Landscape of transcriptional deregulation in lung cancer

Curcumin Inhibits the Progression of Non-small Cell Lung Cancer by Regulating DMRT3/SLC7A11 Axis

Non-small cell lung cancer (NSCLC) is a fatal malignancy all over the world. Emerging studies have shown that curcumin might repress NSCLC progression by regulating ferroptosis, but the underlying mechanism remains unclear. 16HBE, LK-2, and H1650 cell viability was detected using Cell Counting Kit-8 assay. LK-2 and H1650 cell proliferation, apoptosis, and angiopoiesis were measured using 5-ethynyl-2'-deoxyuridine, flow cytometry, and tube formation assay. Superoxide dismutase, Malondialdehyde, Glutathione, and lactate dehydrogenase levels in LK-2 and H1650 cells were examined using special assay kits. Fe+ level was assessed using an iron assay kit. Doublesex and Mab-3 related Transcription Factor 3 (DMRT3) and solute carrier family 7 member 11 (SLC7A11) protein levels were detected using western in NSCLC tissues, adjacent matched normal tissues, 16HBE cells, LK-2 cells, H1650 cells, and xenograft tumor tissues. Glutathione peroxidase 4, Acyl-CoA Synthetase Long Chain Family Member 4, and transferrin receptor 1 protein levels in LK-2 and H1650 cells were examined by western blot assay. DMRT3 and SLC7A11 levels were determined using real-time quantitative polymerase chain reaction. After JASPAR prediction, binding between DMRT3 and SLC7A11 promoter was verified using Chromatin immunoprecipitation and dual-luciferase reporter assays in LK-2 and H1650 cells. Role of curcumin on NSCLC tumor growth was assessed using the xenograft tumor model in vivo. Curcumin blocked NSCLC cell proliferation and angiopoiesis, and induced apoptosis and ferroptosis. DMRT3 or SLC7A11 upregulation partly abolished the suppressive role of curcumin on NSCLC development. In mechanism, DMRT3 was a transcription factor of SLC7A11 and increased the transcription of SLC7A11 via binding to its promoter region. Curcumin inhibited NSCLC growth in vivo by modulating DMRT3. Curcumin might constrain NSCLC cell malignant phenotypes partly through the DMRT3/SLC7A11 axis, providing a promising therapeutic strategy for NSCLC.

Cholangioblastic Cholangiocarcinoma ( NIPBL :: NACC1 Cholangiocarcinoma) : Expanded Morphologic Spectrum and Further Genetic Characterization

The cholangioblastic variant of intrahepatic cholangiocarcinoma is a distinctive neoplasm that typically affects young women without underlying liver disease. Morphologically, it demonstrates solid, trabecular, and tubulocystic architecture, biphasic small cell-large cell cytology, and immunoreactivity for inhibin, neuroendocrine markers, and biliary but not hepatocellular markers. In 2021, our group identified a characteristic NIPBL::NACC1 gene fusion in cholangioblastic cholangiocarcinoma, and since then ~20 genetically confirmed cases have been reported in the literature. We report 2 additional cases, both of which caused diagnostic challenges. The first was previously published as a "biliary adenofibroma with malignant features" which we now show recurred as a high-grade adenocarcinoma. Re-review of the original lesion demonstrated the morphologic and immunohistochemical features of highly cystic cholangioblastic cholangiocarcinoma, whereas the high-grade recurrence lacked many of these features. In addition to the characteristic NIPBL::NACC1 gene fusion, the recurrence demonstrated loss of the RB1 and PTEN genes which were found in the highly cystic, bland areas of the original tumor, suggesting that the recurrence was derived from this bland component. The second case was originally misclassified as metastatic well-differentiated neuroendocrine neoplasm and only focally demonstrated the characteristic biphasic small cell-large cell cytology. In addition, a review of 7 cholangioblastic cholangiocarcinomas in our files demonstrates that loss of chromosome 13q14.2 (where the RB1 gene resides) and loss of chromosome 6q15-q16.3 are recurrent secondary changes in these neoplasms. Expression profiling demonstrated alterations in the transforming growth factor receptor beta superfamily, and overexpression of MYC which was validated by immunohistochemistry. Our findings expand the morphologic and genetic spectrum of this neoplasm and provide insight into secondary genetic changes associated with progression.

Unveiling the powerhouse: ASCL1-driven small cell lung cancer is characterized by higher numbers of mitochondria and enhanced oxidative phosphorylation

BACKGROUND

Small cell lung cancer (SCLC) is an aggressive malignancy with distinct molecular subtypes defined by transcription factors and inflammatory characteristics. This follow-up study aimed to validate the unique metabolic phenotype in achaete-scute homologue 1 (ASCL1)-driven SCLC cell lines and human tumor tissue.

METHODS

Metabolic alterations were analyzed using proteomic data. Structural and functional differences of mitochondria were investigated using qPCR, flow cytometry, confocal imaging, and transmission electron microscopy and seahorse assays. Several metabolic inhibitors were tested using MTT-based and clonogenic assays. Single-cell enzyme activity assays were conducted on cell lines and tumor tissue samples of SCLC patients.

RESULTS

We found increased mitochondrial numbers correlating with higher oxidative phosphorylation activity in ASCL1-dominant cells compared to other SCLC subtypes. Metabolic inhibitors targeting mitochondrial respiratory complex-I or carnitine palmitoyltransferase 1 revealed higher responsiveness in SCLC-A. Conversely, we demonstrated that non-ASCL1-driven SCLCs with lower oxidative signatures show dependence on glutaminolysis as evidenced by the enhanced susceptibility to glutaminase inhibition. Accordingly, we detected increased glutamate-dehydrogenase activity in non-ASCL1-dominant cell lines as well as in human SCLC tissue samples.

CONCLUSIONS

Distinct SCLC subtypes exhibit unique metabolic vulnerabilities, suggesting potential for subtype-specific therapies targeting the respiratory chain, fatty acid transport, or glutaminolysis.

Global transcriptomic network analysis of the crosstalk between microbiota and cancer-related cells in the oral-gut-lung axis

Background

The diagnosis and treatment of lung, colon, and gastric cancer through the histologic characteristics and genomic biomarkers have not had a strong impact on the mortality rates of the top three global causes of death by cancer.

Methods

Twenty-five transcriptomic analyses (10 lung cancer, 10 gastric cancer, and 5 colon cancer datasets) followed our own bioinformatic pipeline based on the utilization of specialized libraries from the R language and DAVID´s gene enrichment analyses to identify a regulatory metafirm network of transcription factors and target genes common in every type of cancer, with experimental evidence that supports its relationship with the unlocking of cell phenotypic plasticity for the acquisition of the hallmarks of cancer during the tumoral process. The network's regulatory functional and signaling pathways might depend on the constant crosstalk with the microbiome network established in the oral-gut-lung axis.

Results

The global transcriptomic network analysis highlighted the impact of transcription factors (SOX4, TCF3, TEAD4, ETV4, and FOXM1) that might be related to stem cell programming and cancer progression through the regulation of the expression of genes, such as cancer-cell membrane receptors, that interact with several microorganisms, including human T-cell leukemia virus 1 (HTLV-1), the human papilloma virus (HPV), the Epstein-Barr virus (EBV), and SARS-CoV-2. These interactions can trigger the MAPK, non-canonical WNT, and IFN signaling pathways, which regulate key transcription factor overexpression during the establishment and progression of lung, colon, and gastric cancer, respectively, along with the formation of the microbiome network.

Conclusion

The global transcriptomic network analysis highlights the important interaction between key transcription factors in lung, colon, and gastric cancer, which regulates the expression of cancer-cell membrane receptors for the interaction with the microbiome network during the tumorigenic process.

m6A modification of lncRNA PHKA1-AS1 enhances Actinin Alpha 4 stability and promotes non-small cell lung cancer metastasis

Cancer is a disease with molecular heterogeneity that is closely related to gene mutations and epigenetic changes. The principal histological subtype of lung cancer is non-small cell lung cancer (NSCLC). Long noncoding RNA (lncRNA) is a kind of RNA that is without protein coding function, playing a critical role in the progression of cancer. In this research, the regulatory mechanisms of lncRNA phosphorylase kinase regulatory subunit alpha 1 antisense RNA 1 (PHKA1-AS1) in the progression of NSCLC were explored. The increased level of N6-methyladenosine (m6A) modification in NSCLC caused the high expression of PHKA1-AS1. Subsequently, high-expressed PHKA1-AS1 significantly facilitated the proliferation and metastasis of NSCLC cells, and these effects could be reversed upon the inhibition of PHKA1-AS1 expression, both in vivo and in vitro. Additionally, the target protein of PHKA1-AS1 was actinin alpha 4 (ACTN4), which is known as an oncogene. Herein, PHKA1-AS1 could enhance the protein stability of ACTN4 by inhibiting its ubiquitination degradation process, thus exerting the function of ACTN4 in promoting the progress of NSCLC. In conclusion, this research provided a theoretical basis for further exploring the potential mechanism of NSCLC metastasis and searching novel biomarkers related to the pathogenesis and progression of NSCLC.

Small cells - big issues: biological implications and preclinical advancements in small cell lung cancer

Current treatment guidelines refer to small cell lung cancer (SCLC), one of the deadliest human malignancies, as a homogeneous disease. Accordingly, SCLC therapy comprises chemoradiation with or without immunotherapy. Meanwhile, recent studies have made significant advances in subclassifying SCLC based on the elevated expression of the transcription factors ASCL1, NEUROD1, and POU2F3, as well as on certain inflammatory characteristics. The role of the transcription regulator YAP1 in defining a unique SCLC subset remains to be established. Although preclinical analyses have described numerous subtype-specific characteristics and vulnerabilities, the so far non-existing clinical subtype distinction may be a contributor to negative clinical trial outcomes. This comprehensive review aims to provide a framework for the development of novel personalized therapeutic approaches by compiling the most recent discoveries achieved by preclinical SCLC research. We highlight the challenges faced due to limited access to patient material as well as the advances accomplished by implementing state-of-the-art models and methodologies.

ID2 Promotes Lineage Transition of Prostate Cancer through FGFR and JAK-STAT Signaling

The use of androgen receptor pathway inhibitors (ARPIs) has led to an increase in the proportion of AR-null prostate cancer, including neuroendocrine prostate cancer (NEPC) and double-negative prostate cancer (DNPC), but the mechanism underlying this lineage transition has not been elucidated. We found that ID2 expression was increased in AR-null prostate cancer. In vitro and in vivo studies confirmed that ID2 promotes PCa malignancy and can confer resistance to enzalutamide in PCa cells. We generated an ID2 UP50 signature, which is capable of determining resistance to enzalutamide and is valuable for predicting patient prognosis. Functional experiments showed that ID2 could activate stemness-associated JAK/STAT and FGFR signaling while inhibiting the AR signaling pathway. Our study indicates a potentially strong association between ID2 and the acquisition of a stem-like phenotype in adenocarcinoma cells, leading to resistance to androgen deprivation therapy (ADT) and next-generation ARPIs in prostate cancer.

Identification of protein signatures for lung cancer subtypes based on BPSO method

The objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downloaded and preprocessed for analysis. We used BPSO feature selection method and identified 10 candidate protein biomarkers that have better classifying performance, as analyzed by t-SNE and PCA algorithms. To explore the causalities among these proteins and their associations with tumor subtypes, we conducted the PCStable algorithm to construct a regulatory network. Results indicated that 4 proteins, MIG6, CD26, NF2, and INPP4B, were directly linked to the lung cancer subtypes and may be useful in guiding therapeutic decision-making. Besides, spearman correlation, Cox proportional hazard model and Kaplan-Meier curve was employed to validate the biological significance of the candidate proteins. In summary, our study highlights the importance of protein biomarkers in the classification of lung cancer subtypes and the potential of computational methods for identifying key biomarkers and understanding their underlying biological mechanisms.

Non-coding RNAs in lung cancer: molecular mechanisms and clinical applications

Lung cancer (LC) is a heterogeneous disease with high malignant degree, rapid growth, and early metastasis. The clinical outcomes of LC patients are generally poor due to the insufficient elucidation of pathological mechanisms, low efficiency of detection and assessment methods, and lack of individualized therapeutic strategies. Non-coding RNAs (ncRNAs), including microRNA (miRNA), long non-coding RNA (lncRNA), and circular RNA (circRNA), are endogenous regulators that are widely involved in the modulation of almost all aspects of life activities, from organogenesis and aging to immunity and cancer. They commonly play vital roles in various biological processes by regulating gene expression via their interactions with DNA, RNA, or protein. An increasing amount of studies have demonstrated that ncRNAs are closely correlated with the initiation and development of LC. Their dysregulation promotes the progression of LC via distinct mechanisms, such as influencing protein activity, activating oncogenic signaling pathways, or altering specific gene expression. Furthermore, some ncRNAs present certain clinical values as biomarker candidates and therapeutic targets for LC patients. A complete understanding of their mechanisms in LC progression may be highly beneficial to developing ncRNA-based therapeutics for LC patients. This review mainly focuses on the intricate mechanisms of miRNA, lncRNA, and circRNA involved in LC progression and discuss their underlying applications in LC treatment.

PU.1 negatively regulates tumorigenesis in non-small-cell lung cancer

PU.1 is a key transcription factor that modulates hematopoietic cell differentiation and is involved in various physiological and pathological processes. PU.1 has been described to have multiple roles in a diverse range of cancers, but its contribution in non-small-cell lung cancer (NSCLC) has not been clearly elucidated. Fifty pairs of lung adenocarcinoma (LUAD) tissues and paraneoplastic tissues were collected. RT-qPCR assay was used to test PU.1 expression. Expression of PU.1 in LUAD cell lines and control cell lines was detected by RT-qPCR, and the role of PU.1 in LUAD was investigated by in vitro experiment. Levels of the major proteins in the apoptotic pathway were also detected by Western blot. The expression of PU.1 was remarkably downregulated in LUAD. Overexpression of PU.1 impaired the viability of LUAD cells as well as their metastatic function. In addition, PU.1 promoted apoptosis of LUAD cells by decreasing Bcl2 and increasing Bax/Bak1 expression. PU.1 plays an inhibitory role in LUAD, mainly promoting the apoptosis of LUAD cells.

Comprehensive Analysis of DMRT3 as a Potential Biomarker Associated with the Immune Infiltration in a Pan-Cancer Analysis and Validation in Lung Adenocarcinoma

Doublesex and Mab-3 related Transcription Factor 3 (DMRT3) is associated with the prognosis of some tumors. It is possible to explore the role of DMRT3 in the cancer process using bioinformatic approaches and experimental validation. We comprehensively explored the clinical and immunological characteristics of DMRT3. The DMRT3 expression is abnormal in human cancers and correlates with clinical staging. A high DMRT3 expression is significantly associated with poor overall survival (OS) in KIRC, KIRP, LUAD, and UCEC. Amplification was the greatest frequency of the DMRT3 alterations in pan-cancer. The OS was significantly lower in the DMRT3 altered group than in the DMRT3 unaltered group (P = 0.0276). The DMRT3 expression was significantly associated with MSI in three cancer types and TMB in six cancer types. The DMRT3 expression was significantly correlated with the level of the immune cell infiltration and the immune checkpoint genes. The DMRT3 was involved in some pathways in pan-cancer. DMRT3 may play a role in chemotherapy and may be associated with chemoresistance. A ceRNA network of KCNQ1OT1/miR-335-5p/DMRT3 was constructed in LUAD. DMRT3 was significantly upregulated in the LUAD cell lines. DMRT3 was aberrantly expressed in pan-cancer and may promote tumorigenesis and progression via different mechanisms. DMRT3 can be used as a therapeutic target to treat cancer in humans.

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.

Circulating circular RNA hsa_circ_0023179 acts as a diagnostic biomarker for non-small-cell lung cancer detection

BACKGROUND

Lung cancer, the most prevalent cancer-related death worldwide, still lacks the means for early diagnosis. Because of the unique properties of the loop that make it stable in body fluids, circular RNAs (circRNAs) as a biomarker becomes a possibility. This research purposed to explore whether hsa_circ_0023179 can be applied as a possible biomarker for the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).

METHODS

hsa_circ_0023179 was screened by high-throughput sequencing of three pairs of NSCLC tissues and their surrounding tissues. Agarose gel electrophoresis (AGE), Sanger sequencing, exonuclease digestion assay, and actinomycin D were used to affirm the molecular properties of circRNA. Precision determination was performed by placement at room temperature and multiple freeze-thawing test for methodological evaluation. The expression of hsa_circ_0023179 in tissues, serum, and cells was determined by quantitative real-time polymerase chain reaction (qRT-PCR) to establish the receiver operating characteristic (ROC) curve to assess the diagnostic efficacy of hsa_circ_0023179.

RESULTS

hsa_circ_0023179 conforms to the basic properties of circRNA, and the detection method of hsa_circ_0023179 has good stability and repeatability. Its expression was connected to histological type, TNM stage, lymph node metastasis, and distal metastasis in NSCLC tissues, serum, and cells. Compared with traditional tumor markers with higher sensitivity and specificity. A combined diagnosis can significantly improve the diagnostic value. The decrease in postoperative expression level suggests some potential for dynamic monitoring.

CONCLUSION

hsa_circ_0023179 might be a promising novel serum marker for the detection and prediction of NSCLC.

Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Identification of metastasis-related long non-coding RNAs in lung cancer through a novel tumor mesenchymal score

Long non-coding RNAs (lncRNAs) have been proven to play critical roles in epithelial-mesenchymal transition (EMT) and metastasis of lung cancer. However, the biological functions and related mechanisms of lncRNAs are unclear. In addition, the EMT-based prognosis prediction in lung cancer still lacks investigation. Here, we established the methodology of identifying critical metastasis-related lncRNAs using comprehensive datasets of cancer transcriptome, genome and epigenome, and also provided tools for prognosis prediction in lung cancer. Initially, important mesenchymal marker genes were identified to compose the tumor mesenchymal score, which predicted patient prognosis in lung cancer, especially lung adenocarcinoma (LUAD). The score was also correlated with several crucial biological and physiological processes, such as tumor immune and hypoxia. Based on the score, lung cancer patients was classified into epithelial and mesenchymal subtypes, and lncRNAs which exhibited expressional dysregulation, promotor methylation alteration and copy number variation between the two subtypes in LUAD were identified and underwent further prognostic analyses. Finally, we identified 14 lncRNAs as EMT-related and significant biomarkers in prognosis prediction of LUAD. As validation, lncRNA RBPMS-AS1 was proven to be co-expressed with epithelial biomarkers, suppressive for A549 cell migration, invasion and EMT, and also significantly associated with better outcomes of LUAD patients, suggesting the potential of RBPMS-AS1 to serve as a lncRNA epithelial biomarker in metastasis of LUAD. Based on the identified lncRNAs, an EMT-linked lncRNA prognostic signature was further established. Taken together, our study provides robust predictive tools, potential lncRNA targets and feasible screening strategies for future study of lung cancer metastasis.

A prognostic score based on long-term survivor unique transcriptomic signatures predicts patient survival in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is known for its poor prognosis with few long-term survivors. This study aimed to establish a prognostic score using unique transcriptomic profiles of long-term survivors to be used as a patient selection tool for meaningful clinical intervention in PDAC. In TCGA PDAC cohort, 16 genes were significantly upregulated in the long-term survivor tumors. A prognostic score was established using these 16 genes by LASSO Cox regression, and PHKG1, HOXA4, ISL2, DMRT3 and TRA2A gene expressions were included in the score. The prognostic value was confirmed in both testing and validation cohorts. The characteristics of the high score tumor was investigated by bioinformatical approach. The high score tumor was associated with TP53 mutation but not with other commonly enhanced signaling pathways in PDAC. The high score tumor was associated with higher tumor mutational burden and unfavorable tumor microenvironment (TME), such as lower infiltration of CD8-positive T cells and dendritic cells, and less cell composition of mature blood vessels and fibroblasts. The high score tumor was also associated with enhanced cell proliferation and margin positivity after surgery. The impact of score component genes on the cell proliferation was investigated by in vitro experiments. Silencing of the score component genes promoted cell proliferation. In conclusion, the prognostic score predicted PDAC patient survival and was associated with cancer aggressiveness such as unfavorable TME and enhanced cell proliferation.

Transcriptome Analysis Identifies Doublesex and Mab-3 Related Transcription Factor (DMRT3) in Nasal Polyp Epithelial Cells of Patients Suffering from Non-Steroidal Anti-Inflammatory Drug-Exacerbated Respiratory Disease (AERD)

Background: Aspirin-exacerbated respiratory disease (AERD) is a syndrome characterised by chronic rhinosinusitis, nasal polyps, asthma and aspirin intolerance. An imbalance of eicosanoid metabolism with anover-production of cysteinyl leukotrienes (CysLTs) has been associated with AERD. However, the precise mechanisms underlying AERD are unknown. Objective: To establish the transcriptome of the nasal polyp airway epithelial cells derived from AERD patients to discover gene expression patterns in this disease. Methods: Nasal airway epithelial cells were isolated from 12 AERD polyps and 8 AERD non-polyp nasal mucosa samples as controls from the same subjects. Utilising the Illumina HiSeq 2500 platform, RNA samples were sequenced. Potential gene candidate DMRT3 was selected from the differentially-expressed genes for validation. Results: Comparative transcriptome profiling of nasal epithelial cells was accomplished in AERD. A total of 20 genes had twofold mean regulation expression differences or greater. In addition, 8 genes were upregulated, including doublesex and mab-3 related transcription factor 3 (DMRT3), and 12 genes were downregulated. Differentially regulated genes comprised roles in inflammation, defence and immunity. Metabolic process and embryonic development pathways were significantly enriched. Enzyme-linked immune sorbent assay (ELISA) results of DMRT3 in AERD patients were significantly upregulated compared to controls (p = 0.03). Immunohistochemistry (IHC) of AERD nasal polyps localised DMRT3 and was predominantly released in the airway epithelia. Conclusion: Findings suggest that DMRT3 could be potentially involved in nasal polyp development in AERD patients. Furthermore, several genes are downregulated, hinting at the dedifferentiation phenomenon in AERD polyps. However, further studies are imperative to confirm the exact mechanism of polyp formation in AERD patients.

ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of human disease in early stages

MOTIVATION

Gene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a 'black box', barely providing biological and clinical interpretability from the box.

RESULTS

To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative-restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small-cell lung cancer patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. In addition, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (P-value < 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine.

AVAILABILITYAND IMPLEMENTATION

ECMarker is open source as a general-purpose tool at https://github.com/daifengwanglab/ECMarker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Use of four genes in exosomes as biomarkers for the identification of lung adenocarcinoma and lung squamous cell carcinoma

The determination of biomarkers in the blood specific for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is crucial for the selection of effective treatment strategies and the prediction of prognosis. The purpose of the present study was to analyze the differentially expressed genes (DEGs) in LUSC and LUAD from The Cancer Genome Atlas (TCGA) database. In order to identify the potential biomarkers for non-small cell lung cancer (NSCLC) for clinical diagnosis, bioinformatics was used to analyze the DEGs of two subtypes of NSCLC, LUAD and LUSC. Exosomes were isolated from the serum of patients with LUAD or LUSC and identified using transmission electron microscopy, nanoparticle tracking analysis and western blot analysis. A total of four differential exosomal mRNAs were selected for validation with serum samples from 70 patients with NSCLC via reverse transcription-quantitative polymerase chain reaction. Receiver operating characteristic curves were established to evaluate the clinical diagnostic value of four DEGs for patients with LUAD and LUSC. The analysis based on TCGA data revealed the DEGs in LUSC and LUAD: A total of 1,619 genes were differentially expressed in patients with LUSC and LUAD. DEGs analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that inflammation-related signaling pathways, such as complement pathways, and multiple autoimmune diseases, such as systemic lupus erythematosus and asthma were mainly enriched in LUAD. The cell cycle, Hippo signaling pathway, Rap1 signaling pathway and Wnt signaling pathway were the main signaling pathways enriched in LUSC. The combination of tumor protein P63 (TP63), keratin 5 (KRT5), CEA cell adhesion molecule 6 (CEACAM6) and surfactant protein B (SFTPB) improved the specificity and sensitivity in the diagnosis of different lung cancer subtypes. Exosomal TP63, KRT5, CEACAM6 and SFTPB mRNAs can thus be used as biomarkers to differentiate between LUSC and LUAD, and may provide a novel strategy for their differential diagnosis and treatment.

Novel gene signatures for stage classification of the squamous cell carcinoma of the lung

The squamous cell carcinoma of the lung (SCLC) is one of the most common types of lung cancer. As GLOBOCAN reported in 2018, lung cancer was the first cause of death and new cases by cancer worldwide. Typically, diagnosis is made in the later stages of the disease with few treatment options available. The goal of this work was to find some key components underlying each stage of the disease, to help in the classification of tumor samples, and to increase the available options for experimental assays and molecular targets that could be used in treatment development. We employed two approaches. The first was based in the classic method of differential gene expression analysis, network analysis, and a novel concept known as network gatekeepers. The second approach was using machine learning algorithms. From our combined approach, we identified two sets of genes that could function as a signature to identify each stage of the cancer pathology. We also arrived at a network of 55 nodes, which according to their biological functions, they can be regarded as drivers in this cancer. Although biological experiments are necessary for their validation, we proposed that all these genes could be used for cancer development treatments.

Single-cell RNA sequencing reveals cellular and molecular immune profile in a Pembrolizumab-responsive PD-L1-negative lung cancer patient

High expression of PD-L1 predicts PD-1/PD-L1 inhibitor benefit, meanwhile a few PD-L1-negative patients still benefit from these drugs. In this study, we aimed to explore the underlying cellular and molecular characteristics via single-cell sequencing. Before and after treatment with Pembrolizumab, peripheral blood mononuclear cells (PBMCs) were isolated via Ficoll gradient. Thereafter, single-cell RNA sequencing was performed, and clinical significance was validated with The Cancer Genome Atlas (TCGA) cohort. All 3423 cells of 16 clusters were classified into eight cell types, including NKG7+ T, NKG7+ NK, Naïve T, CDC1C+ dendritic cells, CD8+ T cells, B cells, macrophages and erythrocytes. Cell proportion, the clinical significance of differentially expressed genes and significant pathways of NKG7+ T, NKG7+ NK, Naïve T and CD8+ T cells were analyzed. Ubiquitin-mediated proteolysis/cell cycle/natural killer cell-mediated cytotoxicity were identified as PD-1 blockage-responsive pathways in NKG7+ NK cells. Apoptosis/Th1 and Th2 cell differentiation were proposed as Pembrolizumab-affected pathways in NKT cells. In gene level, ID2, PIK3CD, UQCR10, MATK, MZB1, IL7R and TRGC2 showed a significant correlation with PD-1 expression after TCGA dataset validation, which could possess potential as predictive markers for patients with PD-L1-negative lung squamous cell carcinoma who can benefit from Pembrolizumab.

Distinct transcriptional programs of SOX2 in different types of small cell lung cancers

SOX2 is recognized as an oncogene in human small cell lung cancer (SCLC), which is an aggressive neuroendocrine (NE) tumor. However, the role of SOX2 in SCLC is not completely understood, and strategies to selectively target SOX2 in SCLC cells remain elusive. Here, we show, using next-generation sequencing, that SOX2 expressed in the ASCL1-high SCLC (SCLC-A) subtype cell line is dependent on ASCL1, which is a lineage-specific transcriptional factor, and is involved in NE differentiation and tumorigenesis. ASCL1 recruits SOX2, which promotes INSM1 and WNT11 expression. Immunohistochemical studies revealed that SCLC tissue samples expressed SOX2, ASCL1, and INSM1 in 18 out of the 30 cases (60%). Contrary to the ASCL1-SOX2 signaling axis controlling SCLC biology in the SCLC-A subtype, SOX2 targets distinct genes such as those related to the Hippo pathway in the ASCL1-negative, YAP1-high SCLC (SCLC-Y) subtype. Although SOX2 knockdown experiments suppressed NE differentiation and cell proliferation in the SCLC-A subtype, they did not sufficiently impair the growth of the SCLC-Y subtype cell lines in vitro and ex vivo. The present results support the importance of the ASCL1-SOX2 axis as a main subtype of SCLC, and suggest the therapeutic potential of targeting the ASCL1-SOX2 axis.

Downregulation of miRNA-126-3p is associated with progression of and poor prognosis for lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is the main pathological type of pulmonary malignant tumors; at present, less than 10% of patients with advanced metastatic LUSC live for more than 5 years. We previously reported that low expression of miRNA-126-3p is associated with the occurrence and progression of lung adenocarcinoma (LUAD). Here, we examined expression of miRNA-126-3p in 23 samples from patients with LUSCs and 23 normal control specimens by quantitative real-time PCR (RT-qPCR). Associations between miRNA-126-3p expression and clinical features were studied from materials derived from Gene Expression Omnibus (GEO) chips and The Cancer Genome Atlas (TCGA) database. Twelve online platforms were used to identify candidate target genes of miRNA-126-3p. Further analyses of the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and protein-protein interaction (PPI) network were performed on the target genes. GEO microarray analysis, TCGA data mining, RT-qPCR, and integration analysis consistently reported low expression of miRNA-126-3p in LUSC. A total of 42 genes were identified as potential target genes of miRNA-126-3p from online platforms, GEO microarrays, and the TCGA database. GO and KEGG analyses demonstrated that the target genes are involved in several biological processes that promote the progression of LUSC. SOX2, E2F2, and E2F3 were selected as hub genes from the PPI network for further analysis. In summary, our results suggest that the low expression of miRNA-126-3p may play a role in promoting the development of LUSC and miRNA-126-3p may be a biomarker for LUSC early diagnosis and prognosis.

PenDA, a rank-based method for personalized differential analysis: Application to lung cancer

The hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here, we propose a novel method, PenDA, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access, user-friendly R package is available at https://github.com/bcm-uga/penda.

The hopes of precision medicine rely on our capacity to measure individual molecular information for personalized diagnosis and treatment. These challenging perspectives will be only possible with the development of efficient methodological tools to identify patient-specific molecular defects from the many precise molecular information that one can access at the single-individual, single tissue or even single-cell levels. Such methods will provide a better understanding of disease-specific biological mechanisms and will promote the development of personalized therapeutic strategies. Here we describe a novel method, named PenDA, to perform differential analysis of gene expression at the individual level. Based on a realistic benchmark of simulated tumors, we demonstrated that PenDA reaches very high efficiency in detecting sample-specific deregulated genes. We then applied the method to two large cohorts associated with lung cancer. A detailed statistical analysis of the results allowed to isolate genes with specific deregulation patterns, like genes that are up-regulated in all tumors or genes that are expressed but never deregulated in any tumors. Given their specificities, these genes are likely to be of interest in therapeutic research. In particular, we were able to identified 37 new biomarkers associated to bad prognosis that we validated on two independent cohorts.

Landscape of cancer diagnostic biomarkers from specifically expressed genes

Although there has been great progress in cancer treatment, cancer remains a serious health threat to humans because of the lack of biomarkers for diagnosis, especially for early-stage diagnosis. In this study, we comprehensively surveyed the specifically expressed genes (SEGs) using the SEGtool based on the big data of gene expression from the The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects. In 15 solid tumors, we identified 233 cancer-specific SEGs (cSEGs), which were specifically expressed in only one cancer and showed great potential to be diagnostic biomarkers. Among them, three cSEGs (OGDH, MUDENG and ACO2) had a sample frequency >80% in kidney cancer, suggesting their high sensitivity. Furthermore, we identified 254 cSEGs as early-stage diagnostic biomarkers across 17 cancers. A two-gene combination strategy was applied to improve the sensitivity of diagnostic biomarkers, and hundreds of two-gene combinations were identified with high frequency. We also observed that 13 SEGs were targets of various drugs and nearly half of these drugs may be repurposed to treat cancers with SEGs as their targets. Several SEGs were regulated by specific transcription factors in the corresponding cancer, and 39 cSEGs were prognosis-related genes in 7 cancers. This work provides a survey of cancer biomarkers for diagnosis and early diagnosis and new insights to drug repurposing. These biomarkers may have great potential in cancer research and application.

[Identification of differentially expressed genes between lung adenocarcinoma and squamous cell carcinoma using transcriber signature analysis]

OBJECTIVE

To analyze the differentially expressed genes (DEGs) between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) with bioinformatics analysis and search for potential biomarkers for clinical diagnosis of nonsmall cell lung cancer (NSCLC).

METHODS

The gene expression profiling datasets of LUAD and LUSC were acquired. The transcriptome differences between LUAD and LUSC were identified using R language processing and t-test analysis. The differential expressions of the genes were shown by Venn diagram. The DEGs identified by GEO2R were analyzed with DAVID and Ingenuity Pathway Analysis (IPA) to identify the signaling pathways and biomarkers that could be used for differential diagnosis of LUAD and LUSC. The TCGA data and the biomarker expression data from clinical lung cancer samples were used to verify the differential expressions of the Osteoarthritis pathway and LXR/RXR between LUAD and LUSC. We further examined the differential expressions of miR-181 and its two target genes, WNT5A and MBD2, in 23 clinical specimens of lung squamous cell carcinoma and the paired adjacent tissues.

RESULTS

GEO data analysis identified 851 DEGs (including 276 up-regulated and 575 down-regulated genes) in LUAD and 885 DEGs (including 406 up-regulated and 479 down-regulated genes) in LUSC. DAVID and IPA analysis revealed that leukocyte migration and inflammatory responses were more abundant in LUAD than in LUSC. Osteoarthritis pathway was inhibited in LUAD and activated in LUSC. IPA analysis showed that transcription factors (GATA4, RELA, YBX1, TP63 and MBD2), cytokines (WNT5A and IL1A) and microRNAs (miR-34a, miR-181b and miR-15a) differed significantly between LUAD and LUSC. miR-34a with IL-1A, miR-15a with YBX1, and miR-181b with WNT5A and MBD2 could serve as the paired microRNA and mRNA targets for differential diagnosis of NSCLC subtypes. Analysis of the clinical samples showed an increased expression of miR-181b-5p and the down-regulation of WNT5A, which could be used as molecular markers for the diagnosis of LUSC.

CONCLUSIONS

Through transcriptome analysis, we identified candidate genes, paired microRNAs and pathways for differentiating LUAD and LUSC, and they can provide novel differential diagnosis and therapeutic strategies for LUAD and LUSC.

Epigenetic Suppression of the T-box Subfamily 2 (TBX2) in Human Non-Small Cell Lung Cancer

(1) The TBX2 subfamily of transcription factors (TBXs 2, 3, 4 and 5) are markedly down-regulated in human non-small cell lung cancer (NSCLC) and exert tumor suppressor effects in lung malignancy. Yet, mechanisms underlying suppressed expression of the TBX2 subfamily in NSCLC are elusive. Here, we interrogated probable epigenetic mechanisms in suppressed expression of the TBX2 subfamily in human NSCLC. (2) TBX2 subfamily gene expression and methylation levels in NSCLC and normal lung tissues were surveyed using publicly available RNA-sequence and genome-wide methylation datasets. Methylation β-values of the four genes were statistically compared between NSCLCs and normal lung tissues, correlated with gene expression levels, and interrogated with clinicopathological variables. Expression and methylation levels of TBXs were quantified in NSCLC cells using real-time PCR and methylation-specific PCR assays, respectively. Effects of the DNA methyltransferase inhibitor 5-azacytidine (Aza) on TBX2 subfamily expression were assessed in NSCLC cells. Impact of TBX2 subfamily expression on Aza-treated cells was evaluated by RNA interference. (3) All four TBXs were significantly hypermethylated in NSCLCs relative to normal lung tissues (p < 0.05). Methylation β-values of the genes, with exception of TBX2, were significantly inversely correlated with corresponding mRNA expression levels (p < 0.05). We found no statistically significant differences in hypermethylation levels of the TBX2 subfamily by clinicopathological features including stage and tobacco history. Expression levels of the TBX genes were overall suppressed in NSCLC cells relative to normal alveolar cells. Members of the subfamily were significantly hypermethylated in all tested NSCLC cell lines relative to normal alveolar cells. Treatment with Aza induced the expression of the TBX2 subfamily concomitant with NSCLC cell growth inhibition. Further, simultaneous knockdown of the four TBX genes markedly reduced anti-growth effects of Aza in NSCLC cells. (4) Our study sheds light on new epigenetic profiles in the molecular pathogenesis of human NSCLC.