Tracking significant modules and key genes for esophageal squamous cell carcinoma based on differential modules.

BACKGROUND: The exact molecular mechanism of esophageal squamous cell carcinoma (ESCC) is still unknown, and the prognosis of ESCC has not been significantly improved. OBJECTIVE: To understand the molecular mechanism of ESCC, differential modules (DMs) and key genes were identified through conducting analysis on the differential co-expression network (DCN) based on the gene expression profiles of ESCC and protein-protein interaction (PPI) data. MATERIALS AND METHODS: First, gene expression profiles of ESCC and PPI data recruiting and preprocessing were conducted; then, a DCN was constructed based on the gene co-expression and gene differential expression in ESCC; in the following, candidate DMs were mined from DCN through a systemic module searching strategy, and significance analysis was performed on candidate DMs to identify DMs; moreover, significant genes contained in the DMs were analyzed to identify the underlying biomarkers for ESCC. Finally, pathway enrichment analysis was conducted to disclose the function of these DMs. RESULTS: A total of 10,975 genes were obtained after comprehensively preprocessing on the gene expression profiles and PPI data. Then, a DCN with 915 nodes (1164 interactions) was built, and 45 seed genes were identified. In the following, four DMs that separately enriched in phenylalanine metabolism, nicotine addiction, phenylalanine metabolism, and B-cell receptor signaling pathway were identified, where module 1 and module 3 were all enriched in phenylalanine metabolism pathway. Furthermore, the most significant seed gene myeloperoxidase (MPO) was contained in all of the DMs. CONCLUSIONS: In this study, we successfully identified 4 DMs, three significant pathways, and a key gene MPO in ESCC, which might play key roles during the occurrence and development of ESCC and could be chosen as good indicators and therapeutic schedule for ESCC.

Utilizing multiple pathway cross-talk networks reveals hub pathways in primary mediastinal B-cell lymphoma.

OBJECTIVE: The objective of this paper was to reveal hub pathways in primary mediastinal B-cell lymphoma (PMBL) based on multiple pathway crosstalk networks (PCNs) and give insight for its pathological mechanism. MATERIALS AND METHODS: Based on gene expression data, pathway data and protein-protein interaction data, background PCN (BPCN) and tumor PCN (TPCN) of PMBL were constructed. The rank product algorithm was implemented to identify hub pathways of BPCN and TPCN. Finally, topological properties (degree, closeness, betweenness, and transitivity) of hub pathways were analyzed. RESULTS: For BPCN, there were three hundred nodes and 42,239 edges, and the pathway pairs had great overlaps. TPCN was composed of 281 nodes and 12,700 cross-talks. A total of five hub pathways were identified, nonalcoholic fatty liver disease (NAFLD), tuberculosis, human T-lymphotropic virus type-I (HTLV-I) infection, hepatitis B, and Epstein-Barr virus infection. The topological properties for them were different from each other, further between PMBL and normal controls. CONCLUSION: We have identified five hub pathways for PMBL, such as NAFLD, HTLV-I infection, and Hepatitis B, which might be potential biomarkers for target therapy for PMBL.

Pathway cross-talk network strategy reveals key pathways in non-small cell lung cancer.

PURPOSE: The purpose of this study was to explore the pathway cross-talks and key pathways in non-small cell lung cancer (NSCLC) to better understand the underlying pathological mechanism. METHODS: Integrated gene expression data, pathway data and protein-protein interaction (PPI) data were assessed to identify the pathway regulatory interactions in NSCLC, and constructed the background and disease pathway crosstalk networks, respectively. In this work, the attractor method was implemented to identified the differential pathways, and the rank product (RP) algorithm was used to determine the importance of pathways. RESULTS: Based on 787,896 PPI interactions from STRING database and 300 human pathways from KEGG, we constructed the back pathway cross-talk network with 300 nodes and 42239 edges. Integrating with expression data of NSCLC, each pathway cross-talk endowed with a weight value, and disease pathway cross-talks were identified. By RP algorithm and topology analysis of network, we selected 5 key pathways, including Alanine, DNA replication, Fanconi anemia pathway, Cell cycle and MicroRNAs in cancer under the pre-set thresholds. CONCLUSION: We successfully revealed the disease pathway cross-talks and explored 5 key pathways in NSCLC, which may be the underlying therapeutic targets for lung cancer.

Integrating differential pathway analysis with Monte Carlo cross-validation reveals seed cross-talks in non-small cell lung carcinoma.

PURPOSE: The objective of this study was to identify seed pathway cross-talks in non-small cell lung carcinoma (NSCLC), and to reveal potential pathological mechanism at molecular level systematically. METHODS: Differentially expressed genes (DEGs) between NSCLC and normal controls were identified using quantile- adjusted conditional maximum likelihood (QCML) method. Subsequently, differential pathways (DPs) enriched by DEGs were determined according to the Ingenuity Pathways Analysis )IPA) pathways and Fisher's exact test. A discriminating score )DS) was computed for each pair of DPs also called as cross-talk, and random forest )RF) algorithm was implemented to investigated hub cross-talks. Finally, global cross-talks with repeated times > 5 were calculated by Monte Carlo Cross-Validation )MCCV). By taking intersections between hub cross-talks and global crosstalks, we obtained seed cross-talks. RESULTS: We obtained 122 DEGs and 5 DPs between NSCLC samples and normal controls. Based on DS and RF algorithm, 5 hub cross-talks with best area under the curve )AUC) were identified, of which Agranulocyte Adhesion and Diapedesis, and IL-17A Signaling in Fibroblasts were the best with AUC=0.996. After intersected with global cross-talks, we gained 2 seed cross-talks (Agranulocyte Adhesion and Diapedesis, Granulocyte Adhesion and Diapedesis and Agranulocyte Adhesion and Diapedesis, Glutathione Redox Reactions I). CONCLUSIONS: Two seed cross-talks were identified and validated by MCCV, which may give insights for revealing pathological mechanism and potential biomarkers for target therapy in NSCLC.

MiRNA-155 and miRNA-132 as potential diagnostic biomarkers for pulmonary tuberculosis: A preliminary study.

In our study, we aimed to profile a panel microRNAs (miRNAs) as potential biomarkers for the early diagnosis of pulmonary tuberculosis (PTB) and to illuminate the molecular mechanisms in the development of PTB. Firstly, gene expression profile of E-GEOD-49951 was downloaded from ArrayExpress database, and quantile-adjusted conditional maximum likelihood method was utilized to identify statistical difference between miRNAs of Mycobacterium tuberculosis (MTB)-infected individuals and healthy subjects. Furthermore, in order to assess the performance of our methodology, random forest (RF) classification model was utilized to identify the top 10 miRNAs with better Area Under The Curve (AUC) using 10-fold cross-validation method. Additionally, Monte Carlo Cross-Validation was repeated 50 times to explore the best miRNAs. In order to learn more about the differentially-expressed miRNAs, the target genes of differentially-expressed miRNAs were retrieved from TargetScan database and Ingenuity Pathways Analysis (IPA) was used to screen out biological pathways where target genes were involved. After normalization, a total of 478 miRNAs with higher than 0.25-fold quantile average across all samples were required. Based on the differential expression analysis, 38 differentially expressed miRNAs were identified when the significance was set as false discovery rate (FDR) < 0.01. Among the top 10 differentially expressed miRNAs, miRNA-155 obtained a highest AUC value 0.976, showing a good performance between PTB and control groups. Similarly, miRNA-449a, miRNA-212 and miRNA-132 revealed also a good performance with AUC values 0.947, 0.931 and 0.930, respectively. Moreover, miRNA-155, miRNA-449a, miRNA-29b-1* and miRNA-132 appeared in 50, 49, 49 and 48 bootstraps. Thus, miRNA-155 and miRNA-132 might be important in the progression of PTB and thereby, might present potential signatures for diagnosis of PTB.

[An experimental research on inhibitory action of skeletal muscle conditioned medium on metastases of lung carcinomas].

BACKGROUND & OBJECTIVE: Malignant tumors spread and metastasize in the majority of the organs, but are very rare in skeletal muscles. This study was conducted to explore the effect of organic microenvironment of skeletal muscles on the proliferation of pulmonary large cell carcinomas with different metastatic potential and to investigate the mechanism of the rarity of metastases in skeletal muscles. METHODS: Primary culture of newborn Wistar rat skeletal muscle cells was established, and the murine skeletal muscle conditioned medium(MMCM)was prepared to test its effect in vitro on pulmonary large cell carcinomas with different metastatic potential (PLA-801C with lower potential and PLA-801D with relatively higher potential) by MTT assay. Adriamycin was used as positive control for MMCM; murine benign renal cells BHK-21 were used as negative control for lung carcinoma cells. RESULTS: Proliferation of tumor cell lines of both PLA-801C and PLA-801D was significantly restrained when cultured with MMCM, while BHK-21 cells were not affected(P< 0.05). Compared with PLA-801C (significant only in primary MMCM), PLA-801D showed significantly decreased proliferation even when cultured in higher reciprocal of MMCM dilution(1/16 of primary MMCM). CONCLUSION: Skeletal muscle cells could selectively inhibit the proliferation of cancerous cells in vitro while benign cells are not affected. Tumor cells with higher metastatic potential are more sensitive to this effect.