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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]

Abstract 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.

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]

Insulin-like growth factor 1/Insulin-like growth factor 1-receptor (IGF-1/IGF-1R) pathway is highly breast cancer subtype context-dependent. Triple-negative breast cancer (TNBC) is an aggressive, highly metastatic cancer showing early recurrence and poor prognosis. High expression of IGF-1 and its receptor IGF-1R, their interaction, autophosphorylation, and activation of intracellular signaling cascades have been significantly associated with TNBC pathophysiology. In the last five to seven years, marvelous work has been done to explore the role of IGF-1/IGF-1R axis in TNBC. In the present review, starting from the general introduction to IGF-1/IGF-1R pathway an up-to-date discussion was focused on its role in TNBC pathophysiology. Further we discussed the up/down stream molecular events of IGF-1/IGF-1R axis, clinical relevance of IGF-1 and IGF-1R levels in TNBC patients, anti-TNBC therapy and possible way-out for IGF-1/IGF-1R axis mediate therapy resistance in TNBC. Combination therapy strategy has been researched to overcome direct IGF-1/IGF-1R pathway inhibition mediated therapy resistance and produced promising results in the management of TNBC. The understanding of up/downstream of the IGF-1/IGF-1R axis provide immense focus on the pathway as a therapeutic target. It is expected within the next decade to determine its potentiality, or lack thereof, for TNBC treatment. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) have important regulatory roles in cancer biology. Although some lncRNAs have well‐characterized functions, the vast majority of this class of molecules remains functionally uncharacterized. To systematically pinpoint functional lncRNAs, a computational approach was proposed for identification of lncRNA‐mediated competing endogenous RNAs (ceRNAs) through combining global and local regulatory direction consistency of expression. Using esophageal squamous cell carcinoma (ESCC) as model, we further identified many known and novel functional lncRNAs acting as ceRNAs (ce‐lncRNAs). We found that most of them significantly regulated the expression of cancer‐related hallmark genes. These ce‐lncRNAs were significantly regulated by enhancers, especially super‐enhancers (SEs). Landscape analyses for lncRNAs further identified SE‐associated functional ce‐lncRNAs in ESCC, such as HOTAIR, XIST, SNHG5, and LINC00094. THZ1, a specific CDK7 inhibitor, can result in global transcriptional downregulation of SE‐associated ce‐lncRNAs. We further demonstrate that a SE‐associated ce‐lncRNA, LINC00094 can be activated by transcription factors TCF3 and KLF5 through binding to SE regions and promoted ESCC cancer cell growth. THZ1 downregulated expression of LINC00094 through inhibiting TCF3 and KLF5. Our data demonstrated the important roles of SE‐associated ce‐lncRNAs in ESCC oncogenesis and might serve as targets for ESCC diagnosis and therapy.

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.

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]

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non‐coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well‐known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan‐cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in ‘Tissue Invasion and Metastasis’ in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA‐based target therapy.

Long noncoding RNAs (lncRNAs) have important regulatory roles in cancer biology. Although some lncRNAs have well‐characterized functions, the vast majority of this class of molecules remains functionally uncharacterized. To systematically pinpoint functional lncRNAs, a computational approach was proposed for identification of lncRNA‐mediated competing endogenous RNAs (ceRNAs) through combining global and local regulatory direction consistency of expression. Using esophageal squamous cell carcinoma (ESCC) as model, we further identified many known and novel functional lncRNAs acting as ceRNAs (ce‐lncRNAs). We found that most of them significantly regulated the expression of cancer‐related hallmark genes. These ce‐lncRNAs were significantly regulated by enhancers, especially super‐enhancers (SEs). Landscape analyses for lncRNAs further identified SE‐associated functional ce‐lncRNAs in ESCC, such as HOTAIR, XIST, SNHG5, and LINC00094. THZ1, a specific CDK7 inhibitor, can result in global transcriptional downregulation of SE‐associated ce‐lncRNAs. We further demonstrate that a SE‐associated ce‐lncRNA, LINC00094 can be activated by transcription factors TCF3 and KLF5 through binding to SE regions and promoted ESCC cancer cell growth. THZ1 downregulated expression of LINC00094 through inhibiting TCF3 and KLF5. Our data demonstrated the important roles of SE‐associated ce‐lncRNAs in ESCC oncogenesis and might serve as targets for ESCC diagnosis and therapy. [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]

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non‐coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well‐known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan‐cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in 'Tissue Invasion and Metastasis' in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA‐based target therapy. Through integrative analysis of 5918 tumors across 11 cancer types, we identified 385 cancer hallmark‐associated driver lncRNAs that exhibited highly similar genomic patterns (i.e. evolutionary conservation and early replication) with known cancer driver genes. [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.

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis -effects and distal trans -effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types. [Display omitted] • Multi-omic clusters reveal shared oncogenic driver pathways across ten cancer types • Genetic changes correlate with altered, tumor-specific protein-protein interactions • cis / trans -effects and kinase activities show driver heterogeneity and druggability • Proteomic integration with genomic drivers resolves distinct cancer hallmark patterns A multi-omics analysis-based resource across ten cancer types from more than 1,000 patients provides pan-cancer insights into shared oncogenic driver mechanisms and pathways. [ABSTRACT FROM AUTHOR]

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. ‘Network operational signatures’ which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors’ evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer. [ABSTRACT FROM AUTHOR]

Cancer research over the past decades has revealed a number of molecular, biochemical, and cellular events that reflect progressive transformation of normal human cells into their malignant derivatives. These findings help to better understand the complexity of human tumorigenesis. In our study, molecular information is organized to chart a comprehensive map of the signaling network for human cancer. It includes transcriptional and translational regulation and diverse feedback-control loops. It is demonstrated that applying this signaling network map allows predicting the effect of targeted therapy before it can be applied into practice to reduce clinical trial risks. Hence, the proposed map with prognosticating potential effect might become part of drug discovery programs for targeted therapy. Applied in individual patient care it helps to reduce the current reliance of cancer treatment on chemotherapies with low therapeutic indices. This study also demonstrates that continuing elucidation of tumorigenesis will not only need heterotypic organ culture systems in vitro and increasingly refined animal models in vivo , but also computationally calculable virtual cell models in silico . [ABSTRACT FROM AUTHOR]

Abstract: Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones. [Copyright &y& Elsevier]

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]

Normal cells must overcome multiple protective mechanisms to develop into cancer cells. Their new capabilities include self-sufficiency in growth signals and insensitivity to antigrowth signals, evasion of apoptosis, a limitless replicative potential, sustained angiogenesis, and tissue invasion and metastasis; these are also termed the six hallmarks of cancer. A deep understanding of the genetic and protein alterations involved in these processes has enabled the development of targeted therapeutic strategies and clinical trial design in the search for ovarian cancer treatments. Clinically, significantly longer progression-free survival has been observed in the single use of PARP, MEK, VEGF and Chk1/Chk2 inhibitors. However, the clinical efficacy of the targeted agents is still restricted to specific molecular subtypes and no trials illustrate a benefit in overall survival. Exploring novel drug targets or combining current feasible biological agents hold great promise to further improve outcomes in ovarian cancer. In this review, we intend to provide a comprehensive description of the molecular alterations involved in ovarian cancer carcinogenesis and of emerging biological agents and combined strategies that target aberrant pathways, which might shed light on future ovarian cancer treatment., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Long noncoding RNAs (lncRNAs) have been implicated in cancer biogenesis and prognosis. However, we still lack knowledge on their function during glioma progression. In this study, we analyzed the lncRNA expression profile across 907 glioma patients in grades II, III, and IV. Widespread dynamic expression of lncRNAs during glioma progression was revealed, and we identified 33 onco-lncRNAs and 61 tumor suppressor lncRNAs. We found that the expression of these oncogenic lncRNAs is regulated by grade-specific expressed transcription factors. Based on the "guilt by association" rule, we predicted the potential functions of oncogenic lncRNAs, and the majority of these lncRNAs are involved in cancer hallmarks. Especially we found that CARD8-AS1 regulates the metastatic potential of glioma cell lines in vitro. Integrating clinical information, we identified the 12 protective and 8 risk lncRNAs (such as PWAR6 and CARD8-AS1) in glioma. Finally, an lncRNA-gene functional module was identified to be associated with the survival of patients. The predictive ability of this module signature was further validated in an independent dataset. Our results revealed the dynamic transcriptome transition during glioma progression, indicating that the lncRNA signature could be a useful biomarker that may improve upon our understanding of the molecular mechanisms underlying glioma progression., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)