Recognition of the long range enhancer-promoter interactions by further adding DNA structure properties and transcription factor binding motifs in human cell lines.

The enhancer-promoter interactions (EPIs) with strong tissue-specificity play an important role in cis-regulatory mechanism of human cell lines. However, it still remains a challenging work to predict these interactions so far. Due to that these interactions are regulated by the cooperativeness of diverse functional genomic signatures, DNA spatial structure and DNA sequence elements. In this paper, by adding DNA structure properties and transcription factor binding motifs, we presented an improved computational method to predict EPIs in human cell lines. In comparison with the results of other group on the same datasets, our best accuracies by cross-validation test were about 15%-24% higher in the same cell lines, and the accuracies by independent test were about 11%-15% higher in new cell lines. Meanwhile, we found that transcription factor binding motifs and DNA structure properties have important information that would largely determine long range EPIs prediction. From the distribution comparisons, we also found their distinct differences between interacting and non-interacting sets in each cell line. Then, the correlation analysis and network models for relationships among top-ranked functional genomic signatures indicated that diverse genomic signatures would cooperatively establish a complex regulatory network to facilitate long range EPIs. The experimental results provided additional insights about the roles of DNA intrinsic properties and functional genomic signatures in EPIs prediction.

Recognition of long-range enhancer-promoter interactions by adding genomic signatures of segmented regulatory regions.

Enhancer-promoter interaction (EPI) is an important cis-regulatory mechanism in the regulation of tissue-specific gene expression. However, it still has limitation to precisely identity these interactions so far. In this paper, using diverse genomic features for various regulatory regions, we presented a computational approach to predict EPIs with improved accuracies. Meanwhile, we comprehensively studied more potential regulatory factors that are important to EPIs prediction, such as nucleosome occupancy, enhancer RNA; and found the cell line-specificity and region-specificity of the contributions of diverse regulatory signatures. By adding genomic signatures of segmented regulatory regions, our best accuracies of cross-validation test were about 11%-16% higher than the previous results, indicating the location-specificity of genomic signatures in a regulatory region for predicting EPIs. Additionally, more training samples and related features can provide reliable performances in new cell lines. Consequently, our study provided additional insights into the roles of diverse signature features for predicting long-range EPIs.

Non-coding RNA identification based on topology secondary structure and reading frame in organelle genome level.

Non-coding RNA (ncRNA) genes make transcripts as same as the encoding genes, and ncRNAs directly function as RNAs rather than serve as blueprints for proteins. As the function of ncRNA is closely related to organelle genomes, it is desirable to explore ncRNA function by confirming its provenance. In this paper, the topology secondary structure, motif and the triplets under three reading frames are considered as parameters of ncRNAs. A method of SVM combining the increment of diversity (ID) algorithm is applied to construct the classifier. When the method is applied to the ncRNA dataset less than 80% sequence identity, the overall accuracies reach 95.57%, 96.40% in the five-fold cross-validation and the jackknife test, respectively. Further, for the independent testing dataset, the average prediction success rate of our method achieved 93.24%. The higher predictive success rates indicate that our method is very helpful for distinguishing ncRNAs from various organelle genomes.

[Metastatic characteristics of lymph node in supraclavicular zone and radiotherapy target volume for limited-stage small cell lung cancer].

OBJECTIVE: To explore the reasonable radiotherapy range by analyzing the characteristics of supraclavicular lymph node metastasis in limited-stage small cell lung cancer (LS-SCLC). METHODS: From January 2005 to December 2011, patients of LS-SCLC were reviewed. Supraclavicular zone was further divided into five subgroups including para-recurrent laryngeal nerve (region I and region II ), para-internal jugular vein (region III ), supraclavicular region (region IV), as well as the other regions except for the mentioned above (region V). The characteristics of the lymph nodes in each region were analyzed. RESULTS: The supraclavicular lymph node metastasis was found in 60 patients, with a positive rate of 34.5%. In multivariate Logistic regression analysis,intra-thoracic lymph node metastasis in the lymph node stations of level 2 and 3 were found to be the risk factors of supraclavicular lymph node metastasis (P = 0.006,P = 0.000). Our data suggests that the frequencies of metastasis in region I and III were much higher than those in the other areas.Among the sixty patients with supraclavicular lymph node metastasis, 95.0% were found at region I or III while the incidence of skip metastasis was only 5.0%. CONCLUSIONS: It is advisable to contain the bilateral supraclavicular nodes in patients with mediastinal lymph nodes metastasis to the level 2 or 3 for elective radiation target volume.The clinical target volume (CTV) exterior margin containing the outer margin of internal jugular vein may be suitable.

Identification of risk factors and characteristics of supraclavicular lymph node metastasis in patients with small cell lung cancer.

Thoracic radiotherapy provides a survival benefit in patients with limited-stage disease of small cell lung cancer (LS-SCLC), but inclusion and exclusion of prophylactic irradiation of the supraclavicular area are still controversial. This study analyses the risk factors and characteristics of lymph node metastases in the supraclavicular area of LS-SCLC patients, which could help in developing a better radiotherapy for the patients. A total of 239 patients with LS-SCLC were included in this retrospective analysis. Clinical characteristics and mediastinal lymph node metastasis were analyzed for association with SCM, and the SCM pattern was further analyzed based on the treatment planning CT scans. The SCM incidence was 34.7 % (83 of 239). The multivariate analysis showed that only the mediastinal level 2 (OR = 16.101, P = 0.000) and level 3 (OR = 5.597, P = 0.000) lymph node metastases were significantly associated with SCM. As the most frequently involved region, supraclavicular level I lymph node metastases were identified in 61 of 83 patients (73.5 %), followed by level III, level IV, level V, and level II lymph node metastases, accounting a total of 95.2 % for level I and/or III lymph node metastases, whereas the incidence of skip metastasis was only 4.8 %. SCLC patients with mediastinal level 2 and level 3 lymph node metastasis were at high risk of SCM. If prophylactic irradiation therapy is considered, the nodal clinical target volume of irradiation should include bilateral lower para-recurrent laryngeal neural region (level I) and the para-internal jugular venous region (level III).