A transcriptional network signature characterizes lung cancer subtypes

Regulation of early diagnosis and prognostic markers of lung adenocarcinoma in immunity and hypoxia

Lung adenocarcinoma is still cancer with the highest mortality. Hypoxia and immunity play an essential role in the occurrence and development of tumors. Therefore, this study is mainly to find new early diagnosis and prognosis markers and explore the relationship among the markers and immunity and hypoxia, to improve the prognosis of patients. Firstly, based on the clinical database in TCGA, we determined the most critical clinicopathological parameters affecting the prognosis of patients through a variety of analysis methods. According to pathological parameters, logistic most minor absolute contraction selection operator (lasso), univariate and multivariate regression analysis, the risk genes related to early prognosis were screened, and the risk model was established. Then, in different risk groups, GSEA and CIBERSORT algorithms were used to analyze the distribution and enrichment of the immune cells and hypoxia, to study the effects of early prognostic indicators on hypoxia and immunity. At the same time, we analyzed the different levels of risk genes in normal cells (BSEA-2B) and tumor cells (H1299, A549, PC9, and H1975). Finally, A549 and PC9 cells were induced by CoCl2 to establish a hypoxic environment, and the correlation between risk genes and HIF1A was analyzed. The risk model based on risk genes (CYP4B1, KRT6A, and FAM83A) was accurate and stable for the prognosis of patients. It is closely related to immunity and hypoxia. In BSEA-2B cells, the mRNA and protein expression of CYP4B1 was higher, while the expression of KRT6A and FAM83A was lower. Finally, we found that FAM83A and HIF1A showed a significant positive correlation when A549 and PC9 cells were exposed to hypoxia. The discovery of early diagnostic markers related to immunity, hypoxia, and prognosis, provides a new idea for early screening and prognostic treatment of lung adenocarcinoma.

An explainable AI-driven biomarker discovery framework for Non-Small Cell Lung Cancer classification

Non-Small Cell Lung Cancer (NSCLC) exhibits intrinsic heterogeneity at the molecular level that aids in distinguishing between its two prominent subtypes - Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). This paper proposes a novel explainable AI (XAI)-based deep learning framework to discover a small set of NSCLC biomarkers. The proposed framework comprises three modules - an autoencoder to shrink the input feature space, a feed-forward neural network to classify NSCLC instances into LUAD and LUSC, and a biomarker discovery module that leverages the combined network comprising the autoencoder and the feed-forward neural network. In the biomarker discovery module, XAI methods uncovered a set of 52 relevant biomarkers for NSCLC subtype classification. To evaluate the classification performance of the discovered biomarkers, multiple machine-learning models are constructed using these biomarkers. Using 10-Fold cross-validation, Multilayer Perceptron achieved an accuracy of 95.74% (±1.27) at 95% confidence interval. Further, using Drug-Gene Interaction Database, we observe that 14 of the discovered biomarkers are druggable. In addition, 28 biomarkers aid the prediction of the survivability of the patients. Out of 52 discovered biomarkers, we find that 45 biomarkers have been reported in previous studies on distinguishing between the two NSCLC subtypes. To the best of our knowledge, the remaining seven biomarkers have not yet been reported for NSCLC subtyping and could be further explored for their contribution to targeted therapy of lung cancer.

Identification of the Transcriptional Regulatory Role of RUNX2 by Network Analysis in Lung Cancer Cells

The use of a new bioinformatics pipeline allowed the identification of deregulated transcription factors (TFs) coexpressed in lung cancer that could become biomarkers of tumor establishment and progression. A gene regulatory network (GRN) of lung cancer was created with the normalized gene expression levels of differentially expressed genes (DEGs) from the microarray dataset GSE19804. Moreover, coregulatory and transcriptional regulatory network (TRN) analyses were performed for the main regulators identified in the GRN analysis. The gene targets and binding motifs of all potentially implicated regulators were identified in the TRN and with multiple alignments of the TFs' target gene sequences. Six transcription factors (E2F3, FHL2, ETS1, KAT6B, TWIST1, and RUNX2) were identified in the GRN as essential regulators of gene expression in non-small-cell lung cancer (NSCLC) and related to the lung tumoral process. Our findings indicate that RUNX2 could be an important regulator of the lung cancer GRN through the formation of coregulatory complexes with other TFs related to the establishment and progression of lung cancer. Therefore, RUNX2 could become an essential biomarker for developing diagnostic tools and specific treatments against tumoral diseases in the lung after the experimental validation of its regulatory function.

A Novel 2-Metagene Signature to Identify High-Risk HNSCC Patients amongst Those Who Are Clinically at Intermediate Risk and Are Treated with PORT

(1) Background: Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) who are biologically at high risk for the development of loco−regional recurrences after postoperative radiotherapy (PORT) but at intermediate risk according to clinical risk factors may benefit from additional concurrent chemotherapy. In this matched-pair study, we aimed to identify a corresponding predictive gene signature. (2) Methods: Gene expression analysis was performed on a multicenter retrospective cohort of 221 patients that were treated with postoperative radiochemotherapy (PORT-C) and 283 patients who were treated with PORT alone. Propensity score analysis was used to identify matched patient pairs from both cohorts. From differential gene expression analysis and Cox regression, a predictive gene signature was identified. (3) Results: 108 matched patient pairs were selected. We identified a 2-metagene signature that stratified patients into risk groups in both cohorts. The comparison of the high-risk patients between the two types of treatment showed higher loco−regional control (LRC) after treatment with PORT-C (p < 0.001), which was confirmed by a significant interaction term in Cox regression (p = 0.027), i.e., the 2-metagene signature was indicative for the type of treatment. (4) Conclusion: We have identified a novel gene signature that may be helpful to identify patients with high-risk HNSCC amongst those at intermediate clinical risk treated with PORT, who may benefit from additional concurrent chemotherapy.

Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

Lung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.

KRT6A Promotes Lung Cancer Cell Growth and Invasion Through MYC-Regulated Pentose Phosphate Pathway

Keratin 6A (KRT6A) belongs to the keratin protein family which is a critical component of cytoskeleton in mammalian cells. Although KRT6A upregulation in non-small cell lung cancer (NSCLC) has been reported, the regulatory mechanism and functional role of KRT6A in NSCLC development have been less well investigated. In this study, KRT6A was confirmed to be highly expressed in NSCLC tissue samples, and its high expression correlated with poor patient prognosis. Furthermore, overexpression of KRT6A promotes NSCLC cell proliferation and invasion. Mechanistically, KRT6A overexpression is sufficient to upregulate glucose-6-phosphate dehydrogenase (G6PD) levels and increase the pentose phosphate pathway flux, an essential metabolic pathway to support cancer cell growth and invasion. In addition, we discovered that lysine-specific demethylase 1A (LSD1) functions upstream to promote KRT6A gene expression. We also found that the MYC family members c-MYC/MYCN are involved in KRT6A-induced G6PD upregulation. Therefore, this study reveals an underappreciated mechanism that KRT6A acts downstream of LSD1 and functions as a pivotal driver for NSCLC progression by upregulating G6PD through the MYC signaling pathway. Together, KRT6A and LSD1 may serve as potential prognostic indictors and therapeutic targets for NSCLC.

Integrative RNA-Seq and H3 Trimethylation ChIP-Seq Analysis of Human Lung Cancer Cells Isolated by Laser-Microdissection

Simple Summary

Tissue heterogeneity is one of the major problems in cancer genomics. Thus, we developed and conducted an RNA-Seq and ChIP-Seq integrative analysis of clinical lung tissue samples with the isolation of specific cell populations using laser-microdissection microscopy (LMD). The transcriptomic profile was successfully captured and somatically altered regions marked by histone H3 lysine 4 trimethylation (H3K4me3) were identified in lung cancer. We also observed the differential expressions of cancer-related genes near the altered proximal H3K4me3 regions, while altered distal H3K4me3 regions were overlapped with enhancer activity annotations of cancer regulatory genes. Additionally, proximal tumor-gained promoters were associated with the core components of polycomb repressive complex 2. Our study demonstrates the practical workflow of using LMD on clinical samples for integrative analyses, which improves the overall understanding of genetic and epigenetic dysregulation of malignancy.

Abstract

Our previous integrative study in gastric cancer discovered cryptic promoter activation events that drive the expression of important developmental genes. However, it was unclear if such cancer-associated epigenetic changes occurred in cancer cells or other cell types in bulk tissue samples. An integrative analysis consisting of RNA-Seq and H3K4me3 ChIP-Seq was used. This workflow was applied to a set of matched normal lung tissues and non-small cell lung cancer (NSCLC) tissues, for which the stroma and tumor cell parts could be isolated by laser-microdissection microscopy (LMD). RNA-Seq analysis showed subtype-specific differential expressed genes and enriched pathways in NSCLC. ChIP-Seq analysis results suggested that the proximal altered H3K4me3 regions were located at differentially expressed genes involved in cancer-related pathways, while altered distal H3K4me3 regions were annotated with enhancer activity of cancer regulatory genes. Interestingly, integration with ENCODE data revealed that proximal tumor-gained promoters were associated with EZH2 and SUZ12 occupancies, which are the core components of polycomb repressive complex 2 (PRC2). This study used LMD on clinical samples for an integrative analysis to overcome the tissue heterogeneity problem in cancer research. The results also contribute to the overall understanding of genetic and epigenetic dysregulation of lung malignancy.

KRT6A Promotes EMT and Cancer Stem Cell Transformation in Lung Adenocarcinoma

Aim:

Keratin 6A is a type II cytokeratin which is important in forming nail bed, filiform papillae, the epithelial lining of oral mucosa, and esophagus; recently, keratin 6A was found hyperexpressed in different types of cancer. But, the biological function of keratin 6A in lung adenocarcinoma still remains unclear. Therefore, in current study, we investigated the biological role of keratin 6A in lung adenocarcinoma.

Methods:

By utilizing The Cancer Genome Atlas database, we investigated the expression profile of keratin 6A and its relationship with other clinical parameters in lung adenocarcinoma. The biological function of keratin 6A in lung adenocarcinoma was also investigated by using A549 and PC-9 lung cancer cell lines in vitro.

Results:

Our data indicate that, compared with normal lung tissue samples, keratin 6A was hyperexpressed in lung adenocarcinoma. Moreover, keratin 6A hyperexpression was positively correlated with lymph node positive and aggressive tumor T stage. Keratin 6A knockdown inhibited the cell proliferation, migration, and colony formation ability but not cell death in lung adenocarcinoma cells. In addition, we found keratin 6A exerted its phenotype via promoting cancer stem cells (CXCR4^high/CD133^high) transformation and epithelial–mesenchymal transition.

Conclusion:

In conclusion, current study suggests that hyperexpressed keratin 6A in lung adenocarcinoma promotes lung cancer proliferation and metastasis via epithelial–mesenchymal transition and cancer stem cells transformation.

A 3D biofabricated cutaneous squamous cell carcinoma tissue model with multi-channel confocal microscopy imaging biomarkers to quantify antitumor effects of chemotherapeutics in tissue

Cutaneous squamous cell carcinoma (cSCC) causes approximately 10,000 deaths annually in the U. S. Current therapies are largely ineffective against metastatic and locally advanced cSCC. There is a need to identify novel, effective, and less toxic small molecule cSCC therapeutics. We developed a 3-dimensional bioprinted skin (3DBPS) model of cSCC tumors together with a microscopy assay to test chemotherapeutic effects in tissue. The full thickness SCC tissue model was validated using hematoxylin and eosin (H&E) and immunohistochemical histological staining, confocal microscopy, and cDNA microarray analysis. A nondestructive, 3D fluorescence confocal imaging assay with tdTomato-labeled A431 SCC and ZsGreen-labeled keratinocytes was developed to test efficacy and general toxicity of chemotherapeutics. Fluorescence-derived imaging biomarkers indicated that 50% of cancer cells were killed in the tissue after 1μM 5-Fluorouracil 48-hour treatment, compared to a baseline of 12% for untreated controls. The imaging biomarkers also showed that normal keratinocytes were less affected by treatment (11% killed) than the untreated tissue, which had no significant killing effect. Data showed that 5-Fluorouracil selectively killed cSCC cells more than keratinocytes. Our 3DBPS assay platform provides cellular-level measurement of cell viability and can be adapted to achieve nondestructive high-throughput screening (HTS) in bio-fabricated tissues.

Anti-tumour effects of Keratin 6A in lung adenocarcinoma

BACKGROUND

To examine the effects of Keratin 6A (KRT6A) protein on the proliferation, migration and invasion abilities of lung adenocarcinoma cells, and to analyse the relationship between the expression level of KRT6A protein and the survival prognosis of lung adenocarcinoma patients.

METHODS

Western Blot was used to detect the expression of KRT6A protein in lung adenocarcinoma cell lines. CCK-8 experiment and colony formation assays were performed to detect the proliferation ability. Wound healing assay and transwell migration assay were conducted to detect the migration ability. Transwell invasion assay was conducted to detect the invasion ability. Immunohistochemistry was used to detect the expression of KRT6A protein in lung adenocarcinoma tissues.

RESULTS

We first found that the expression of KRT6A protein in lung adenocarcinoma cell lines was low. After overexpressed KRT6A protein in lung adenocarcinoma cells, we then found that KRT6A protein could not only inhibit the proliferation ability of lung adenocarcinoma cells but also inhibit them migration and invasion abilities. In addition, we also found that there had obvious difference in the expression of KRT6A protein in between patients. And through further analysis, we finally discovered that high expression of KRT6A protein was related to favourable prognosis in lung adenocarcinoma patients.

CONCLUSIONS

KRT6A protein inhibits the proliferation, migration and invasion abilities of lung adenocarcinoma cells, and high expression of KRT6A protein is a predictor of good prognosis in patients with lung adenocarcinoma.

Expression profiling revealed keratins and interleukins as potential biomarkers in squamous cell carcinoma of horn in Indian bullocks (Bos indicus)

Horn cancer is most prevalent in Bos indicus and poorly defined genetic landscape makes disease diagnosis and treatment difficult. In this study, RNA-Seq and data analysis using CLC Genomics Workbench was employed to identify biomarkers associated with horn cancer. As a result, a total of 149 genes were found significant differentially expressed in horn cancer samples compared to horn normal samples. The study revealed 'keratins' and 'interleukins' as apex groups of significant differentially expressed genes (DEGs). Functional analysis showed that the upregulated keratins support metastasis of tumor via cell proliferation, migration, and affecting cell stability, while downregulated interleukins along with other associated chemokine receptors deprive the immune response to tumor posing clear path for metastasis of horn cancer. Combi-action of both the group facilitates the tumor microenvironment to reproduce tumorigenesis. Analysis of pathways enriched in DEGs and exemplified protein-protein interaction network indicated actual role of DEGs in horn cancer at a fine level. Important effect of deregulated expression of keratin and interleukin genes in horn cancer enrolling their candidacy as potential biomarkers for horn cancer prognosis. This study appraises the possibility to mitigate horn cancer at fine resolution to extract attainable identification of prognostic molecular portraits.

Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma

Lung cancer is a highly prevalent type of cancer with a poor 5-year survival rate of about 4-17%. Eighty percent lung cancer belongs to non-small-cell lung cancer (NSCLC). For a long time, the treatment of NSCLC has been mostly guided by tumor stage, and there has been no significant difference between the therapy strategy of lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (SCLC), the two major subtypes of NSCLC. In recent years, important molecular differences between LUAD and SCLC are increasingly identified, indicating that targeted therapy will be more and more histologically specific in the future. To investigate the LUAD and SCLC difference on multi-omics scale, we analyzed the methylation and gene expression data together. With the Boruta method to remove irrelevant features and the MCFS (Monte Carlo Feature Selection) method to identify the significantly important features, we identified 113 key methylation features and 23 key gene expression features. HNF1B and TP63 were found to be dysfunctional on both methylation and gene expression levels. The experimentally determined interaction network suggested that TP63 may play an important role in connecting methylation genes and expression genes. Many of the discovered signature genes have been supported by literature. Our results may provide directions of precision diagnosis and therapy of LUAD and SCLC.

Expanding the Transcriptome of Head and Neck Squamous Cell Carcinoma Through Novel MicroRNA Discovery

Head and neck squamous cell carcinoma (HNSCC) has a poor survival rate mainly due to late stage diagnosis and recurrence. Despite genomic efforts to identify driver mutations and changes in protein-coding gene expression, developing effective diagnostic and prognostic biomarkers remains a priority to guide disease management and improve patient outcome. Recent reports of previously-unannotated microRNAs (miRNAs) from multiple somatic tissues have raised the possibility of HNSCC-specific miRNAs. In this study, we applied a customized in-silico analysis pipeline to identify novel miRNAs from raw small-RNA sequencing datasets from public repositories. We discovered 146 previously-unannotated sequences expressed in head and neck samples that share structural properties highly characteristic of miRNAs. The combined expression of the novel miRNAs revealed tissue and context-specific patterns. Furthermore, comparison of tumor with non-malignant tissue samples (n = 43 pairs) revealed 135 of these miRNAs as differentially expressed, most of which were overexpressed or exclusively found in tumor samples. Additionally, a subset of novel miRNAs was significantly associated with HPV infection status and patient outcome. A prognostic-model combining novel and known miRNA was developed (multivariate Cox regression analysis) leading to an improved death and relapse risk stratification (log rank p < 1e-7). The presence of these miRNAs was corroborated both in an independent dataset and by RT-qPCR analysis, supporting their potential involvement in HNSCC. In this study, we report the discovery of 146 novel miRNAs in head and neck tissues and demonstrate their potential biological significance and clinical relevance to head and neck cancer, providing a new resource for the study of HNSCC.

High expression of keratin 6C is associated with poor prognosis and accelerates cancer proliferation and migration by modulating epithelial-mesenchymal transition in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a more frequent subtype of lung cancer and most cases are discovered in the late stages. The proliferation and metastasis of LUAD are pivotal for disease progression. Despite unremitting deeper understanding of LUAD biology, the mechanisms involved in the proliferation and metastasis of LUAD remain unclear. The objective of our article was to inquiry the expression and the function of keratin 6C (KRT6C) in LUAD cells.

METHODS

First, the expression level and prognostic value of KRT6C in LUAD tissues were analyzed on the basis of the data acquired from TCGA database. Through qRT-PCR, the expression level of KRT6C on LUAD cell lines (A549, H1299, PC-9) and human normal lung cell line MRC-5 was tested. After that, CCK8 and colony formation assays was utilized to detect cell proliferation. In addition, to explore the influence of KRT6C on LUAD migration and invasion ability, scratch wound healing and transwell assays were utilized. Through western blotting, the protein expression levels of KRT6C, PCNA, E-cadherin, N-cadherin, Snail and Vimentin were detected.

RESULTS

The outcomes revealed that KRT6C was highly expressed in LUAD tissues and cell lines. Besides, elevated level of KRT6C was related to worse prognosis in LUAD patients. Ablation of KRT6C restrained proliferation, migration and invasion of A549 cells. KRT6C deficiency augmented the expression of E-cadherin as well as reduced the expression of N-cadherin, Snail and Vimentin.

CONCLUSION

Above all, these consequences indicated that depletion of KRT6C suppressed A549 cell proliferation, migration and invasion, which might be achieved by regulating EMT. In general, KRT6C is identified as a potential therapeutic target for LUAD.

Keratin 6A gene silencing suppresses cell invasion and metastasis of nasopharyngeal carcinoma via the β‑catenin cascade

Nasopharyngeal carcinoma (NPC) is a type of head and neck cancer. This study aimed to study the mechanisms of ectopic keratin 6A (KRT6A) in NPC. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting were performed to detect KRT6A levels in NPC cell lines (C666-1, 5-8F and SUNE-1) and a nasopharyngeal epithelial cell line (NP69, as a control). After SUNE-1 NPC cells had been silenced by KRT6A, cell viability, metastasis and invasion were determined using Cell Counting Kit-8, wound healing and Transwell assays, respectively. KRT6A levels, metastasis-associated factors and the Wnt/β-catenin pathway were measured using RT-qPCR and western blotting. It was demonstrated that KRT6A was upregulated in all detected NPC cells, among which KRT6A was the highest in SUNE-1 cells. In SUNE-1 cells, cell viability was inhibited at 24 and 48 h, and that cell metastasis and invasion were demonstrated to be suppressed by KRT6A silencing. Both the mRNA and protein levels of KRT6A, matrix metalloproteinase (MMP)-2, MMP-9, β-catenin, lymphoid enhancer binding factor 1 and T-cell specific factor 4 were reduced in the small interfering (si)KRT6A group. However, the results demonstrated that the levels of epithelial-cadherin and tissue inhibitor of metalloproteinase-2 (TIMP-2) were promoted in the siKRT6A group. The activation of the Wnt/β-catenin pathway by lithium chloride reversed the effect of si-KRT6A by modulating the expression of MMP-2/9 and TIMP2. It was observed that KRT6A silencing suppressed cell invasion and metastasis of NPC via the β-catenin cascade. Together these results provide important insights into a novel approach for the diagnosis and treatment of NPC.

Exhaled breath condensate biomarkers for the early diagnosis of lung cancer using proteomics

We explored whether the proteomic analysis of exhaled breath condensate (EBC) may provide biomarkers for noninvasive screening for the early detection of lung cancer (LC). EBC was collected from 192 individuals [49 control (C), 49 risk factor-smoking (S), 46 chronic obstructive pulmonary disease (COPD) and 48 LC]. With the use of liquid chromatography and tandem mass spectrometry, 348 different proteins with a different pattern among the four groups were identified in EBC samples. Significantly more proteins were identified in the EBC from LC compared with other groups (C: 12.4 ± 1.3; S: 15.3 ± 1; COPD: 14 ± 1.6; LC: 24.2 ± 3.6; P = 0.0001). Furthermore, the average number of proteins identified per sample was significantly higher in LC patients, and receiver operating characteristic curve (ROC) analysis showed an area under the curve of 0.8, indicating diagnostic value. Proteins frequently detected in EBC, such as dermcidin and hornerin, along with others much less frequently detected, such as hemoglobin and histones, were identified. Cytokeratins (KRTs) were the most abundant proteins in EBC samples, and levels of KRT6A, KRT6B, and KRT6C isoforms were significantly higher in samples from LC patients ( P = 0.0031, 0.0011, and 0.0009, respectively). Moreover, the amount of most KRTs in EBC samples from LC patients showed a significant positive correlation with tumor size. Finally, we used a random forest algorithm to generate a robust model using EBC protein data for the diagnosis of patients with LC where the area under the ROC curve obtained indicated a good classification (82%). Thus this study demonstrates that the proteomic analysis of EBC samples is an appropriated approach to develop biomarkers for the diagnosis of lung cancer.

Eight potential biomarkers for distinguishing between lung adenocarcinoma and squamous cell carcinoma

Lung adenocarcinoma (LADC) and squamous cell carcinoma (LSCC) are the most common non-small cell lung cancer histological phenotypes. Accurate diagnosis distinguishing between these two lung cancer types has clinical significance. For this study, we analyzed four Gene Expression Omnibus (GEO) datasets (GSE28571, GSE37745, GSE43580, and GSE50081). We then imported the datasets into the Gene-Cloud of Biotechnology Information online platform to identify genes differentially expressed in LADC and LSCC. We identified DSG3 (desmoglein 3), KRT5 (keratin 5), KRT6A (keratin 6A), KRT6B (keratin 6B), NKX2-1 (NK2 homeobox 1), SFTA2 (surfactant associated 2), SFTA3 (surfactant associated 3), and TMC5 (transmembrane channel-like 5) as potential biomarkers for distinguishing between LADC and LSCC. Receiver operating characteristic curve analysis suggested that KRT5 had the highest diagnostic value for discriminating between these two cancer types. Using the PrognoScan online survival analysis tool and the Kaplan-Meier Plotter, we found that high KRT6A or KRT6B levels, or low NKX2-1, SFTA3, or TMC5 levels correlated with unfavorable prognoses in LADC patients. Further studies will be needed to verify our findings in additional patient samples, and to elucidate the mechanisms of action of these potential biomarkers in non-small cell lung cancer.

Combined analysis identifies six genes correlated with augmented malignancy from non-small cell to small cell lung cancer

With increased malignancy, lung cancer can be classified into adenocarcinoma (ADC), squamous cell carcinoma (SQC), large cell carcinoma (LCC), and the small cell subtype (SCLC); yet, elucidations to this augmented malignancy has not been addressed. In this study, we elucidated the molecular diversity among these subtypes by investigating large-scale sequencing datasets. Among genes upregulated from normal, ADC, SQC, LCC to SCLC, six hub genes were found closely correlated with adverse clinical outcome and were testified on cellular or tissue level with quantitative RT-PCR. Cox regression model was then built to generate a risk signature. The possible linkages among these genes were also explored. Transcript levels of BUB1, E2F1, ESPL1, GTSE1, RAB3B, and U2AF2 were found significantly elevated from normal, ADC, SQC, LCC to SCLC. Overexpression of one or multiple of these genes was correlated with adverse overall survival (OS) and relapse-free survival (RFS) in the whole patient cohort or groups stratified according to clinical variables, while most of all six genes were independent prognostic factors. When used as a six-gene risk signature, patients with high signature score displayed more unfavorable clinical variables and poorer outcome. Tight regulative relationships were found within these genes, while BUB1 and E2F1 were likely to be the drivers. We considered the augmented malignancy from non-small cell lung cancer (NSCLC) to SCLC might be due to the elevation of these six genes. We believe these genes were powerful cancer prognostic markers and potential therapeutic targets in lung cancer; moreover, changes of their level might be correlated with lung cancer phenotype plasticity.

The Major Prognostic Features of Nuclear Receptor NR5A2 in Infiltrating Ductal Breast Carcinomas

Background. Gene expression profiles of 181 breast cancer samples were analyzed to identify prognostic features of nuclear receptors NR5A1 and NR5A2 based upon their associated transcriptional networks. Methods. A supervised network analysis approach was used to build the NR5A-mediated transcriptional regulatory network. Other bioinformatic tools and statistical methods were utilized to confirm and extend results from the network analysis methodology. Results. NR5A2 expression is a negative factor in breast cancer prognosis in both ER(-) and ER(-)/ER(+) mixed cohorts. The clinical and cohort significance of NR5A2-mediated transcriptional activities indicates that it may have a significant role in attenuating grade development and cancer related signal transduction pathways. NR5A2 signature that conditions poor prognosis was identified based upon results from 15 distinct probes. Alternatively, the expression of NR5A1 predicts favorable prognosis when concurrent NR5A2 expression is low. A favorable signature of eight transcription factors mediated by NR5A1 was also identified. Conclusions. Correlation of poor prognosis and NR5A2 activity is identified by NR5A2-mediated 15-gene signature. NR5A2 may be a potential drug target for treating a subset of breast cancer tumors across breast cancer subtypes, especially ER(-) breast tumors. The favorable prognostic feature of NR5A1 is predicted by NR5A1-mediated 8-gene signature.

Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases

Identification and validation of interaction networks and network biomarkers have become more critical and important in the development of disease-specific biomarkers, which are functionally changed during disease development, progression or treatment. The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB). Disease-specific interaction networks, network biomarkers, or DNB have great significance in the understanding of molecular pathogenesis, risk assessment, disease classification and monitoring, or evaluations of therapeutic responses and toxicities. Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients. Clinical bioinformatics should be a key approach to the identification and validation of disease-specific biomarkers.

Identification of logic relationships between genes and subtypes of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) has two major subtypes: adenocarcinoma (AC) and squamous cell carcinoma (SCC). The diagnosis and treatment of NSCLC are hindered by the limited knowledge about the pathogenesis mechanisms of subtypes of NSCLC. It is necessary to research the molecular mechanisms related with AC and SCC. In this work, we improved the logic analysis algorithm to mine the sufficient and necessary conditions for the presence states (presence or absence) of phenotypes. We applied our method to AC and SCC specimens, and identified lower and higher logic relationships between genes and two subtypes of NSCLC. The discovered relationships were independent of specimens selected, and their significance was validated by statistic test. Compared with the two earlier methods (the non-negative matrix factorization method and the relevance analysis method), the current method outperformed these methods in the recall rate and classification accuracy on NSCLC and normal specimens. We obtained biomarkers. Among biomarkers, genes have been used to distinguish AC from SCC in practice, and other six genes were newly discovered biomarkers for distinguishing subtypes. Furthermore, NKX2-1 has been considered as a molecular target for the targeted therapy of AC, and other genes may be novel molecular targets. By gene ontology analysis, we found that two biological processes (‘epidermis development’ and ‘cell adhesion’) were closely related with the tumorigenesis of subtypes of NSCLC. More generally, the current method could be extended to other complex diseases for distinguishing subtypes and detecting the molecular targets for targeted therapy.

MicroRNA-dependent regulation of transcription in non-small cell lung cancer

Squamous cell lung cancer (SCC) and adenocarcinoma are the most common histological subtypes of non-small cell lung cancer (NSCLC), and have been traditionally managed in the clinic as a single entity. Increasing evidence, however, illustrates the biological diversity of these two histological subgroups of lung cancer, and supports the need to improve our understanding of the molecular basis beyond the different phenotypes if we aim to develop more specific and individualized targeted therapy. The purpose of this study was to identify microRNA (miRNA)-dependent transcriptional regulation differences between SCC and adenocarcinoma histological lung cancer subtypes. In this work, paired miRNA (667 miRNAs by TaqMan Low Density Arrays (TLDA)) and mRNA profiling (Whole Genome 44 K array G112A, Agilent) was performed in tumor samples of 44 NSCLC patients. Nine miRNAs and 56 mRNAs were found to be differentially expressed in SCC versus adenocarcinoma samples. Eleven of these 56 mRNA were predicted as targets of the miRNAs identified to be differently expressed in these two histological conditions. Of them, 6 miRNAs (miR-149, miR-205, miR-375, miR-378, miR-422a and miR-708) and 9 target genes (CEACAM6, CGN, CLDN3, ABCC3, MLPH, ACSL5, TMEM45B, MUC1) were validated by quantitative PCR in an independent cohort of 41 lung cancer patients. Furthermore, the inverse correlation between mRNAs and microRNAs expression was also validated. These results suggest miRNA-dependent transcriptional regulation differences play an important role in determining key hallmarks of NSCLC, and may provide new biomarkers for personalized treatment strategies.

Comprehensive transcriptome profiling of squamous cell carcinoma of horn in Bos indicus

Squamous cell carcinoma (SCC) of horn is frequently observed in Bos indicus affecting 1% of cattle population and accounting 83.34% of total tumours found. The transcriptome profile of horn cancer (HC) tissue and the matched normal (HN) tissue were analysed by RNA-seq using Roche 454 sequencing. A total of 1 504 900 reads comprising of 612 MB data were used to identify differentially expressed genes using CLC Genomic Workbench. These include up-regulation of KRT6A, KRT6B, KRT6C, KRT14, SFN, KRT84, PI3, COL17A1, ANLN, SERPINB5 and down-regulation of BOLA, SCGB1A1, CXCL17, KRT19, BPIFB1, NR4A1 and TFF3 in HC, which are involved in regulation of gene transcription, cell proliferation, apoptosis, cell survival and metabolic pathways. The qPCR analysis of several targets suggested concordance of gene expression profile with RNA-seq analysis. The present findings would provide basis for further screening of genes and identification of markers for early diagnosis and therapeutic intervention of HC.

Transcriptome analysis and SNP identification in SCC of horn in (Bos indicus) Indian cattle

Single Nucleotide Polymorphisms (SNPs) have become the marker of choice for genome wide association studies. In order to provide the best genome coverage for the analysis of disease, production and performance traits, a large number of relatively evenly distributed SNPs are needed. The main objective of present work was to identify large numbers of gene-associated SNPs using high-throughput sequencing in squamous cell carcinoma of horn. RNA-seq analysis was conducted on 2 tissues viz. Horn Cancer (HC) and Horn Normal (HN) in Kankrej breed of cattle. A total of 909,362 reads with average read length of 405 bp for HC and 583,491 reads with average read length of 411 bp for HN were obtained. We found 9532 and 7065 SNPs as well as 1771 and 1172 Indels in HC and HN, respectively, from which, 7889 SNPs and 1736 Indels were uniquely present in HC, 5886 SNPs and 1146 Indels were uniquely present in HN and reported first time in Bos indicus, whereas the rest are already reported in Bos taurus dbSNP database. The gene-associated SNPs and Indels were high in upregulated genes of HC as compared to HN. Analysis of differentially expressed genes was identified, these genes are involved in regulation of cell proliferation, apoptosis, gene transcription, cell survival and metabolism through various metabolic pathways. The result of transcriptome expression profiling was validated using Real Time quantitative PCR in nine randomly selected genes. We identified numbers aberrant signaling pathways responsible for carcinogenesis in HC which are also commonly altered in squamous cell carcinoma (SCC) of lung in human being. We conclude that a large number of altered genes and dysfunction of multiple pathways are involved in the development of Horn Cancer. The present findings contribute to theoretical information for further screening of genes and identification of markers for early diagnosis of HC as well as SNPs identified in this report provide a much needed resource for genetic studies in B. indicus and shall contribute to the development of a high density SNP array. Validation and testing of these SNPs using SNP arrays will form the material basis for gene associated SNPs in HC.

Transcriptome alterations induced by cigarette smoke

Cigarette smoke alters the transcriptome of multiple tissues; those directly exposed to toxic products and those exposed to circulating components and metabolic products of tobacco smoke. In most tissues and organs that have been studied, the smoking transcriptome is characterized by increased expression of antioxidant and xenobiotic genes as well as a wide spectrum of inflammation-related genes, and potential oncogenic genes. Smoking is associated with an increased incidence of cancer in a number of organs both those directly exposed (lungs and airways) and those indirectly exposed (bladder, liver, pancreas). Individual transcriptomic responses vary, based to some degree on as yet to be clarified genetic factors, and likely how and what the individual has smoked. The complexity of individual responses to tobacco exposure and of smoking-related cancers in various organs is beginning to be revealed in transcriptomic and whole genome sequencing studies, of both tumors and cytologically normal appearing cells that have been exposed to cigarette smoke or its products creating a genomic "field of injury." The recent application of next generation sequencing to defining the transcriptome alterations induced by cigarette smoke holds the promise of discovering new approaches to personalized prevention and treatment of smoking-related lung diseases in the future.

Phenotype prediction by integrative network analysis of SNP and gene expression microarrays

A long-term goal of biomedical research is to decipher how genetic processes influence disease formation. Ubiquitous and advancing microarray technology can measure millions of DNA structural variants (single-nucleotide polymorphisms, or SNPs) and thousands of gene transcripts (RNA expression microarrays) in cells. Both of these information modalities can be brought to bear on disease etiology. This paper develops a Bayesian network-based approach to integrate SNP and expression microarray data. The network models SNP-gene interactions using a phenotype-centric network. Inferring the network consists of two steps: variable selection and network learning. The learned network illustrates how functionally dependent SNPs and genes influence each other, and also serves as a predictor of the phenotype. The application of the proposed method to a pediatric acute lymphoblastic leukemia dataset demonstrates the feasibility of our approach and its impact on biological investigation and clinical practice.

Major Functional Transcriptome of an Inferred Center Regulator of an ER(-) Breast Cancer Model System

We aimed to find clinically relevant gene activities ruled by the signal transducer and activator of transcription 3 (STAT3) proteins in an ER(-) breast cancer population via network approach. STAT3 is negatively associated with both lymph nodal category and stage. MYC is a component of STAT3 network. MYC and STAT3 may co-regulate gene expressions for Warburg effect, stem cell like phenotype, cell proliferation and angiogenesis. We identified a STAT3 network in silico showing its ability in predicting its target gene expressions primarily for specific tumor subtype, tumor progression, treatment options and prognostic features. The aberrant expressions of MYC and STAT3 are enriched in triple negatives (TN). They promote histological grade, vascularity, metastasis and tumor anti-apoptotic activities. VEGFA, STAT3, FOXM1 and METAP2 are druggable targets. High levels of METAP2, MMP7, IGF2 and IGF2R are unfavorable prognostic factors. STAT3 is an inferred center regulator at early cancer development predominantly in TN.