Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Escitalopram in Treating Depression in Patients With Advanced Lung or Gastrointestinal Cancer

ABSTRACT: RATIONALE: Escitalopram may help improve depression and quality of life in patients with advanced lung or gastrointestinal cancer. It is not yet known whether escitalopram is more effective than a placebo in treating depression in patients with advanced lung or gastrointestinal cancer. PURPOSE: This randomized clinical trial is studying the side effects of escitalopram and to see how well it works compared to a placebo in treating depression in patients with advanced lung or gastrointestinal cancer.

DISEASE(S): Lung Cancer,Colorectal Cancer,Fatigue,Depression,Depressive Disorder,Liver Cancer,Pancreatic Cancer,Gallbladder Cancer,Liver Neoplasms,Extrahepatic Bile Duct Cancer,Lung Neoplasms,Esophageal Neoplasms,Gallbladder Neoplasms,Bile Duct Neoplasms,Gastric Cancer,Esophageal Cancer,Pancreatic Neoplasms,Stomach Neoplasms,Cholangiocarcinoma

PROVIDER: 2033517 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

ACCESS DATA

Json Xml

Similar Datasets

lung cancer sEV uptake by T cells

Project description:Despite advances in immunotherapy, non-small cell lung carcinoma (NSCLC)’s clinical success is limited, possibly due to substantial immunological alterations in advanced cancer patients. This study examines the immunomodulatory effects of sEVs derived from lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) on T cells. Methods: SEVs were isolated from lung cancer cell lines and Jurkat-E6.1. SEV size and morphology were analysed by NTA and TEM, respectively, while western blotting confirmed sEV markers. SEV uptake was assessed, followed by resazurin assay, RNA isolation, quantification, cDNA preparation, RT-PCR, nano LC-MS, and bioinformatic analysis, before and after treating Jurkat-E6.1 cells with sEVs from A549 and SKMES1.

2025-06-02 | PXD063051 | Pride

Liu2023 - Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data

Project description:Immunotherapy has improved the prognosis of patients with advanced non-small cell lung cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC

2023-07-11 | BIOMD0000001074 | BioModels

Depression

Project description:depression

| PRJEB77878 | ENA

Development depression-associated miRNA biomarkers research

Project description:we profiled whole transcriptome in depression patients’ and healthy volunteers blood samples via microarray analysis.Differentially expressed miRNAs were selected for deep analysis. Especially the Differentially expressed genes that associated with cancer

2021-08-17 | GSE182194 | GEO

Development depression-associated ncRNA biomarkers research

Project description:we profiled whole transcriptome in depression patients’ and healthy volunteers blood samples via microarray analysis.Differentially expressed circRNAs, lncRNAs and mRNAs were selected for deep analysis. Especially the Differentially expressed genes that associated with cancer

2021-08-17 | GSE182193 | GEO

Anterior cingulate cortex mediates the comorbidity between colorectal cancer and depression-like behaviors

Project description:In this study, we established mice with orthotopic colorectal cancer (CRC mice) and observed depression-like behaviors in the CRC mice. Through whole-brain c-FOS mapping, functional connectivity analysis, gut-brain inverse mapping, and correlation analysis, combined with chemogenetic manipulation, we identified the anterior cingulate cortex (ACC) as a key hub in the depression-related brain network. we performed whole transcriptome sequencing of key neural nuclei to identify molecular changes associated with CRC-depression comorbidity. Furthermore, chemogenetic modulation of neuronal excitability not only alleviated depression-like behavior but also reduced CRC progression in these mice. Importantly, our findings suggest that targeting the ACC, in addition to conventional colorectal cancer therapies, could offer therapeutic and quality-of-life benefits for patients with both CRC and depression.

2024-11-20 | GSE281840 | GEO

Peripheral whole blood: Healthy control and Major depression disorder patients

Project description:Transcriptional profiling in the whole blood samples of healthy controls and major depression disorder (MDD) patients who have never been treated with depression medication. Samples included 20 healthy controls and 20 MDD patients who have never been treated with depression medication. Goal was to discover the differentially expressed genes.

2022-07-18 | GSE201332 | GEO

CHN1 as a Predictive Biomarker for Atopic Dermatitis-related Depression

Project description:The comorbidity of Atopic Dermatitis (AD) and depression has garnered increasing attention in recent years. Yet, the immunopathological mechanisms underlying this connection remain unclear. To bridge this gap, we collected peripheral blood mononuclear cell samples from 20 AD patients with and without depression, and performed RNA sequencing analysis. By integrating bioinformatics and machine learning techniques, we aimed to uncover the immune regulatory networks and identify key genetic markers for the depression comorbidity in AD patients. Our analysis revealed 394 differentially expressed genes in AD patients with depression as compared to those non-depression counterparts. Weighted gene co-expression network analysis pinpointed a pink module encompassing 83 genes strongly linked to depression. Functional enrichment analysis of critical module genes revealed significant enrichment scores in neurotransmitter uptake and the negative regulation of T-helper (Th) 17 cell differentiation. Furthermore, machine learning models of least absolute shrinkage and selection operator and support vector machines highlighted CHN1 as a potential pivotal gene upregulated in AD patients with depression. The expression level of CHN1 demonstrated positive correlation with Th2 and Th17 cytokine families, as well as with the Hospital Anxiety and Depression Scale-Depression score and Eczema Area and Severity Index. Validation experiments on clinical samples of an additional 20 participants confirmed the significant upregulation of CHN1 in depressed AD patients. This study is the first to unveil the intricate inflammatory and immune pathways underlying AD-associated depression, shedding light on the critical role of CHN1 connecting inflammation and neuropsychiatric symptoms.

2025-11-13 | GSE307177 | GEO

New perspectives in different gene expression profiles for early and locally advanced non-small cell lung cancer stem cells.

Project description:No effective treatments against lung cancer for early or advanced stages have been found. Our aim is to assess whether cancer stem cells (CSC) do show over expression of genes, which were previously identified for adenocarcinoma recurrence, in patients with early and locally advanced non-small cell lung cancer (NSCLC) stages.

2021-03-29 | GSE157427 | GEO

Targeting the novel SALL4/CBL-B pathway by HDAC inhibitor MS-275 in lung cancer

Project description:The overall survival of lung cancer patients remains dismal despite the availability of targeted therapies. Oncofetal protein SALL4 is a novel cancer target. We herein report that SALL4 was aberrantly expressed in a subset of lung cancer patients with poor survival. SALL4 silencing by RNA interference or SALL4 peptide inhibitor treatment led to impaired lung cancer cell growth. Expression profiling on SALL4-knockdown cells demonstrated that both the EGFR and IGF1R signaling pathways were affected. Further studies revealed that SALL4 suppresses these pathways indirectly by repressing CBL-B. Connectivity Map analysis revealed HDAC inhibitor MS-275 as a potential drug in treating SALL4-expressing cancers and this was confirmed using 17 lung cancer cell lines. In summary, we report for the first time that MS-275 can target the novel SALL4/CBL-B pathway in lung cancer. This lays the foundation for future clinical studies to evaluate the therapeutic efficacy of MS-275 in SALL4-positive lung cancer patients. Cells from human lung carcinoma cell line H661 with wildtype scrambled controls or Sall4 knockdown were used for RNA extraction and hybridization on Affymetrix microarrays.

2015-05-16 | E-GEOD-68959 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data