Systems Biology Approach to Identify Novel Genomic Determinants for Pancreatic Cancer Pathogenesis

MKRN1 regulates the expression profiles and transcription factor activity in HeLa cells inhibition suppresses cervical cancer cell progression

Cervical cancer is one of the most common gynecologic malignancies worldwide, necessitating the identification of novel biomarkers and therapeutic targets. This study aimed to investigate the significance of MKRN1 in cervical cancer and explore its potential as a diagnostic marker and therapeutic target. The results indicated that MKRN1 expression was up-regulated in cervical cancer tissues and correlated with advanced tumor stage, higher grade, and poor patient survival. Functional studies demonstrated that targeting MKRN1 effectively inhibited cell proliferation, migration, and invasion, highlighting its critical role in tumor progression and metastasis. Moreover, the knockdown of MKRN1 resulted in altered expression patterns of six transcription factor-encoding genes, revealing its involvement in gene regulation. Co-expression network analysis unveiled complex regulatory mechanisms underlying the effects of MKRN1 knockdown on gene expression. Furthermore, the results suggested that MKRN1 might serve as a diagnostic marker for personalized treatment strategies and a therapeutic target to inhibit tumor growth, metastasis, and overcome drug resistance. The development of MKRN1-targeted interventions might hold promise for advancing personalized medicine approaches in cervical cancer treatment. Further research is warranted to validate these findings, elucidate underlying mechanisms, and translate these insights into improved management and outcomes for cervical cancer patients.

TGM2, HMGA2, FXYD3, and LGALS4 genes as biomarkers in acquired oxaliplatin resistance of human colorectal cancer: A systems biology approach

Acquired resistance to oxaliplatin is considered as the primary reason for failure in colorectal cancer (CRC) therapy. Identifying the underlying resistance mechanisms may improve CRC treatment. The present study aims to identify the key genes involved in acquired oxaliplatin-resistant in CRC by confirming the oxaliplatin resistance index (OX-RI). To this aim, two public microarray datasets regarding oxaliplatin-resistant CRC cells with different OX-RI, GSE42387, and GSE76092 were downloaded from GEO database to identify differentially expressed genes (DEGs). The results indicated that the OX-RI affects the gene expression pattern significantly. Then, 54 common DEGs in both datasets including 18 up- and 36 down-regulated genes were identified. Protein-protein interaction (PPI) analysis revealed 13 up- (MAGEA6, TGM2, MAGEA4, SCHIP1, ECI2, CD33, AKAP12, MAGEA12, CALD1, WFDC2, VSNL1, HMGA2, and MAGEA2B) and 12 down-regulated (PDZK1IP1, FXYD3, ALDH2, CEACAM6, QPRT, GRB10, TM4SF4, LGALS4, ALDH3A1, USH1C, KCNE3, and CA12) hub genes. In the next step, two novel up-regulated hub genes including ECI2 and SCHIP1 were identified to be related to oxaliplatin resistance. Functional enrichment and pathway analysis indicated that metabolic pathways, proliferation, and epithelial-mesenchymal transition may play dominant roles in CRC progression and oxaliplatin resistance. In the next procedure, two in vitro oxaliplatin-resistant sub-lines including HCT116/OX-R4.3 and HCT116/OX-R10 cells with OX-IR 3.93 and 10.06 were established, respectively. The results indicated the up-regulation of TGM2 and HMGA2 in HCT116/OX-R10 cells with high OX-RI and down-regulation of FXYD3, LGALS4, and ECI2 in both cell types. Based on the results, TGM2, HMGA2, FXYD3, and LGALS4 genes are related to oxaliplatin-resistant CRC and may serve as novel therapeutic targets.

Proteogenomic analysis of pancreatic cancer subtypes

Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. Therefore, there is a need to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset, including proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis based on the proteomics data, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes. In addition, we explore the clinical characteristics of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways previously associated with pancreatic cancer are altered among both subtypes of pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes that can be leveraged to devise more effective treatments.

Proteomic Analysis of Cell Lines and Primary Tumors in Pancreatic Cancer Identifies Proteins Expressed Only In Vitro and Only In Vivo

OBJECTIVES

A limited repertoire of good pancreatic ductal adenocarcinoma (PDAC) models is one of the main barriers in developing effective new PDAC treatments. We aimed to characterize 6 commonly used PDAC cell lines and compare them with PDAC patient tumor samples using proteomics.

METHODS

Proteomic methods were used to generate an extensive catalog of proteins from 10 PDAC surgical specimens, 9 biopsies of adjacent normal tissue, and 6 PDAC cell lines. Protein lists were interrogated to determine what extent the proteome of the cell lines reflects the proteome of primary pancreatic tumors.

RESULTS

We identified 7973 proteins from the cell lines, 5680 proteins from the tumor tissues, and 4943 proteins from the adjacent normal tissues. We identified 324 proteins unique to the cell lines, some of which may play a role in survival of cells in culture. Conversely, a list of 63 proteins expressed only in the patient samples, whose expression is lost in culture, may place limitations on the degree to which these model systems reflect tumor biology in vivo.

CONCLUSIONS

Our work offers a catalog of proteins detected in each of the PDAC cell lines, providing a useful guide for researchers seeking model systems for PDAC functional studies.

Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics

Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To address this challenge, we used targeted proteomics and other molecular data compiled by The Cancer Genome Atlas to reveal that pancreatic tumours can be broadly segregated into two distinct subtypes. Besides being associated with substantially different clinical outcomes, tumours belonging to each of these subtypes also display notable differences in diverse signalling pathways and biological processes. At the proteome level, we show that tumours belonging to the less severe subtype are characterised by aberrant mTOR signalling, whereas those belonging to the more severe subtype are characterised by disruptions in SMAD and cell cycle-related processes. We use machine learning algorithms to define sets of proteins, mRNAs, miRNAs and DNA methylation patterns that could serve as biomarkers to accurately differentiate between the two pancreatic cancer subtypes. Lastly, we confirm the biological relevance of the identified biomarkers by showing that these can be used together with pattern-recognition algorithms to accurately infer the drug sensitivity of pancreatic cancer cell lines. Our study shows that integrative profiling of multiple data types enables a biological and clinical representation of pancreatic cancer that is comprehensive enough to provide a foundation for future therapeutic strategies.