Systematic exploration of the underlying mechanism of gemcitabine resistance in pancreatic adenocarcinoma

Chemoresistance often causes the failure of treatment and death of patients with advanced non-small-cell lung cancer. However, there is still no resistance genes signature and available enriched signaling derived from a comprehensive RNA-Seq data analysis of lung cancer patients that could act as a therapeutic target to re-sensitize the acquired resistant cancer cells to chemo-drugs. Hence, in this study, we aimed to identify the resistance signature for clinical lung cancer patients and explore the regulatory mechanism.Analysis of RNA-Seq data from clinical lung cancer patients was conducted in R studio to identify the resistance signature. The resistance signature was validated by survival time of lung cancer patients and qPCR in chemo-resistant cells. Cytokine application, small-interfering RNA and pharmacological inhibition approaches were applied to characterize the function and molecular mechanism of EREG and downstream signaling in chemoresistance regulation via stemness.The RTK and vitamin D signaling were enriched among resistance genes, where 6 genes were validated as resistance signature and associated with poor survival in patients. EREG/ERK signaling was activated by chemo-drugs in NSCLC cells. EREG protein promoted the NSCLC resistance to chemo-drugs by increasing stemness genes expression. Additionally, inhibition of EREG/ErbB had downregulated ERK signaling, resulting in decreased expression of stemness-associated genes and subsequently re-sensitized the resistant NSCLC cells and spheres to chemo-drugs.These findings revealed 6 resistance genes signature and proved that EREG/ErbB regulated the stemness to maintain chemoresistance of NSCLC via ERK signaling. Therefore, targeting EREG/ErbB might significantly and effectively resolve the chemoresistance issue. 

/pdf/epiregulin-increases-stemness-associated-genes-expression-wbr3ysgm.pdf

Epiregulin increases stemness-associated genes expression and promotes chemoresistance of non-small cell lung cancer via ERK signaling

Targeting protein for Xenopus kinesin-like protein 2 (TPX2) overexpression in human tumours is associated with increased malignancy. Its effect on gemcitabine resistance in pancreatic ductal adenocarcinoma (PDAC) has not been studied yet.The prognostic impact of TPX2 expression was examined in the tumour tissue of 139 patients with advanced PDAC (aPDAC) treated within the AIO-PK0104 trial or translational trials and of 400 resected PDAC (rPDAC) patients. The findings were validated using RNAseq data of 149 resected PDAC patients.In the aPDAC cohorts, 13.7% of all samples showed high TPX2 expression, conferring significantly shorter progression-free survival (PFS, HR 5.25, P < 0.001) and overall survival times (OS, HR 4.36, P < 0.001) restricted to gemcitabine-based treated patients (n = 99). In the rPDAC cohort, 14.5% of all samples showed high TPX2 expression, conferring significantly shorter disease-free survival times (DFS, HR 2.56, P < 0.001) and OS times (HR 1.56, P = 0.04) restricted to patients treated with adjuvant gemcitabine. RNAseq data from the validation cohort confirmed the findings.High TPX2 expression may serve as a negative predictor of gemcitabine-based palliative and adjuvant chemotherapy in PDAC and could be used to inform clinical therapy decisions.The clinical trial registry identifier is NCT00440167. 

/pdf/tpx2-expression-as-a-negative-predictor-of-gemcitabine-2rqdhkxq.pdf

TPX2 expression as a negative predictor of gemcitabine efficacy in pancreatic cancer

Recent advancements in cancer biology have revealed molecular changes associated with carcinogenesis and chemotherapeutic exposure. The available information is being gainfully utilized to develop therapies targeting specific molecules involved in cancer cell growth, survival, and chemoresistance. Targeted therapies have dramatically increased overall survival (OS) in many cancers. Therefore, developing such targeted therapies against oral squamous cell carcinoma (OSCC) is anticipated to have significant clinical implications. In the current work, we have identified drug-specific sensitivity-related prognostic biomarkers (BOP1, CCNA2, CKS2, PLAU, and SERPINE1) using gene expression, Cox proportional hazards regression, and machine learning in OSCC. Dysregulation of these markers is significantly associated with OS in many cancers. Their elevated expression is related to cellular proliferation and aggressive malignancy in various cancers. Mechanistically, inhibition of these biomarkers should significantly reduce cellular proliferation and metastasis in OSCC and should result in better OS. It is pertinent to note that no effective small-molecule candidate has been identified against these biomarkers to date. Therefore, a comprehensive in silico drug design strategy assimilating homology modeling, extensive molecular dynamics (MD) simulation, and ensemble molecular docking has been applied to identify potential compounds against identified targets, and potential molecules have been identified. We hope that this study will help in deciphering potential genes having roles in chemoresistance and a significant impact on OS. It will also result in the identification of new targeted therapeutics against OSCC. Graphical Abstract

/pdf/identification-of-therapeutically-potential-targets-and-cc1qnyfd.pdf

Identification of therapeutically potential targets and their ligands for the treatment of OSCC

The drug 5-fluorouracil (5-Fu) is the critical composition of colorectal cancer (CRC) treatments. Prognostic and predictive molecular biomarkers for CRC patients (CRCpts) treated with 5-Fu-based chemotherapy can provide assistance for tailoring treatment approach. Here, we established a molecular biomarker of 5-Fu resistance derived from colorectal cancer organoids (CRCOs) for predicting the survival of CRCpts. Forty-one CRCO cultures were generated from 50 CRC tumor tissues after surgery (82%). The following experiments revealed a great diversity in drug sensitivity for 10 μM 5-Fu treatment tested by using organoid size change. Fourteen cases (34.1%) were 5-Fu sensitive and the other 27 (65.9%) were resistant. Then, differentially expressed genes (DEGs) associated with 5-Fu resistance were outputted by transcriptome sequencing. In particular, DEGs were generated in two comparison groups: 1) 5-Fu sensitive and resistant untreated CRCOs; 2) CRCOs before 5-Fu treatment and surviving CRCOs after 5-Fu treatment. Some molecules and most of the pathways that have been reported to be involved in 5-Fu resistance were identified in the current research. By using DEGs correlated with 5-Fu resistance and survival of CRCpts, the gene signature and drug-resistant score model (DRSM) containing five molecules were established in The Cancer Genome Atlas (TCGA)-CRC cohort by least absolute shrinkage and selection operator (LASSO) regression analysis and 5-fold cross-validation. Multivariate analysis revealed that drug-resistant score (DRS) was an independent prognostic factor for overall survival (OS) in CRCpts in TCGA-CRC cohort (P < 0.001). Further validation results from four Gene Expression Omnibus (GEO) cohorts elucidated that the DRSM based on five genes related to 5-Fu chemosensitivity and developed from patient-derived organoids can predict survival of CRCpts. Meanwhile, our model could predict the survival of CRCpts in different subgroups. Besides, the difference of molecular pathways, tumor mutational burden (TMB), immune response-related pathways, immune score, stromal score, and immune cell proportion were dissected between DRS-high and DRS-low patients in TCGA-CRC cohort.

/pdf/molecular-biomarker-of-drug-resistance-developed-from-2cl3swo3.pdf

Molecular Biomarker of Drug Resistance Developed From Patient-Derived Organoids Predicts Survival of Colorectal Cancer Patients

To identify a gemcitabine resistance-associated gene signature for risk stratification and prognosis prediction in pancreatic cancer. Pearson correlation analysis was performed with gemcitabine half maximal inhibitory concentration (IC50) data of 17 primary pancreatic cancer lines from Genomics of Drug Sensitivity in Cancer (GDSC) and the transcriptomic data from GDSC and Broad Institute Cancer Cell Line Encyclopedia, followed by risk stratification, expression evaluation, overall survival (OS) prediction, clinical data validation and nomogram establishment. Our biomarker discovery effort identified a 14-gene signature, most of which featured differential expression. The 14-gene signature was associated with poor OS in E-MTAB-6134 (HR 2.37; 95% CI 1.75-3.2; p < 0.0001), pancreatic cancer-Canada (PACA-CA) (HR 1.76; 95% CI 1.31-2.37; p = 0.00015), and 4 other independent validation cohorts: pancreatic cancer-Australia (PACA-AU) (HR 1.9; 95% CI 1.38-2.61; p < 0.0001), The Cancer Genome Atlas (TCGA) (HR 1.73; 95% CI 1.11-2.69; p = 0.014), GSE85916 (HR 1.97; 95% CI 1.14-3.42; p = 0.014) and GSE62452 (HR 1.82; 95% CI 1.02-3.24; p = 0.039). Multivariate analysis revealed that the 14-gene risk score was an independent pancreatic cancer outcome predictor in E-MTAB-6134 (p < 0.001) and TCGA (p = 0.006). A nomogram including the 14-gene was established for eventual clinical translation. We identified a novel gemcitabine resistance gene signature for risk stratification and robust categorization of pancreatic cancer patients with poor prognosis.

/pdf/a-14-gene-gemcitabine-resistance-gene-signature-is-5gqlprl6jg.pdf

A 14-gene gemcitabine resistance gene signature is significantly associated with the prognosis of pancreatic cancer patients

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Zhen Weng

Author Tools

Chat about Author

Papers

A 14-gene gemcitabine resistance gene signature is significantly associated with the prognosis of pancreatic cancer patients