAD, respectively. Conclusion: In this study, we applied our in silico pre-clinical validation approach the protein biomarkers for lung cancer cases. However, this approach can be applied and adapted to all cancer biomarkers. We believe that this approach will greatly facilitate the transition of cancer biomarkers into the clinical phase and offers great potential for future biomarker research. Plain Language Summary: Biomarkers, which are routinely used in clinics, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application. This research article highlights the challenges of translating cancer biomarkers into clinical practice and summarizes feasible step toward "in silico pre-clinical validation" using the example of lung cancer types. Accordingly, protein biomarkers proposed for lung cancer are being investigated using the "in silico pre-clinical validation" approach to determine whether they have cancer altering properties (i.e., oncoprotein, tumor suppressor, and cancer hallmark), are expressed in body fluids (i.e., plasma/serum, saliva, urine, and bronchoalveolar lavage) and can be targeted with FDA-approved drugs. We believe that the step of in silico pre-clinical validation is the future of biomarker research for all professionals involved in clinical, biological, epidemiological, biostatistical and health research, and that it will greatly facilitate the transition of biomarkers to the clinical phase. [ABSTRACT FROM AUTHOR]

Neuroblastoma is the most common malignant extracranial solid tumor of childhood. Recent studies involving the application of advanced high-throughput "omics" techniques have revealed numerous genomic alterations, including aberrant coding-gene transcript levels and dysfunctional pathways, that drive the onset, growth, progression, and treatment resistance of neuroblastoma. Research conducted in the past decade has shown that long non-coding RNAs, once thought to be transcriptomic noise, play key roles in cancer development. With the recent and continuing increase in the amount of evidence for the underlying roles of long non-coding RNAs in neuroblastoma, the potential clinical implications of these RNAs cannot be ignored. In this review, we discuss their biological mechanisms of action in the context of the central driving mechanisms of neuroblastoma, focusing on potential contributions to the diagnosis, prognosis, and treatment of this disease. We also aim to provide a clear, integrated picture of future research opportunities. [ABSTRACT FROM AUTHOR]

Background: Numerous studies have shown that the insulin‐like growth factor (IGF) pathway is highly associated with tumor initial and progression in several tumors. However, compared with IGF1/1R and IGF2/2R, insufficient studies have focused on IGF‐binding proteins (IGFBPs). Methods: The GDC TCGA and GTEx data of 33 cancers, TCGA pan‐cancer immune phenotypes, tumor mutation burdens, and the copy number alterations of IGFBPs were extracted. Next, the prognostic value of IGFBPs was analyzed based on a univariate Cox analysis. Additionally, the ESTIMATE algorithm was used to calculate stromal and immune scores and tumor purity, and the CIBERSORT algorithm was used to estimate tumor‐infiltrating immunocyte levels. Ultimately, the correlation between IGFBP expression and cancer hallmark pathways was estimated with a Spearman analysis. Results: The expression of IGFBPs was differentially expressed and correlated with prognosis in specific cancers. IGFBPs may operate as biological markers for carcinogenesis and progression and as prognostic biomarkers. Additionally, IGFBP5 has been proved that promotes the invasion and migration of ovarian cancer. Conclusions: In general, IGFBPs can serve as predictable biomarkers and potential therapeutic targets for specific tumors. Our results could provide underlying targets for the design of laboratory experiments to elucidate the mechanism of IGFBPs in cancers and identify IGFBP5 as a prognostic factor in ovarian cancers. [ABSTRACT FROM AUTHOR]

The role of long non-coding RNA (lncRNA) in human tumors has gradually received increasing attention in recent years. Particularly, the different functions of lncRNAs in different subcellular localizations have been widely investigated. The upregulation of lncRNA small nucleolar RNA host gene 17 (SNHG17) has been observed in various human tumors. Growing evidence has proved that SNHG17 plays a tumor-promoting role in tumorigenesis and development. This paper describes the molecular mechanisms by which SNHG17 contributes to tumor formation and development. The different functions of SNHG17 in various subcellular localizations are also emphasized: its function in the cytoplasm as a competing endogenous RNA (ceRNA), its action in the nucleus as a transcriptional coactivator, and its function through the polycomb repressive complex 2 (PRC2)-dependent epigenetic modifications that regulate transcriptional processes. Finally, the correlation between SNHG17 and human tumors is summarized. Its potential as a novel prognostic and diagnostic biomarker for cancer is explored especially. [ABSTRACT FROM AUTHOR]

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8 , and INFA5 , and the other including CDK4, AVIL , and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8 , and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer. [ABSTRACT FROM AUTHOR]

Oral squamous cell carcinoma (OSCC) is the most common malignant tumour in the oral and maxillofacial region. Numerous cancers share ten common traits ("hallmarks") that govern the transformation of normal cells into cancer cells. Long non‐coding RNAs (lncRNAs) are important factors that contribute to tumorigenesis. However, very little is known about the cooperative relationships between lncRNAs and cancer hallmark‐associated genes in OSCC. Through integrative analysis of cancer hallmarks, somatic mutations, copy number variants (CNVs) and expression, some OSCC‐specific cancer hallmark‐associated genes and lncRNAs are identified. A computational framework to identify gene and lncRNA cooperative regulation pairs (GLCRPs) associated with different cancer hallmarks is developed based on the co‐expression and co‐occurrence of mutations. The distinct and common features of ten cancer hallmarks based on GLCRPs are characterized in OSCC. Cancer hallmark insensitivity to antigrowth signals and self‐sufficiency in growth signals are shared by most GLCRPs in OSCC. Some key GLCRPs participate in many cancer hallmarks in OSCC. Cancer hallmark‐associated GLCRP networks have complex patterns and specific functions in OSCC. Specially, some key GLCRPs are associated with the prognosis of OSCC patients. In summary, we generate a comprehensive landscape of cancer hallmark‐associated GLCRPs that can act as a starting point for future functional explorations, the identification of biomarkers and lncRNA‐based targeted therapy in OSCC. [ABSTRACT FROM AUTHOR]

The hallmarks of cancer represent an essential concept for discovering novel knowledge about cancer and for extracting the complexity of cancer. Due to the lack of topic analysis frameworks optimized specifically for cancer data, the studies on topic modeling in cancer research still have a strong challenge. Recently, deep learning (DL) based approaches were successfully employed to learn semantic and contextual information from scientific documents using word embeddings according to the hallmarks of cancer (HoC). However, those are only applicable to labeled data. There is a comparatively small number of documents that are labeled by experts. In the real world, there is a massive number of unlabeled documents that are available online. In this paper, we present a multi-task topic analysis (MTTA) framework to analyze cancer hallmark-specific topics from documents. The MTTA framework consists of three main subtasks: (1) cancer hallmark learning (CHL)—used to learn cancer hallmarks on existing labeled documents; (2) weak label propagation (WLP)—used to classify a large number of unlabeled documents with the pre-trained model in the CHL task; and (3) topic modeling (ToM)—used to discover topics for each hallmark category. In the CHL task, we employed a convolutional neural network (CNN) with pre-trained word embedding that represents semantic meanings obtained from an unlabeled large corpus. In the ToM task, we employed a latent topic model such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) model to catch the semantic information learned by the CNN model for topic analysis. To evaluate the MTTA framework, we collected a large number of documents related to lung cancer in a case study. We also conducted a comprehensive performance evaluation for the MTTA framework, comparing it with several approaches. [ABSTRACT FROM AUTHOR]

Exposure to toxic substances in the environment is one of the most important causes of cancer. However, the time-consuming process for the identification and characterization of carcinogens is not applicable to a huge amount of testing chemicals. The data gaps make the carcinogenic risk uncontrollable. An efficient and effective way of prioritizing chemicals of carcinogenic concern with interpretable mechanism information is highly desirable. This study presents a curation work for genes and pathways associated with 11 hallmarks of cancer (HOCs) reported by the Halifax Project. To demonstrate the usefulness of the curated HOC data, the interacting HOC genes and affected HOC pathways of chemicals of the three carcinogen lists from IARC, NTP and EPA were analyzed using the in silico toxicogenomics ChemDIS system. Results showed that a higher number of affected HOCs were observed for known carcinogens than the other chemicals. The curated HOC data is expected to be useful for prioritizing chemicals of carcinogenic concern. Database URL: The HOC database is available at https://github.com/hocdb-KMU-TMU/hocdb and the website of Database journal as Supplementary Data. [ABSTRACT FROM AUTHOR]

By establishing multi-omics pipelines, we uncover overexpression and gene copy-number alterations of nucleoporin-93 (NUP93), a nuclear pore component, in aggressive human mammary tumors. NUP93 overexpression enhances transendothelial migration and matrix invasion in vitro , along with tumor growth and metastasis in animal models. These findings are supported by analyses of two sets of naturally occurring mutations: rare oncogenic mutations and inactivating familial nephrotic syndrome mutations. Mechanistically, NUP93 binds with importins, boosts nuclear transport of importins' cargoes, such as β-catenin, and activates MYC. Likewise, NUP93 overexpression enhances the ultimate nuclear transport step shared by additional signaling pathways, including TGF-β/SMAD and EGF/ERK. The emerging addiction to nuclear transport exposes vulnerabilities of NUP93-overexpressing tumors. Congruently, myristoylated peptides corresponding to the nuclear translocation signals of SMAD and ERK can inhibit tumor growth and metastasis. Our study sheds light on an emerging hallmark of advanced tumors, which derive benefit from robust nucleocytoplasmic transport. [Display omitted] • Multi-omics pipelines associate NUP93 overexpression with aggressive breast cancers • NUP93 enhances nuclear transport of specific metastasis mediators (e.g., β-catenin) • Peptides inhibiting translocation of these mediators inhibit metastasis in animals • Unblocked nucleocytoplasmic transport emerges as a hallmark of advanced cancers Nataraj et al. develop multi-omics pipelines, which uncover critical roles of nucleoporin-93 (NUP93), a component of the nuclear pore complex, in metastasis of breast cancer. NUP93 overexpression in aggressive tumors unblocks nucleocytoplasmic transport of several transcription mediators, including β-catenin. Accordingly, specific inhibitors of NUP93-assisted transport can reduce metastasis. [ABSTRACT FROM AUTHOR]

Air pollution presents a major environmental problem, inducing harmful effects on human health. Particulate matter of 10 μm or less in diameter (PM10) is considered an important risk factor in lung carcinogenesis. Epithelial–mesenchymal transition (EMT) is a regulatory program capable of inducing invasion and metastasis in cancer. In this study, we demonstrated that PM10 treatment induced phosphorylation of SMAD2/3 and upregulation of SMAD4. We also reported that PM10 increased the expression and protein levels of TGFB1 (TGF-β), as well as EMT markers SNAI1 (Snail), SNAI2 (Slug), ZEB1 (ZEB1), CDH2 (N-cadherin), ACTA2 (α-SMA), and VIM (vimentin) in the lung A549 cell line. Cell exposed to PM10 also showed a decrease in the expression of CDH1 (E-cadherin). We also demonstrated that expression levels of these EMT markers were reduced when cells are transfected with small interfering RNAs (siRNAs) against TGFB1. Interestingly, phosphorylation of SMAD2/3 and upregulation of SMAD induced by PM10 were not affected by transfection of TGFB1 siRNAs. Finally, cells treated with PM10 exhibited an increase in the capacity of invasiveness because of EMT induction. Our results provide new evidence regarding the effect of PM10 in EMT and the acquisition of an invasive phenotype, a hallmark necessary for lung cancer progression. [ABSTRACT FROM AUTHOR]