Transcriptome Profiles Of Patients Research Articles

Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is the most prevalent malignant gastrointestinal tumor. Ion channels contribute to tumor growth and progression through interactions with their neighboring molecules including lipids. The dysregulation of membrane ion channels and lipid metabolism may contribute to the epithelial-mesenchymal transition (EMT), leading to metastatic progression. Herein, transcriptome profiles of patients with ESCC were analyzed by performing differential gene expression and weighted gene co-expression network analysis to identify the altered ion channels, lipid metabolism- and EMT-related genes in ESCC. A total of 1,081 differentially expressed genes, including 113 ion channels, 487 lipid metabolism-related, and 537 EMT-related genes, were identified in patients with ESCC. Thereafter, EMT scores were correlated with altered co-expressed genes. The altered co-expressed genes indicated a correlation with EMT signatures. Interactions among 22 ion channels with 3 hub lipid metabolism- and 13 hub EMT-related proteins were determined using protein-protein interaction networks. A pathway map was generated to depict deregulated signaling pathways including insulin resistance and the estrogen receptor-Ca2+ signaling pathway in ESCC. The relationship between potential ion channels and 5-year survival rates in ESCC was determined using Kaplan-Meier plots and Cox proportional hazard regression analysis. Inositol 1,4,5-trisphosphate receptor type 3 (ITPR3) was found to be associated with poor prognosis of patients with ESCC. Additionally, drugs interacting with potential ion channels, including GJA1 and ITPR3, were identified. Understanding alterations in ion channels with lipid metabolism and EMT in ESCC pathophysiology would most likely provide potential targets for the better treatment of patients with ESCC.

Gastric Cancer Biomarker Candidates Identified by Machine Learning and Integrative Bioinformatics: Toward Personalized Medicine.

Gastric cancer (GC) is among the leading causes of cancer-related deaths worldwide. The discovery of robust diagnostic biomarkers for GC remains a challenge. This study sought to identify biomarker candidates for GC by integrating machine learning (ML) and bioinformatics approaches. Transcriptome profiles of patients with GC were analyzed to identify differentially expressed genes between the tumor and adjacent normal tissues. Subsequently, we constructed protein-protein interaction networks so as to find the significant hub genes. Along with the bioinformatics integration of ML methods such as support vector machine, the recursive feature elimination was used to select the most informative genes. The analysis unraveled 160 significant genes, with 88 upregulated and 72 downregulated, 10 hub genes, and 12 features from the variable selection method. The integrated analyses found that EXO1, DTL, KIF14, and TRIP13 genes are significant and poised as potential diagnostic biomarkers in relation to GC. The receiver operating characteristic curve analysis found KIF14 and TRIP13 are strongly associated with diagnosis of GC. We suggest KIF14 and TRIP13 are considered as biomarker candidates that might potentially inform future research on diagnosis, prognosis, or therapeutic targets for GC. These findings collectively offer new future possibilities for precision/personalized medicine research and development for patients with GC.

C1QA, C1QB, and GZMB are novel prognostic biomarkers of skin cutaneous melanoma relating tumor microenvironment

Skin cutaneous melanoma (SKCM) is the most lethal form of skin cancers owing to high invasiveness and high metastatic potential. Tumor microenvironment (TME) provides powerful evidences for discerning SKCM, raising the prospect to identify biomarkers of SKCM. Based on the transcriptome profiles of patients with SKCM and the corresponding clinical information from The Cancer Genome Atlas (TCGA), we used ESTIMATE algorithm to calculate ImmuneScore and StromalScore and identified the TME-Related differentially expressed genes (DEGs), than the intersected TME-Related DEGs were used for subsequent functional enrichment analysis. Protein–protein interaction (PPI) analysis was used to identify the functionality-related DEGs and univariate Cox regression analysis was used to identify the survival-related DEGs. Furthermore, SKCM-related DEGs were identified based on two Gene Expression Omnibus (GEO) datasets. Finally, we intersected functionality-related DEGs, survival-related DEGs, and SKCM-related DEGs, ascertaining that six DEGs (CCL4, CXCL10, CCL5, GZMB, C1QA, and C1QB) function as core TME-related genes (CTRGs). Significant differences of GZMB, C1QA, and C1QB expressions were found in gender and clinicopathologic staging of SKCM. High levels of GZMB, C1QA, and C1QB expressions were associated with favorable prognosis. Gene set enrichment analysis (GSEA) showed that cell–cell interaction, cell behavior, and intracellular signaling transduction may be mainly involved in both C1QA, C1QB and GZMB expressions and metabolism of phospholipid and amino acid, transcription, and translation may be implicated in low GZMB expressions. C1QA, C1QB, and GZMB are novel SKCM-relating CTRGs, providing promising immune-related prognostic biomarkers for SKCM.

In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways.

Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.

Abstract 71: Integrated-omics analysis identifies the druggable achilles targets to sensitize cancer cells with gefitinib resistance

Abstract Limited eligibility remains one of major drawbacks of target therapeutics as the oncogenic dependency is only determined by mutation status of a target. In order to extend the eligibility of target therapeutics, transcriptome profiles of patients have been examined to better delineate sensitivity of target therapeutics. In this study, we took advantage of multiple large datasets such as cancer cell line transcriptome data (CCLE), drug response data (CTRP), and gene dependency data (Achilles Project) to determine the sensitivity markers of tyrosine kinase inhibitors currently approved by FDA. In particular, EGFR inhibitors (iEGFR: lapatinib, gefitinib and erlotinib) revealed clear sensitivity cluster of cell line regardless of EGFR mutation status. Comparative analysis between sensitive (iEGFR-S) and resistant (iEGFR-R) cell groups revealed that integrated gene signatures such as chemokine, GPCR, and PI3K-Akt signaling pathway, were predicted as putative determinants of iEGFR susceptibility. Through integrated analysis of gene dependency data, among several Achilles’ targets (of which depletion increases vulnerability), predicted in iEGFR-R cells group, druggable Achilles’ targets (available to accompanied drugs) such as CCR6, AURKA and mTOR were identified as putative targets for sensitizing iEGFR-R group to iEGFR. Following biochemical studies validated the clear sensitizing effect of the pharmacological inhibitor for each predicted target, in the isogenic cell models with acquired iEGFR resistance. In addition, prediction and following biochemical validation, we demonstrated that the CCR6 inhibition significantly attenuated resistance of iEGFR by repressing mitochondrial ROS (mt-ROS), of which induction by iEGFR rescued cell death. These data suggest that integrated-omics analysis would be a useful approach to extend eligibility and improve the efficacy of target therapeutics. Citation Format: Eun-Ji Kwon, Haeseung Lee, Hyuk-Jin Cha. Integrated-omics analysis identifies the druggable achilles targets to sensitize cancer cells with gefitinib resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 71.

Identification of biomarkers for immunotherapy response in prostate cancer and potential drugs to alleviate immunosuppression.

Background: Immunotherapy has a significant effect on the treatment of many tumor types. However, prostate cancers generally fail to show significant responses to immunotherapy owing to their immunosuppressive microenvironments. To sustain progress towards more effective immunotherapy for prostate cancer, comprehensive analyses of the genetic characteristics of the immune microenvironment and novel therapeutic strategies are required.Methods: The transcriptome profiles of patients with prostate cancer were obtained from GEO and processed with the TIDE algorithm to predict their responses to immunotherapy. Next, the significant differentially expressed genes (DEGs) between the responder and non-responder groups were identified and used to compute the co-expression modules by WGCNA. Then, co-expression networks were constructed and survival analysis was applied to hub genes. Finally, drug candidates to alleviate immunosuppression were filtered in prostate cancer using GSEA based on hub genes.Results: In total, we identified 2758 significant DEGs and constructed 16 co-expression modules, seven of which were significantly correlated with the immune response score. In total, 133 hub genes were identified, of which 13 were significantly associated with prostate cancer prognosis. Co-expression networks of hub genes were constructed with KMT2B at the center. Finally, six candidate drugs for prostate cancer immunotherapy were identified in PC3 and LNCaP cell lines.Conclusions: We obtained datasets from multiple platforms, performed integrated bioinformatic analysis to identify 133 hub genes and 13 biomarkers of an immunotherapy response, and six candidate drugs were filtered to inhibit the immunosuppressive tumor microenvironment, to ultimately improve patient responses to immunotherapy in prostate cancer.

Comprehensive analysis of the autophagy-dependent ferroptosis-related gene FANCD2 in lung adenocarcinoma

BackgroundThe development of lung adenocarcinoma (LUAD) involves the interactions between cell proliferation and death. Autophagy-dependent ferroptosis, a distinctive cell death process, was implicated in a multitude of diseases, whereas no research revealing the relationship between autophagy-dependent ferroptosis and LUAD pathogenesis was reported. Thus, the primary objective was to explore the role and potential function of the autophagy-dependent ferroptosis-related genes in LUAD.MethodsClinical information and transcriptome profiling of patients with LUAD were retrieved and downloaded from open-source databases. Autophagy-dependent ferroptosis-related genes were screened by published articles. The critical gene was identified as the intersection between the differentially expressed genes and prognosis-related genes. Patients were divided into high- and low-risk groups using the expression level of the critical gene. The validity of the key gene prognosis model was verified by survival analysis. The correlation between the clinical characteristics of LUAD and the expression level of the key gene was analyzed to explore the clinical significance and prognosis value. And the roles of the key gene in response to chemotherapy, immune microenvironment, and tumor mutation burden were predicted. The validation of key gene expression levels was further performed by quantitative real-time PCR and immunohistochemistry staining.ResultsFANCD2, an essential autophagy-dependent ferroptosis-related gene by searching database, was confirmed as an independent prognostic factor for LUAD occurrence. The high expression level of FANCD2 was associated with an advantaged TNM stage, a less chemotherapy sensitivity, a low ImmuneScore, which indicated a deactivation status in an immune microenvironment, a high tumor mutation burden, and poor survival for LUAD patients. Pathway enrichment analysis showed that FANCD2 responded to oxidative stress and neutrophil-mediated immunity. Quantitative real-time PCR and immunohistochemistry staining showed that the expression level of FANCD2 is higher in LUAD patients than in normal tissue samples, which was in accordance with the database report.ConclusionFANCD2, an essential gene related to autophagy-dependent ferroptosis, could work as a biomarker, predicting the survival, chemotherapy sensitivity, tumor immunity, and mutation burden of LUAD. Researching autophagy-dependent ferroptosis and targeting the FANCD2 may offer a new perspective for treating and improving prognosis in LUAD.

Underlying Mechanisms and Candidate Drugs for COVID-19 Based on the Connectivity Map Database.

BackgroundThe coronavirus disease 2019 (COVID-19) has become a worldwide public health crisis. At present, there are no effective antiviral drugs to treat COVID-19. Although some vaccines have been developed, late-stage clinical trials that allow licensure by regulatory agencies are still needed. Previous reports have indicated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and SARS-CoV are highly homologous and both use angiotensin-converting enzyme 2 (ACE2) as the receptor to enter cells, and SARS-CoV infection reduces the ACE2 expression in the lung. Therefore, the analysis of genes co-expressed with ACE2 in the lung may uncover the underlying mechanism of COVID-19. Finally, we used the Connectivity map (Cmap) database to search for candidate drugs using transcriptome profiles of patients with COVID-19.MethodBased on the differentially expressed genes (DEGs), indicated by the expression of RNAs isolated from bronchoalveolar lavage fluid (BALF) cells of patients with COVID-19, we performed functional enrichment analysis and hub gene cluster analysis. Furthermore, we identified genes co-expressed with ACE2 in healthy lung samples and analyzed the significant genes. Additionally, to identify several candidate drugs for the treatment of COVID-19, we queried Cmap using DEGs and genes co-expressed with ACE2.Results and ConclusionThe up-regulated genes in the BALF cells of patients with COVID-19 are related to viral mRNA translation. The down-regulated genes are related to immune response functions. Genes positively correlated with ACE2 are related to immune defense and those negatively correlated are enriched in synaptic transmission functions. The results reflected prosperous viral proliferation and immune dysfunction in patients. Furthermore, ACE2 may not only mediate viral entrance, but also play an important role in immune defense. By using Cmap with transcriptome profiles of patients with COVID-19, we identified candidate drugs for the treatment of COVID-19, such as amantadine and acyclovir.

Therapeutic melanoma vaccine with cancer stem cell phenotype represses exhaustion and maintains antigen-specific T cell stemness by up-regulating BCL6

ABSTRACT We developed a therapeutic, gene-modified, allogeneic melanoma vaccine (AGI-101H), which, upon genetic modification, acquired melanoma stem cell-like phenotype. Since its initial clinical trial in 1997, the vaccine has resulted in the long-term survival of a substantial fraction of immunized patients (up to 20 years). Here, we investigated the potential molecular mechanisms underlying the long-lasting effect of AGI-101H using transcriptome profiling of patients’ peripheral T lymphocytes. Magnetically-separated T lymphocytes from AGI-101H-immunized long-term survivors, untreated melanoma patients, and healthy controls were subjected to transcriptome profiling using the microarray analyses. Data were analyzed with a multitude of bioinformatics tools (WebGestalt, DAVID, GSEA) and the results were validated with RT-qPCR. We found substantial differences in the transcriptomes of healthy controls and melanoma patients (both untreated and AGI-101H-vaccinated). AGI-101H immunization induced similar profiles of peripheral T cells as tumor residing in untreated patients. This suggests that whole stem cells immunization mobilizes analogous peripheral T cells to the natural adaptive anti-melanoma response. Moreover, AGI-101H treatment activated the TNF-α and TGF-β signaling pathways and dampened IL2-STAT5 signaling in T cells, which finally resulted in the significant up-regulation of a BCL6 transcriptional repressor, a known amplifier of the proliferative capacity of central memory T cells and mediator of a progenitor fate in antigen-specific T cells. In the present study, high levels of BCL6 transcripts negatively correlated with the expression of several exhaustion markers (CTLA4, KLRG1, PTGER2, IKZF2, TIGIT). Therefore, Bcl6 seems to promote a progenitor fate for cancer-experienced T cells from AGI-101H-vaccinated patients by repressing the exhaustion markers.

Looking beyond the brain to improve the pathogenic understanding of Parkinson\u2019s disease: implications of whole transcriptome profiling of Patients\u2019 skin

BackgroundParkinson’s Disease is a progressive neurodegenerative disease, characterized by symptoms of motor impairment, resulting from the loss of dopaminergic neurons in the midbrain, however non-neuronal symptoms are also common. Although great advances have been made in the pathogenic understanding of Parkinson’s Disease in the nervous system, little is known about the molecular alterations occurring in other non-neuronal organ systems. In addition, a higher rate of melanoma and non-melanoma skin cancer has been observed in the Parkinson’s Disease population, indicating crosstalk between these diseases.MethodsTo understand the molecular pathogenesis and gene expression alterations of Parkinson’s Disease in peripheral tissues, and in order to explore the possible link between skin cancer and neurodegeneration, whole transcriptomic profiling of patients’ skin was performed. Skin biopsies from 12 patients and matched controls were collected, and processed with high-throughput RNA-sequencing analysis.ResultsThis analysis resulted in a large collection of over 1000 differentially expressed genes, among which clear biological and functional networks could be distinguished. The central functional processes altered in patients skin can be grouped into six broad categories: impaired cellular metabolism and mitochondrial dysfunction, defective protein metabolism, disturbed skin homeostasis, dysfunctional nuclear processes, altered signalling and tumour pathways, as well as disordered immune regulation.ConclusionsThese results demonstrate that the molecular alterations leading to neurodegeneration in the CNS are systemic and manifest also in peripheral tissues, thereby indicating the presence of “skin-brain” crosstalk in Parkinson’s Disease. In addition, the extensive homeostatic imbalance and basal stress can lead to increased susceptibility to external and internal mutagenic hazards in these patients, and thus provide a possible molecular link for the crosstalk between skin cancer and Parkinson’s Disease.

RNA-binding disturbances as a continuum from spinocerebellar ataxia type 2 to Parkinson disease

CAG triplet expansions in Ataxin-2 gene (ATXN2) cause spinocerebellar ataxia type 2 and have a role that remains to be clarified in Parkinson's disease (PD). To study the molecular events associated with these expansions, we sequenced them and analyzed the transcriptome from blood cells of controls and three patient groups diagnosed with spinocerebellar ataxia type 2 (herein referred to as SCA2c) or PD with or without ATXN2 triplet expansions (named SCA2p).The transcriptome profiles of these 40 patients revealed three main observations: i) a specific pattern of pathways related to cellular contacts, proliferation and differentiation associated with SCA2p group, ii) similarities between the SCA2p and sporadic PD groups in genes and pathways known to be altered in PD such as Wnt, Ephrin and Leukocyte extravasation signaling iii) RNA metabolism disturbances with “RNA-binding” and “poly(A) RNA-binding” as a common feature in all groups. Remarkably, disturbances of ALS signaling were shared between SCA2p and sporadic PD suggesting common molecular dysfunctions in PD and ALS including CACNA1, hnRNP, DDX and PABPC gene family perturbations. Interestingly, the transcriptome profiles of patients with parkinsonian phenotypes were prevalently associated with alterations of translation while SCA2c and PD patients presented perturbations of splicing. While ATXN2 RNA expression was not perturbed, its protein expression in immortalized lymphoblastoid cells was significantly decreased in SCA2c and SCA2p versus control groups assuming post-transcriptional biological perturbations.In conclusion, the transcriptome data do not exclude the role of ATXN2 mutated alleles in PD but its decrease protein expression in both SCA2c and SCA2p patients suggest a potential involvement of this gene in PD. The perturbations of “RNA-binding” and “poly(A) RNA-binding” molecular functions in the three patient groups as well as gene deregulations of factors not yet described in PD but known to be deleterious in other neurological conditions, suggest the existence of RNA-binding disturbances as a continuum between spinocerebellar ataxia type 2 and Parkinson's disease.