Modeling local coronavirus outbreaks.

This article presents an overview of methods developed for the modeling and control of local coronavirus outbreaks. The article reviews early transmission dynamics featuring exponential growth in infections, and links this to a renewal epidemic model where the current incidence of infection depends upon the expected value of incidence randomly lagged into the past. This leads directly to simple formulas for the fraction of the population infected in an unmitigated outbreak, and reveals herd immunity as the solution to an optimization problem. The model also leads to direct and easy-to-understand formulas for aligning observable epidemic indicators such as cases, hospitalizations and deaths with the unobservable incidence of infection, and as a byproduct leads to a simple first-order approach for estimating the effective reproduction number R t . The model also leads naturally to direct assessments of the effectiveness of isolation in preventing the spread of infection. This is illustrated with application to repeat asymptomatic screening programs of the sort utilized by universities, sports teams and businesses to prevent the spread of infection.

Repeat SARS-CoV-2 testing models for residential college populations.

Residential colleges are considering re-opening under uncertain futures regarding the COVID-19 pandemic. We consider repeat SARS-CoV-2 testing models for the purpose of containing outbreaks in the residential campus community. The goal of repeat testing is to detect and isolate new infections rapidly to block transmission that would otherwise occur both on and off campus. The models allow for specification of aspects including scheduled on-campus resident screening at a given frequency, test sensitivity that can depend on the time since infection, imported infections from off campus throughout the school term, and a lag from testing until student isolation due to laboratory turnaround and student relocation delay. For early- (late-) transmission of SARS-CoV-2 by age of infection, we find that weekly screening cannot reliably contain outbreaks with reproductive numbers above 1.4 (1.6) if more than one imported exposure per 10,000 students occurs daily. Screening every three days can contain outbreaks providing the reproductive number remains below 1.75 (2.3) if transmission happens earlier (later) with time from infection, but at the cost of increased false positive rates requiring more isolation quarters for students testing positive. Testing frequently while minimizing the delay from testing until isolation for those found positive are the most controllable levers for preventing large residential college outbreaks. A web app that implements model calculations is available to facilitate exploration and consideration of a variety of scenarios.

Prognostic signature associated with radioresistance in head and neck cancer via transcriptomic and bioinformatic analyses.

BACKGROUND: Radiotherapy is an indispensable treatment modality in head and neck cancer (HNC), while radioresistance is the major cause of treatment failure. The aim of this study is to identify a prognostic molecular signature associated with radio-resistance in HNC for further clinical applications. METHODS: Affymetrix cDNA microarrays were used to globally survey different transcriptomes between HNC cell lines and isogenic radioresistant sublines. The KEGG and Partek bioinformatic analytical methods were used to assess functional pathways associated with radioresistance. The SurvExpress web tool was applied to study the clinical association between gene expression profiles and patient survival using The Cancer Genome Atlas (TCGA)-head and neck squamous cell carcinoma (HNSCC) dataset (n = 283). The Kaplan-Meier survival analyses were further validated after retrieving clinical data from the TCGA-HNSCC dataset (n = 502) via the Genomic Data Commons (GDC)-Data-Portal of National Cancer Institute. A panel maker molecule was generated to assess the efficacy of prognostic prediction for radiotherapy in HNC patients. RESULTS: In total, the expression of 255 molecules was found to be significantly altered in the radioresistant cell sublines, with 155 molecules up-regulated 100 down-regulated. Four core functional pathways were identified to enrich the up-regulated genes and were significantly associated with a worse prognosis in HNC patients, as the modulation of cellular focal adhesion, the PI3K-Akt signaling pathway, the HIF-1 signaling pathway, and the regulation of stem cell pluripotency. Total of 16 up-regulated genes in the 4 core pathways were defined, and 11 over-expressed molecules showed correlated with poor survival (TCGA-HNSCC dataset, n = 283). Among these, 4 molecules were independently validated as key molecules associated with poor survival in HNC patients receiving radiotherapy (TCGA-HNSCC dataset, n = 502), as IGF1R (p = 0.0454, HR = 1.43), LAMC2 (p = 0.0235, HR = 1.50), ITGB1 (p = 0.0336, HR = 1.46), and IL-6 (p = 0.0033, HR = 1.68). Furthermore, the combined use of these 4 markers product an excellent result to predict worse radiotherapeutic outcome in HNC (p < 0.0001, HR = 2.44). CONCLUSIONS: Four core functional pathways and 4 key molecular markers significantly contributed to radioresistance in HNC. These molecular signatures may be used as a predictive biomarker panel, which can be further applied in personalized radiotherapy or as radio-sensitizing targets to treat refractory HNC.

Proteomics Analysis Reveals Involvement of Krt17 in Areca Nut-Induced Oral Carcinogenesis.

The areca nut is a known carcinogen that causes oral cancer in individuals in Southeast Asia, but the molecular mechanism that leads to this malignancy is still unclear. To mimic the habit of areca nut chewing, our laboratory has established four oral cancer cell sublines (SAS, OECM1, K2, C9), which have been chronically exposed to areca nut extract (ANE). To elucidate the molecular basis of areca nut-induced oral carcinogenesis, the differential proteomes between oral cancer cells and the ANE-treated sublines were determined using isobaric mass tag (iTRAQ) labeling and multidimensional liquid chromatography-mass spectrometry (LC-MS/MS). Over 1000 proteins were identified in four sublines, and 196 proteins were found to be differentially expressed in at least two ANE-treated sublines. A bioinformatic analysis revealed that these proteins participate in several pathways, and one of the most prominent pathways was the regulation of epithelial to mesenchymal transition (EMT). In all, 24 proteins including Krt17 were confirmed to be differentially expressed in the ANE-treated sublines. To reveal additional information on the mechanism of ANE-induced carcinogenesis, Krt17 was further investigated. Krt17 knockdown significantly suppressed ANE-induced cell migration and invasion and modulated the EMT process. Furthermore, in a murine model of carcinogen-induced (arecoline cocktail, an active compound of ANE) oral cancer, Krt17 was significantly up-regulated in all hyperplastic tissues and in carcinoma tissues (p < 0.001). In conclusion, we have identified a proteome of oral cancer cells that is associated with chronic areca nut exposure. Krt17 was demonstrated to contribute to areca nut-induced oral malignancy. The results of this study contribute to risk assessment, disease prevention and other clinical applications associated with areca nut-induced oral cancer.

Areca nut contributes to oral malignancy through facilitating the conversion of cancer stem cells.

Oral cancer is one of the most frequent malignant diseases worldwide, and areca nut is a primary carcinogen causing this cancer in Southeast Asia. Previous studies to examine the effects of this carcinogen often used short-term and high-dose treatment of area nut extract as a research model, which do not recapitulate the conditions of patients with long-term and habitual use of this substance. To approach authentic mechanism of areca nut-induced oral carcinogenesis that occurs in human, we established four isogenic sublines of oral cells which were chronic exposed to areca nut extract. Without eliciting cytotoxicity or senescence, these four sublines cells exhibited significant increase in invasive ability, along with epithelial-mesenchymal transition. These cells also showed resistance to chemotherapeutic drug and irradiation, accompanying with the augmentation of ABCG2 protein efflux and increased ROS clearance. Moreover, these sublines possessed the characteristics of cancer stemness, as demonstrated by enriched CD24-/CD44+ and CD133+ sub-populations, enhanced spheroid cell formation, and induced expressions of pluripotent stemness regulators, including Gp96, Grp78, Slug, Sox9, Snail, and Foxc2. These stemness regulators were further shown up-regulations in oral cancer patients with areca nut-chewing habit, and were statistically correlated with CD44 expression, a stemness marker. In conclusion, our findings suggested that areca nut contributes to oral malignancy through facilitating the conversion of cancer stem cells. This study may further contribute to clinical applications in disease prevention, risk assessment or molecular therapeutics on areca nut- associated diseases.

OncomiR-196 promotes an invasive phenotype in oral cancer through the NME4-JNK-TIMP1-MMP signaling pathway.

BACKGROUND: MicroRNA-196 (miR-196), which is highly up-regulated in oral cancer cells, has been reported to be aberrantly expressed in several cancers; however, the significance of miR-196 in oral cancer has not yet been addressed. METHODS: Cellular functions in response to miR-196 modulation were examined, including cell growth, migration, invasion and radio/chemosensitivity. Algorithm-based studies were used to identify the regulatory target of miR-196. The miR-196 target gene and downstream molecular mechanisms were confirmed by RT-qPCR, western blot, luciferase reporter and confocal microscopy analyses. miR-196 expression was determined in paired cancer and adjacent normal tissues from oral cancer patients. RESULTS: Both miR-196a and miR-196b were highly over-expressed in the cancer tissue and correlated with lymph node metastasis (P = 0.001 and P = 0.006, respectively). Functionally, miR-196 actively promoted cell migration and invasion without affecting cell growth. Mechanistically, miR-196 performed it's their function by inhibiting NME4 expression and further activating p-JNK, suppressing TIMP1, and augmenting MMP1/9. CONCLUSION: miR-196 contributes to oral cancer by promoting cell migration and invasion. Clinically, miR-196a/b was significantly over-expressed in the cancer tissues and correlated with lymph node metastasis. Thus, our findings provide new knowledge of the underlying mechanism of cancer metastasis. miR-196 may serve as a promising marker for better oral cancer management.

CD4+ T cells disarm or delete cytotoxic T lymphocytes under IL-17-polarizing conditions.

Previous studies have shown that TGF-ß acts cooperatively with IL-6 to elicit a high frequency of IL-17-secreting CD4(+) T cells (termed Th17) and an elevated CD8(+)IL-17(+) T cell population (termed Tc17). These CD8(+) cells fail to behave like most cytotoxic T lymphocytes that express IFN-γ and granzyme B, but they exhibit a noncytotoxic phenotype. Although a significant increase in the number of these Tc17 cells was found in tumors, their role and interaction with other cell types remain unclear. In this study, we demonstrate that the presence of CD4(+)CD25(-) T cells, but not the CD4(+)CD25(+) (regulatory T [Treg]) cell population, significantly reduced the elicitation of Tc17 cells, possibly as a result of the induction of apoptotic signals. Importantly, these signals may be derived from soluble mediators, and the addition of anti-IL-2 restored the reduction of Tc17 cells in the presence of CD4(+)CD25(-) T cells. Finally, the elicited Tc17 and Treg cells exhibited a close association in patients with head and neck cancer, indicating that the surrounding Treg cells might maintain the survival of the Tc17 cells. Taken together, these results reveal an intriguing mechanism in which Tc17 cells are controlled by a finely tuned collaboration between the different types of CD4(+) T cells in distinct tumor microenvironments.

Schooling and variation in the COMT gene: the devil is in the details.

BACKGROUND: Schooling is considered one of the major contributors to the development of intelligence within societies and individuals. Genetic variation might modulate the impact of schooling and explain, at least partially, the presence of individual differences in classrooms. METHOD: We studied a sample of 1,502 children (mean age = 11.7 years) from Zambia. Approximately 57% of these children were enrolled in school, and the rest were not. To quantify genetic variation, we investigated a number of common polymorphisms in the catechol-O-methyltransferase (COMT) gene that controls the production of the protein thought to account for >60% of the dopamine degradation in the prefrontal cortex. RESULTS: Haplotype analyses generated results ranging from the presence to absence of significant interactions between a number of COMT haplotypes and indicators of schooling (i.e., in- vs. out-of-school and grade completed) in the prediction of nonverbal intelligence, depending on the parameter specification. However, an investigation of the distribution of corresponding p-values suggested that these positive results were false. CONCLUSIONS: Convincing evidence that the variation in the COMT gene is associated with individual differences in nonverbal intelligence either directly or through interactions with schooling was not found. p-values produced by the method of testing for haplotype effects employed here may be sensitive to parameter settings, invalid under default settings, and should be checked for validity through simulation.

Molecular Signature of Long Non-Coding RNA Associated with Areca Nut-Induced Head and Neck Cancer.

The areca nut is a high-risk carcinogen for head and neck cancer (HNC) patients in Southeast Asia. The underlying molecular mechanism of areca nut-induced HNC remains unclear, especially regarding the role of long non-coding RNA (lncRNA). This study employed a systemic strategy to identify lncRNA signatures related to areca nut-induced HNC. In total, 84 cancer-related lncRNAs were identified. Using a PCR array method, 28 lncRNAs were identified as being dysregulated in HNC cells treated with areca nut (17 upregulated and 11 downregulated). Using bioinformatics analysis of The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) dataset, 45 lncRNAs were differentially expressed in tumor tissues from HNC patients (39 over- and 6 under-expressions). The integrated evaluation showed 10 lncRNAs dysregulated by the areca nut and altered expression in patients, suggesting that these panel molecules participate in areca nut-induced HNC. Five oncogenic (LUCAT1, MIR31HG, UCA1, HIF1A-AS2, and SUMO1P3) and tumor-suppressive (LINC00312) lncRNAs were independently validated, and three key molecules were further examined. Pathway prediction revealed that LUCAT1, UCA1, and MIR31HG modulate multiple oncogenic mechanisms, including stress response and cellular motility. Clinical assessment showed that these lncRNAs exhibited biomarker potentials in diagnosis (area under the curve = 0.815 for LUCAT1) and a worse prognosis (both p < 0.05, survival analysis). Cellular studies further demonstrated that MIR31HG facilitates areca nut-induced cancer progression, as silencing this molecule attenuated arecoline-induced invasion ability in HNC cells. This study identified lncRNA signatures that play a role in areca nut-induced HNC. These molecules may be further applied in risk assessment, diagnosis, prognosis, and therapeutics for areca nut-associated malignancies.

Spectral top-down recovery of latent tree models.

Modeling the distribution of high-dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying tree structure, given only observations of its terminal nodes. Many algorithms for tree recovery are computationally intensive, which limits their applicability to trees of moderate size. For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps. First, separately recover the structure of multiple, possibly random subsets of the terminal nodes. Second, merge the resulting subtrees to form a full tree. Here, we develop spectral top-down recovery (STDR), a deterministic divide-and-conquer approach to infer large latent tree models. Unlike previous methods, STDR partitions the terminal nodes in a non random way, based on the Fiedler vector of a suitable Laplacian matrix related to the observed nodes. We prove that under certain conditions, this partitioning is consistent with the tree structure. This, in turn, leads to a significantly simpler merging procedure of the small subtrees. We prove that STDR is statistically consistent and bound the number of samples required to accurately recover the tree with high probability. Using simulated data from several common tree models in phylogenetics, we demonstrate that STDR has a significant advantage in terms of runtime, with improved or similar accuracy.

Development and validation of a machine learning model of radiation-induced hypothyroidism with clinical and dose-volume features.

BACKGROUND AND PURPOSE: Radiation-induced hypothyroidism (RIHT) is a common but underestimated late effect in head and neck cancers. However, no consensus exists regarding risk prediction or dose constraints in RIHT. We aimed to develop a machine learning model for the accurate risk prediction of RIHT based on clinical and dose-volume features and to evaluate its performance internally and externally. MATERIALS AND METHODS: We retrospectively searched two institutions for patients aged >20 years treated with definitive radiotherapy for nasopharyngeal or oropharyngeal cancer, and extracted their clinical information and dose-volume features. One was designated the developmental cohort, the other as the external validation cohort. We compared the performances of machine learning models with those of published normal tissue complication probability (NTCP) models. RESULTS: The developmental and external validation cohorts consisted of 378 and 49 patients, respectively. The estimated cumulative incidence rates of grade ≥1 hypothyroidism were 53.5% and 61.3% in the developmental and external validation cohorts, respectively. Machine learning models outperformed traditional NTCP models by having lower Brier scores at every time point and a lower integrated Brier score, while demonstrating a comparable calibration index and mean area under the curve. Even simplified machine learning models using only thyroid features performed better than did traditional NTCP algorithms. The machine learning models showed consistent performance between folds. The performance in a previously unseen external validation cohort was comparable to that of the cross-validation. CONCLUSIONS: Our model outperformed traditional NTCP models, with additional capabilities of predicting the RIHT risk at individual time points. A simplified model using only thyroid dose-volume features still outperforms traditional NTCP models and can be incorporated into future treatment planning systems for biological optimization.

MiR-630 Promotes Radioresistance by Induction of Anti-Apoptotic Effect via Nrf2-GPX2 Molecular Axis in Head-Neck Cancer.

Head and neck cancer (HNC) ranks among the top ten prevalent cancers worldwide. Radiotherapy stands as a pivotal treatment component for HNC; however, radioresistance in cancerous cells often leads to local recurrence, becoming a substantial factor in treatment failure. MicroRNAs (miRNAs) are compact, non-coding RNAs that regulate gene expression by targeting mRNAs to inhibit protein translation. Although several studies have indicated that the dysregulation of miRNAs is intricately linked with malignant transformation, understanding this molecular family's role in radioresistance remains limited. This study determined the role of miR-630 in regulating radiosensitivity in HNC. We discovered that miR-630 functions as an oncomiR, marked by its overexpression in HNC patients, correlating with a poorer prognosis. We further delineated the malignant function of miR-630 in HNC cells. While it had a minimal impact on cell growth, the miR-630 contributed to radioresistance in HNC cells. This result was supported by decreased cellular apoptosis and caspase enzyme activities. Moreover, miR-630 overexpression mitigated irradiation-induced DNA damage, evidenced by the reduced levels of the γ-H2AX histone protein, a marker for double-strand DNA breaks. Mechanistically, the overexpression of miR-630 decreased the cellular ROS levels and initiated Nrf2 transcriptional activity, resulting in the upregulation of the antioxidant enzyme GPX2. Thus, this study elucidates that miR-630 augments radioresistance by inducing an anti-apoptotic effect via the Nrf2-GPX2 molecular axis in HNC. The modulation of miR-630 may serve as a novel radiosensitizing target for HNC.

Multifaceted and Intricate Oncogenic Mechanisms of NDRG1 in Head and Neck Cancer Depend on Its C-Terminal 3R-Motif.

N-Myc downstream-regulated 1 (NDRG1) has inconsistent oncogenic functions in various cancers. We surveyed and characterized the role of NDRG1 in head and neck cancer (HNC). Cellular methods included spheroid cell formation, clonogenic survival, cell viability, and Matrigel invasion assays. Molecular techniques included transcriptomic profiling, RT-qPCR, immunoblotting, in vitro phosphorylation, immunofluorescent staining, and confocal microscopy. Prognostic significance was assessed by Kaplan-Meier analysis. NDRG1 participated in diverse oncogenic functions in HNC cells, mainly stress response and cell motility. Notably, NDRG1 contributed to spheroid cell growth, radio-chemoresistance, and upregulation of stemness-related markers (CD44 and Twist1). NDRG1 facilitated cell migration and invasion, and was associated with modulation of the extracellular matrix molecules (fibronectin, vimentin). Characterizing the 3R-motif in NDRG1 revealed its mechanism in the differential regulation of the phenotypes. The 3R-motif displayed minimal effect on cancer stemness but was crucial for cell motility. Phosphorylating the motif by GSK3b at serine residues led to its nuclear translocation to promote motility. Clinical analyses supported the oncogenic function of NDRG1, which was overexpressed in HNC and associated with poor prognosis. The data elucidate the multifaceted and intricate mechanisms of NDRG1 in HNC. NDRG1 may be a prognostic indicator or therapeutic target for refractory HNC.

MYH9 Facilitates Cell Invasion and Radioresistance in Head and Neck Cancer via Modulation of Cellular ROS Levels by Activating the MAPK-Nrf2-GCLC Pathway.

The MYH9 (Myosin heavy chain 9), an architecture component of the actomyosin cytoskeleton, has been reported to be dysregulated in several types of cancers. However, how this molecule contributes to cancer development is still obscure. This study deciphered the molecular function of MYH9 in head and neck cancer (HNC). Cellular methods included clonogenic survival, wound-healing migration, and Matrigel invasion assays. Molecular techniques included RT-qPCR, western blot, luciferase reporter assays, and flow cytometry. Clinical association studies were undertaken by TCGA data mining, Spearman correlation, and Kaplan-Meier survival analysis. We found that MYH9 was overexpressed in tumors and associated with poor prognosis in HNC patients. MYH9 promoted cell motility along with the modulation of the extracellular matrix (fibronectin, ITGA6, fascin, vimentin, MMPs). Also, MYH9 contributed to radioresistance and was related to the expression of anti-apoptotic and DNA repairing molecules (XIAP, MCL1, BCL2L1, ATM, RAD50, and NBN). Mechanically, MYH9 suppressed cellular ROS levels, which were achieved by activating the pan-MAPK signaling molecules (Erk, p38, and JNK), the induction of Nrf2 transcriptional activity, and the up-regulation of antioxidant enzymes (GCLC, GCLM, GPX2). The antioxidant enzyme GCLC was further demonstrated to facilitate cell invasion and radioresistance in HNC cells. Thus, MYH9 exerts malignant functions in HNC by regulating cellular ROS levels via activating the MAPK-Nrf2-GCLC signaling pathway. As MYH9 contributes to radioresistance and metastasis, this molecule may serve as a prognostic biomarker for clinical application. Furthermore, an in vivo study is emergent to support the therapeutic potential of targeting MYH9 to better manage refractory cancers.

Reconsidering the asymptotic null distribution of likelihood ratio tests for genetic linkage in multivariate variance components models under complete pleiotropy.

Accurate knowledge of the null distribution of hypothesis tests is important for valid application of the tests. In previous papers and software, the asymptotic null distribution of likelihood ratio tests for detecting genetic linkage in multivariate variance components models has been stated to be a mixture of chi-square distributions with binomial mixing probabilities. For variance components models under the complete pleiotropy assumption, we show by simulation and by theoretical arguments based on the geometry of the parameter space that all aspects of the previously stated asymptotic null distribution are incorrect-both the binomial mixing probabilities and the chi-square components. Correcting the null distribution gives more conservative critical values than previously stated, yielding P values that can easily be 10 times larger. The true mixing probabilities give the highest probability to the case where all variance parameters are estimated positive, and the mixing components show severe departures from chi-square distributions. Thus, the asymptotic null distribution has complex features that raise challenges for the assessment of significance of multivariate linkage findings. We propose a method to generate an asymptotic null distribution that is much faster than other empirical methods such as permutation, enabling us to obtain P values with higher precision more efficiently.

Analysis of an ankyrin-like region in Epstein Barr Virus encoded (EBV) BZLF-1 (ZEBRA) protein: implications for interactions with NF-κB and p53.

BACKGROUND: The carboxyl terminal of Epstein-Barr virus (EBV) ZEBRA protein (also termed BZLF-1 encoded replication protein Zta or ZEBRA) binds to both NF-κB and p53. The authors have previously suggested that this interaction results from an ankyrin-like region of the ZEBRA protein since ankyrin proteins such as IκB interact with NF-κB and p53 proteins. These interactions may play a role in immunopathology and viral carcinogenesis in B lymphocytes as well as other cell types transiently infected by EBV such as T lymphocytes, macrophages and epithelial cells. METHODS: Randomization of the ZEBRA terminal amino acid sequence followed by statistical analysis suggest that the ZEBRA carboxyl terminus is most closely related to ankyrins of the invertebrate cactus IκB-like protein. This observation is consistent with an ancient origin of ZEBRA resulting from a recombination event between an ankyrin regulatory protein and a fos/jun DNA binding factor. In silico modeling of the partially solved ZEBRA carboxyl terminus structure using PyMOL software demonstrate that the carboxyl terminus region of ZEBRA can form a polymorphic structure termed ZANK (ZEBRA ANKyrin-like region) similar to two adjacent IκB ankyrin domains. CONCLUSIONS: Viral capture of an ankyrin-like domain provides a mechanism for ZEBRA binding to proteins in the NF-κB and p53 transcription factor families, and also provides support for a process termed "Ping-Pong Evolution" in which DNA viruses such as EBV are formed by exchange of information with the host genome. An amino acid polymorphism in the ZANK region is identified in ZEBRA from tumor cell lines including Akata that could alter binding of Akata ZEBRA to the p53 tumor suppressor and other ankyrin binding protein, and a novel model of antagonistic binding interactions between ZANK and the DNA binding regions of ZEBRA is suggested that may be explored in further biochemical and molecular biological models of viral replication.

Systematic Analysis and Identification of Dysregulated Panel lncRNAs Contributing to Poor Prognosis in Head-Neck Cancer.

Head and neck cancer (HNC) is one of the most prevalent cancers worldwide, accounting for approximately 5% of all cancers. While the underlying molecules and their pathogenetic mechanisms in HNC have yet to be well elucidated, recent studies have shown that dysregulation of lncRNAs may disrupt the homeostasis of various biological pathways. However, the understanding of lncRNAs in HNC is still limited by the lack of expression profiling. In the present study, we employed a systematic strategy to identify a panel of lncRNA associated with HNC. A cancer-related lncRNA profile PCR array was screened to explore potential molecules specific for HNC. A total of 55 lncRNAs were found to be dysregulated in HNC cells when compared to normal keratinocytes. Further analysis of the prognostic significance using The Cancer Genome Atlas (TCGA) database revealed 15 lncRNAs highly correlated with overall survival in HNC patients. Additionally, clinical sample expression analysis of the TCGA-HNSC cohort revealed 16 highly dysregulated lncRNAs in HNC, resulting in a combined 31-lncRNA signature panel that could predict prognosis. Validation of these molecules confirmed the considerable level of altered expressions in HNC cells, with XIST, HOXA11-AS, TSIX, MALAT1, WT1-AS, and IPW being the most prominently dysregulated. We further selected a molecule from our panel (XIST) to confirm the validity of these lncRNAs in the regulation of cancer aggressiveness. Gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses demonstrated that XIST participated in various cancer-related functions, including cell proliferation and metastasis. XIST silencing with the RNAi technique substantially reduced invasion and migration in several HNC cell lines. Thus, our study defined a 31-lncRNA panel as prognostic signatures in HNC. These perspective results provide a knowledge foundation for further application of these molecules in precision medicine.

Spectral neighbor joining for reconstruction of latent tree Models.

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover the structure of latent tree graphical models. Given a matrix that contains a measure of similarity between all pairs of observed variables, SNJ computes a spectral measure of cohesion between groups of observed variables. We prove that SNJ is consistent, and derive a sufficient condition for correct tree recovery from an estimated similarity matrix. Combining this condition with a concentration of measure result on the similarity matrix, we bound the number of samples required to recover the tree with high probability. We illustrate via extensive simulations that in comparison to several other reconstruction methods, SNJ requires fewer samples to accurately recover trees with a large number of leaves or long edges.

Panel biomarkers associated with cancer invasion and prognostic prediction for head-neck cancer.

Aim: Cell invasion leading to metastasis is a major cause of treatment failure in head-neck cancers (HNCs). Identifying prognostic molecules associated with invasiveness is imperative for clinical applications. Materials & methods: A systemic approach was used to globally survey invasion-related genes, including transcriptomic profiling, pathway analysis, data mining and prognostic assessment using TCGA-HNSC dataset. Results: Six functional pathways and six hub molecules (LAMA3, LAMC2, THBS1, IGF1R, PDGFB and TGFß1) were identified that significantly contributed to cell invasion, leading to poor survival in HNC patients. Combinations of multiple biomarkers substantially increased the probability of accurately predicting prognosis. Conclusion: Our six defined invasion-related molecules may be used as a panel signature in precision medicine for prognostic indicators or molecular therapeutic targets for HNC.