An in silico analytical study of lung cancer and smokers datasets from gene expression omnibus (GEO) for prediction of differentially expressed genes

The Molecular Landscape of Lung Metastasis in Primary Head and Neck Squamous Cell Carcinomas

Background Lung metastasis in head and neck cancer (HNC) patients is a critical concern, often indicating an advanced disease stage and a poor prognosis. This study explores the molecular complexities of such metastases, identifying specific genes and pathways that may serve as valuable targets for diagnosis and treatment. The findings underscore the potential for significantly improved patient outcomes through targeted therapeutic strategies. Methodology In this research, we systematically collected raw gene expression data from head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LSCC). By comparing tumorous and normal gene expression profiles from paired patient samples, we identified differentially expressed genes (DEGs). Network analysis helped visualize protein interactions and pinpoint crucial hub genes. Through validation and comparison across several datasets, we identified common DEGs. Additionally, we employed Kaplan-Meier analysis and log-rank testing to examine the relationship between gene expression patterns and patient survival. Result The study identified 145 overlapping DEGs in both HNSCC and LSCC, which are crucial for cancer progression and linked to lung metastasis, offering vital targets for personalized therapy by identifying key genes affecting disease development and patient survival. Pathway analyses linked these to lung metastasis, while protein-protein interaction network construction and hub gene identification highlighted genes crucial for development and patient survival, offering targets for personalized therapy. Conclusion Identifying key genes and pathways in lung metastasis from HNC, this study highlights potential targets for enhanced diagnosis and therapy. It underscores the crucial role of molecular insights in driving forward personalized treatment approaches and improving patient outcomes.

A novel role of ADGRF1 (GPR110) in promoting cellular quiescence and chemoresistance in human epidermal growth factor receptor 2-positive breast cancer

While G protein-coupled receptors (GPCRs) are known to be excellent drug targets, the second largest family of adhesion-GPCRs is less explored for their role in health and disease. ADGRF1 (GPR110) is an adhesion-GPCR and has an important function in neurodevelopment and cancer. Despite serving as a poor predictor of survival, ADGRF1's coupling to G proteins and downstream pathways remain unknown in cancer. We evaluated the effects of ADGRF1 overexpression on tumorigenesis and signaling pathways using two human epidermal growth factor receptor-2-positive (HER2+) breast cancer (BC) cell-line models. We also interrogated publicly available clinical datasets to determine the expression of ADGRF1 in various BC subtypes and its impact on BC-specific survival (BCSS) and overall survival (OS) in patients. ADGRF1 overexpression in HER2+ BC cells increased secondary mammosphere formation, soft agar colony formation, and % of Aldefluor-positive tumorigenic population in vitro and promoted tumor growth in vivo. ADGRF1 co-immunoprecipitated with both Gαs and Gαq proteins and increased cAMP and IP1 when overexpressed. However, inhibition of only the Gαs pathway by SQ22536 reversed the pro-tumorigenic effects of ADGRF1 overexpression. RNA-sequencing and RPPA analysis revealed inhibition of cell cycle pathways with ADGRF1 overexpression, suggesting cellular quiescence, as also evidenced by cell cycle arrest at the G0/1 phase and resistance to chemotherapy in HER2+ BC. ADGRF1 was significantly overexpressed in the HER2-enriched BC compared to luminal A and B subtypes and predicted worse BCSS and OS in these patients. Therefore, ADGRF1 represents a novel drug target in HER2+ BC, warranting discovery of novel ADGRF1 antagonists.

Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer's Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.

Transcriptomic Analysis of Laser Capture Microdissected Tumors Reveals Cancer- and Stromal-Specific Molecular Subtypes of Pancreatic Ductal Adenocarcinoma

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) lethality is multifactorial; although studies have identified transcriptional and genetic subsets of tumors with different prognostic significance, there is limited understanding of features associated with the minority of patients who have durable remission after surgical resection. In this study, we performed laser capture microdissection (LCM) of PDAC samples to define their cancer- and stroma-specific molecular subtypes and identify a prognostic gene expression signature for short-term and long-term survival.

EXPERIMENTAL DESIGN

LCM and RNA sequencing (RNA-seq) analysis of cancer and adjacent stroma of 19 treatment-naïve PDAC tumors was performed. Gene expression signatures were tested for their robustness in a large independent validation set. An RNA-ISH assay with pooled probes for genes associated with disease-free survival (DFS) was developed to probe 111 PDAC tumor samples.

RESULTS

Gene expression profiling identified four subtypes of cancer cells (C1-C4) and three subtypes of cancer-adjacent stroma (S1-S3). These stroma-specific subtypes were associated with DFS (P = 5.55E-07), with S1 associated with better prognoses when paired with C1 and C2. Thirteen genes were found to be predominantly expressed in cancer cells and corresponded with DFS in a validation using existing RNA-seq datasets. A second validation on an independent cohort of patients using RNA-ISH probes to six of these prognostic genes demonstrated significant association with overall survival (median 17 vs. 25 months; P < 0.02).

CONCLUSIONS

Our results identified specific signatures from the epithelial and the stroma components of PDAC, which add clarity to the nature of PDAC molecular subtypes and may help predict survival.

RNA Sequencing Analyses Reveal the Potential Mechanism of Pulmonary Injury Induced by Gallium Arsenide Particles in Human Bronchial Epithelioid Cells

Extensive use of gallium arsenide (GaAs) has led to increased exposure to humans working in the semiconductor industry. This study employed physicochemical characterization of GaAs obtained from a workplace, cytotoxicity analysis of damage induced by GaAs in 16HBE cells, RNA-seq and related bioinformatic analysis, qRT-PCR verification and survival analysis to comprehensively understand the potential mechanism leading to lung toxicity induced by GaAs. We found that GaAs-induced abnormal gene expression was mainly related to the cellular response to chemical stimuli, the regulation of signalling, cell differentiation and the cell cycle, which are involved in transcriptional misregulation in cancer, the MAPK signalling pathway, the TGF-β signalling pathway and pulmonary disease-related pathways. Ten upregulated genes (FOS, JUN, HSP90AA1, CDKN1A, ESR1, MYC, RAC1, CTNNB1, MAPK8 and FOXO1) and 7 downregulated genes (TP53, AKT1, NFKB1, SMAD3, CDK1, E2F1 and PLK1) related to GaAs-induced pulmonary toxicity were identified. High expression of HSP90AA1, RAC1 and CDKN1A was significantly associated with a lower rate of overall survival in lung cancers. The results of this study indicate that GaAs-associated toxicities affected the misregulation of oncogenes and tumour suppressing genes, activation of the TGF-β/MAPK pathway, and regulation of cell differentiation and the cell cycle. These results help to elucidate the molecular mechanism underlying GaAs-induced pulmonary injury.

The Emerging Role of Adhesion GPCRs in Cancer

Aberrant expression, function, and mutation of G protein-coupled receptors (GPCRs) and their signaling partners, G proteins, have been well documented in many forms of cancer. These cell surface receptors and their endogenous ligands are implicated in all aspects of cancer including proliferation, angiogenesis, invasion, and metastasis. Adhesion GPCRs (aGPCRs) form the second largest family of GPCRs, most of which are orphan receptors with unknown physiological functions. This is mainly due to our limited insight into their structure, natural ligands, signaling pathways, and tissue expression profiles. Nevertheless, recent studies show that aGPCRs play important roles in cell adhesion to the extracellular matrix and cell-cell communication, processes that are dysregulated in cancer. Emerging evidence suggests that aGPCRs are implicated in migration, proliferation, and survival of tumor cells. We here review the role of aGPCRs in the five most common types of cancer (lung, breast, colorectal, prostate, and gastric) and emphasize the importance of further translational studies in this field.

Towards the early detection of ductal carcinoma (a common type of breast cancer) using biomarkers linked to the PPAR(γ) signaling pathway

Breast cancer is a leading cause of morbidity and mortality among women comprising about 12% females worldwide. The underlying alteration in the gene expression, molecular mechanism and metabolic pathways responsible for incidence and progression of breast tumorigenesis are yet not completely understood. In the present study, potential biomarker genes involved in the early progression for early diagnosis of breast cancer has been detailed. Regulation and Gene profiling of Ductal Carcinoma In-situ (DCIS), Invasive Ductal Carcinoma (IDC) and healthy samples have been analyzed to follow their expression pattern employing normalization, statistical calculation, DEGs annotation and Protein-Protein Interaction (PPI) network. We have performed a comparative study on differentially expressed genes among Healthy vs DCIS, Healthy vsIDC and DCIS vs IDC. We found MCM102 and SLC12A8as consistently over-expressed and LEP, SORBS1, SFRP1, PLIN1, FABP4, RBP4, CD300LG, ID4, CRYAB, ECRG4, G0S2, FMO2, ADAMTS5, CAV1, CAV2, ABCA8, MAMDC2, IGFBP6, CLDN11, TGFBR3as under-expressed genes in all the 3 conditions categorized for pre-invasive and invasive ductal breast carcinoma. These genes were further studied for the active pathways where PPAR(γ) signaling pathway was found to be significantly involved. The gene expression profile database can be a potential tool in the early diagnosis of breast cancer.

Investigating Cell Signaling with Gene Expression Datasets

Modern molecular biology is a data- and computationally-intensive field with few instructional resources for introducing undergraduate students to the requisite skills and techniques for analyzing large data sets. This Lesson helps students: (i) build an understanding of the role of signal transduction in the control of gene expression; (ii) improve written scientific communication skills through engagement in literature searches, data analysis, and writing reports; and (iii) develop an awareness of the procedures and protocols for analyzing and making inferences from high-content quantitative molecular biology data. The Lesson is most suited to upper level biology courses because it requires foundational knowledge on cellular organization, protein structure and function, and the tenets of information flow from DNA to proteins. The first step lays the foundation for understanding cell signaling, which can be accomplished through assigned readings and presentations. In subsequent active learning sessions, data analysis is integrated with exercises that provide insight into the structure of scientific papers. The Lesson emphasizes the role of quantitative methods in research and helps students gain experience with functional genomics databases and data analysis, which are important skills for molecular biologists. Assessment is conducted through mini-reports designed to gauge students' perceptions of the purpose of each step, their awareness of the possible limitations of the methods utilized, and the ability to identify opportunities for further investigation. Summative assessment is conducted through a final report. The modules are suitable for complementing wet-laboratory experiments and can be adapted for different courses that use molecular biology data.

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps

BACKGROUND

For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure.

RESULTS

Here, for rare samples of gene expression data sets, we use MG-PCC (mini-groups that are defined by PCC) algorithm to divide them into mini-groups, and use t-SNE-SSP maps to display these mini-groups, where the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups, t-SNE-SSP map is selected from a series of t-SNE(t-statistic Stochastic Neighbor Embedding) maps of standardized samples, and these t-SNE maps have different perplexity parameter. Moreover, for PCC clusters of mass genes, they are displayed by t-SNE-SGI map, where t-SNE-SGI map is selected from a series of t-SNE maps of standardized genes, and these t-SNE maps have different initialization dimensions. Here, t-SNE-SSP and t-SNE-SGI maps are selected by A-value, where A-value is modeled from areas of clustering projections, and t-SNE-SSP and t-SNE-SGI maps are such t-SNE map that has the smallest A-value.

CONCLUSIONS

From the analysis of cancer gene expression data sets, we demonstrate that MG-PCC algorithm is able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP(or t-SNE-SGI) maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Furthermore, t-SNE-SS(m)(or t-SNE-SG(n)) maps are able to construct independent tree diagrams of the nearest sample(or gene) neighbors, where each tree diagram is corresponding to a mini-group of samples(or genes).

Bioinformatic analysis of differential expression and core GENEs in breast cancer

Breast cancer (BRCA) is one of the most common malignancies in women. The gene expression profile of GSE103512 from the GEO database was downloaded in order to find key genes involved in the occurrence and development of BRCA. 75 samples, including 65 cancer and 10 normal samples, were included in this analysis. Differentially expressed genes (DEGs) between BRCA patients and health people were chosen using R tool. We next performed gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) was utilized to visualize protein-protein interaction (PPI) of these DEGs. The related genes and medicines specific to hub genes were predicted by CBioportal. We screened a total of 357 DEGs including 77 up-regulated and 280 down-regulated. A series of BRCA related GO terms and pathways were identified by analysis of these DEGs. Insulin-like growth factor 1 (IGF1); epidermal growth factor receptor (EGFR); v-jun avian sarcoma virus 17 oncogene homolog (JUN) and Estrogen Receptor 1 (ESR1) of the DEGs were screened by construction of the PPI network and the degree of connectivity. IGF1 and ESR1 were finally selected as potential hub genes and treatment targets of BRCA. In conclusion, this bioinformatics analysis demonstrated that DEGs and hub genes, such as IGF1, might regulate the development of gastric cancer. These DEGs could be used as new biomarkers for diagnosis and to guide the combination medicine of BRCA.

Polymorphisms in genes related to epithelial-mesenchymal transition and risk of non-small cell lung cancer

The epithelial-mesenchymal transition (EMT) process is a crucial step for tumor invasion and metastasis. Previous research investigating EMT has mostly focused on its role in cancer progression. Recent studies showed that EMT and EMT-driving transcription factor (EMT-TF) expression are early events in lung cancer pathogenesis, implying a potential association between EMT and lung cancer risk. In this study, we examined whether genetic variants in EMT-related genes are associated with risk of non-small cell lung cancer (NSCLC). We used data from a genome-wide association study of 1482 NSCLC cases and 1544 healthy controls as the discovery phase, in which we analyzed 1602 single-nucleotide polymorphisms (SNPs) within 159 EMT-related genes. We then validated the significant SNPs in another 5699 cases and 5815 controls from the National Cancer Institute lung cancer genome-wide association study. Cumulative effects were evaluated for validated SNPs, and a gene-based test was performed to explore gene-level association with disease risk. In the discovery phase, 174 SNPs demonstrated significant associations with NSCLC risk. In the validation phase, seven SNPs mapped to EGFR, NOTCH3, ADGRF1 and SMAD3 were confirmed. Cumulative effect analysis of the significant SNPs demonstrated increasing risk with the number of unfavorable genotypes in the discovery and validation datasets. Gene-based analysis implicated ADGRF1, NOTCH3 and CDH1 as significant for NSCLC risk. Functional prediction revealed several potential mechanisms underlying these associations. Our results suggest that EMT-related gene variants may be involved in susceptibility to NSCLC; if confirmed, they might help identify higher-risk individuals.

Cost-effectiveness of lung cancer screening and treatment methods: a systematic review of systematic reviews

BACKGROUND

Due to extensive literature in the field of lung cancer and their heterogeneous results, the aim of this study was to systematically review of systematic reviews studies which reviewed the cost-effectiveness of various lung cancer screening and treatment methods.

METHODS

In this systematic review of systematic reviews study, required data were collected searching the following key words which selected from Mesh: "lung cancer", "lung oncology", "lung Carcinoma", "lung neoplasm", "lung tumors", "cost- effectiveness", "systematic review" and "Meta-analysis". The following databases were searched: PubMed, Cochrane Library electronic databases, Google Scholar, and Scopus. Two reviewers (RA and A-AS) evaluated the articles according to the checklist of "assessment of multiple systematic reviews" (AMSTAR) tool.

RESULTS

Overall, information of 110 papers was discussed in eight systematic reviews. Authors focused on cost-effectiveness of lung cancer treatments in five systematic reviews. Targeted therapy options (bevacizumab, Erlotinib and Crizotinib) show an acceptable cost-effectiveness. Results of three studies failed to show cost-effectiveness of screening methods. None of the studies had used the meta-analysis method. The Quality of Health Economic Studies (QHES) tool and Drummond checklist were mostly used in assessing the quality of articles. Most perspective was related to the Payer (64 times) and the lowest was related to Social (11times). Most cases referred to Incremental analysis (82%) and also the lowest point of referral was related to Discounting (in 49% of the cases). The average quality score of included studies was calculated 9.2% from 11.

CONCLUSIONS

Targeted therapy can be an option for the treatment of lung cancer. Evaluation of the cost-effectiveness of computerized tomographic colonography (CTC) in lung cancer screening is recommended. The perspective of the community should be more taken into consideration in studies of cost-effectiveness. Paying more attention to the topic of Discounting will be necessary in the studies.

Influence of PCOS in Obese vs. Non-Obese women from Mesenchymal Progenitors Stem Cells and Other Endometrial Cells: An in silico biomarker discovery

Polycystic ovary syndrome (PCOS) is endocrine system disease which affect women ages 18 to 44 where the women's hormones are imbalance. Recently it has been reported to occur in early age. Alteration of normal gene expression in PCOS has shown negative effects on long-term health issues. PCOS has been the responsible factor for the infertility in women of reproductive age group. Early diagnosis and treatment can improve the women's health suffering from PCOS. Earlier Studies shows correlation of PCOS upon insulin resistance with significant outcome, Current study shows the linkage between PCOS with obesity and non-obese patients. Gene expression datasets has been downloaded from GEO (control and PCOS affected patients). Normalization of the datasets were performed using R based on RMA and differentially expressed gene (DEG) were selected on the basis of p-value 0.05 followed by functional annotation of selected gene using Enrich R and DAVID. The DEGs were significantly related to PCOS with obesity and other risk factors involved in disease. The Gene Enrichment Analysis suggests alteration of genes and associated pathway in case of obesity. Current study provides a productive groundwork for specific biomarkers identification for the accurate diagnosis and efficient target for the treatment of PCOS.

Adhesion GPCR Function in Pulmonary Development and Disease

Classic G-protein-coupled receptors (GPCRs) control multiple aspects of pulmonary physiology as demonstrated by loss-of-function experiments in mice and pharmacologic targeting of GPCRs for treatment of several pulmonary diseases. Emerging data demonstrate critical roles for members of the adhesion GPCR (aGPCR) family in pulmonary development, homeostasis, and disease. Although this field is still in its infancy, this chapter will review all available data regarding aGPCRs in pulmonary biology, with a particular focus on the aGPCR for which the most substantial data to date exist: Adgrf5.