Network analysis in the identification of special mechanisms between small cell lung cancer and non-small cell lung cancer

Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

DNA repair pathways and their roles in drug resistance for lung adenocarcinoma

Lung cancer is the leading cancer type of death rate. The lung adenocarcinoma subtype is responsible for almost half of the total lung cancer deaths. Despite the improvements in cancer treatment in recent years, lung adenocarcinoma patients' overall survival rate remains poor. Immunetherapy and chemotherapy are two of the most widely used options for the treatment of cancer. Although many cancer types initially respond to these treatments, the development of resistance is inevitable. The rapid development of drug resistance mainly characterizes lung adenocarcinoma. Despite being the subject of many studies in recent years, the resistance initiation and progression mechanism is still unclear. In this review, we have examined the role of the primary DNA repair pathways (non-homologous end joining (NHEJ) pathway, homologous-recombinant repair (HR) pathway, base excision repair (BER) pathway, and nucleotide excision repair (NER) pathway and transactivation mechanisms of tumor protein 53 (TP53) in drug resistance development. This review suggests that mentioned pathways have essential roles in developing the resistance against chemotherapy and immunotherapy in lung adenocarcinoma patients.

IL-17F expression correlates with clinicopathologic factors and biological markers in non-small cell lung cancer

Interleukin-17 F (IL-17F) is a pro-inflammatory cytokine that participate in inflammatory responses. Studies showed that IL-17F is likely involved in tumor development, but the biological function of IL-17F in non-small cell lung cancer (NSCLC) is unclear. The aim of this study was to explore the biological role of IL-17F in NSCLC and investigate its correlation with biological markers CD31, P53, Ki-67 and E-cadherin. Paraffin-embedded tumor tissues from 55 NSCLC patients were collected to detect proteins expression using immunohistochemistry (IHC). 12 normal lung tissues samples were used as control. IHC results showed that the expression of IL-17F in NSCLC cells (61.8%) was significantly higher compared with normal lung tissues (25.0%) (P <  0.05). The expression of IL-17F was positively associated with tumor differentiation and negatively associated with lymph node metastasis and TNM staging (P all < 0.05). Multivariate analysis showed that IL-17F expression was an independent factor associated with TNM staging (P <  0.01). Pearson's correlation analysis showed a negative correlation between IL-17F and CD31 expression and a positive correlation between IL-17F and E-cadherin expression (P all < 0.05). There was no relationship between IL-17 F and P53 or Ki-67 expression in NSCLC tissues (P >  0.05). These data suggest that IL-17 F may be considered as a potential marker for predicting the progression of NSCLC.

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.

Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model

Background

The purpose of this study was to achieve early and accurate diagnosis of lung cancer and long-term monitoring of the therapeutic response.

Methods

We downloaded GSE20189 from GEO database as analysis data. We also downloaded human lung adenocarcinoma RNA-seq transcriptome expression data from the TCGA database as validation data. Finally, the expression of all of the genes underwent z test normalization. We used ANOVA to identify differentially expressed genes specific to each stage, as well as the intersection between them. Two methods, correlation analysis and co-expression network analysis, were used to compare the expression patterns and topological properties of each stage. Using the functional quantification algorithm, we evaluated the functional level of each significantly enriched biological function under different stages. A machine-learning algorithm was used to screen out significant functions as features and to establish an early diagnosis model. Finally, survival analysis was used to verify the correlation between the outcome and the biomarkers that we found.

Results

We screened 12 significant biomarkers that could distinguish lung cancer patients with diverse risks. Patients carrying variations in these 12 genes also presented a poor outcome in terms of survival status compared with patients without variations.

Conclusions

We propose a new molecular-based noninvasive detection method. According to the expression of the stage-specific gene set in the peripheral blood of patients with lung cancer, the difference in the functional level is quantified to realize the early diagnosis and prediction of lung cancer.

A regulation probability model-based meta-analysis of multiple transcriptomics data sets for cancer biomarker identification

BACKGROUND

Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent but subtle gene activity patterns across studies. Study heterogeneity needs to be addressed carefully for this goal.

RESULTS

This paper proposes a regulation probability model-based meta-analysis, jGRP, for identifying differentially expressed genes (DEGs). The method integrates multiple transcriptomics data sets in a gene regulatory space instead of in a gene expression space, which makes it easy to capture and manage data heterogeneity across studies from different laboratories or platforms. Specifically, we transform gene expression profiles into a united gene regulation profile across studies by mathematically defining two gene regulation events between two conditions and estimating their occurring probabilities in a sample. Finally, a novel differential expression statistic is established based on the gene regulation profiles, realizing accurate and flexible identification of DEGs in gene regulation space. We evaluated the proposed method on simulation data and real-world cancer datasets and showed the effectiveness and efficiency of jGRP in identifying DEGs identification in the context of meta-analysis.

CONCLUSIONS

Data heterogeneity largely influences the performance of meta-analysis of DEGs identification. Existing different meta-analysis methods were revealed to exhibit very different degrees of sensitivity to study heterogeneity. The proposed method, jGRP, can be a standalone tool due to its united framework and controllable way to deal with study heterogeneity.

The relationship between histopathological and imaging features of sacroiliitis

This study aimed to investigate the histopathological feature of sacroiliitis and to examine its relationship with imaging features using various imaging techniques. Computed-tomography (CT)-guided needle biopsy of the sacroiliac (SI) joints was performed in 36 patients with spondyloarthritis. Histopathological examination was performed on the samples and subjects were divided as with or without sacroiliitis according histopathology. Magnetic resonance imaging (MRI), Single-photon emission CT (SPECT), CT scans and plain X-rays of the SI joints were conducted. Histopathological changes indicative of sacroiliitis were found in 29 (81%) of the 36 patients, with changes in cartilage (72.4%) and subchondral bone (72.4%) being the most common features. Histopathological sacroiliitis was not found in seven (19.4%) patients. Using MRI, sacroiliitis was found in 29 (80.5%) patients. Increased SI index (> 1.34) by SPECT scans indicative of sacroiliitis was found in 29 (80.5%) patients. Sacroiliitis was detected by CT and plain X-rays in 23 (63.8%) and 19 (52.7%) patients, respectively. Using the histopathological sacroiliitis as the gold standard, sensitivity for SPECT, MRI, CT and plain X-rays was 92.8%, 96.4%, 73.3%, respectively, and 64.2% and the corresponding specificity was 62.5%, 75%, 87.5% and 87.5%, respectively. Needle biopsy is an important method for the diagnosis of sacroiliitis. MRI and SPECT have comparable diagnostic value as CT and plain X-rays and can quantify the inflammatory activity. A combination of these two techniques could increase the sensitivity and specificity and serve as a valuable tool for the diagnosis, assessment and monitoring of sacroiliitis.