Identification of Key mRNAs as Prediction Models for Early Metastasis of Pancreatic Cancer Based on LASSO

Correlation of laminin subunit alpha 3 expression in pancreatic ductal adenocarcinoma with tumor liver metastasis and survival

BACKGROUND

The high mortality rate of pancreatic ductal adenocarcinoma (PDAC) is primarily attributed to metastasis. Laminin subunit alpha 3 (LAMA3) is known to modulate tumor progression. However, the influence of LAMA3 on liver metastasis in PDAC remains unclear. This study aimed to elucidate whether LAMA3 expression is increased in PDAC with liver metastasis.

PATIENTS AND METHODS

We extracted information related to LAMA3 expression levels and associated clinicopathological parameters from The Cancer Genome Atlas (TCGA) and four Gene Expression Omnibus (GEO) datasets. Clinicopathological analysis was performed; the Kaplan-Meier Plotter was used to evaluate LAMA3's prognostic effect in PDAC. We retrospectively collected clinicopathological data and tissue specimens from 117 surgically treated patients with PDAC at the Affiliated Hospital of Qingdao University. We assessed LAMA3 expression and investigated its correlation with the clinicopathological traits, clinical outcomes, and hepatic metastasis.

RESULTS

Amplified expression of LAMA3 was observed in PDAC tissue compared with normal tissue in the TCGA and GEO databases. High LAMA3 expression was associated with poor overall survival (OS) and relapse-free survival (RFS) in patients with PDAC. LAMA3 expression was significantly enhanced in PDAC tissues than in adjacent tissues. Tumor tissues from patients with PDAC exhibiting liver metastasis showed higher LAMA3 expression than those without liver metastasis. High LAMA3 expression correlated with large tumor size and TNM stage. LAMA3 expression and liver metastasis were independent predictive factors for OS; the former was independently associated with liver metastasis.

CONCLUSIONS

LAMA3 expression is elevated in patients with PDAC with liver metastasis and is a predictor of prognosis.

EMT-related gene risk model establishment for prognosis and drug treatment efficiency prediction in hepatocellular carcinoma

This study was designed to evaluate the prognosis and pharmacological therapy sensitivity of epithelial mesenchymal transition-related genes (EMTRGs) that obtained from the EMTome database in hepatocellular carcinoma (HCC) using bioinformatical method. The expression status of EMTRGs were also investigated using the clinical information of HCC patients supported by TCGA database and the ICGC database to establish the TCGA cohort as the training set and the ICGC cohort as the validation set. Analyze the EMTRGs between HCC tissue and liver tissue in the TCGA cohort in the order of univariate COX regression, LASSO regression, and multivariate COX regression, and construct a risk model for EMTRGs. In addition, enrichment pathways, gene mutation status, immune infiltration, and response to drugs were also analyzed in the high-risk and low-risk groups of the TCGA cohort, and the protein expression status of EMTRGs was verified. The results showed a total of 286 differentially expressed EMTRGs in the TCGA cohort, and EZH2, S100A9, TNFRSF11B, SPINK5, and CCL21 were used for modeling. The TCGA cohort was found to have a worse outcome in the high-risk group of HCC patients, and the ICGC cohort confirmed this finding. In addition, EMTRGs risk score was shown to be an independent prognostic factor in both cohorts by univariate and multivariate COX regression. The results of GSEA analysis showed that most of the enriched pathways in the high-risk group were associated with tumor, and the pathways enriched in the low-risk group were mainly associated with metabolism. Patients in various risk groups had varying immunological conditions, and the high-risk group might benefit more from targeted treatments. To sum up, the EMTRGs risk model was developed to forecast the prognosis for HCC patients, and the model might be useful in assisting in the choice of treatment drugs for HCC patients.

Multimodal AI for prediction of distant metastasis in carcinoma patients

Metastasis of cancer is directly related to death in almost all cases, however a lot is yet to be understood about this process. Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types (Bladder Carcinoma, Pancreatic Adenocarcinoma, and Head and Neck Squamous Carcinoma) with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at: https://github.com/rit-cui-lab/Multimodal-AI-for-Prediction-of-Distant-Metastasis-in-Carcinoma-Patients.

An epithelial-mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is closely associated with cancer cell metastasis. Colon adenocarcinoma (COAD) is one of the most common malignancies in the world, and its metastasis leading to poor prognosis remains a challenge for clinicians. The purpose of this study was to explore the prognostic value of EMT-related genes (EMTRGs) by bioinformatics analysis and to develop a new EMTRGs prognostic signature for COAD. Methods: The TCGA-COAD dataset was downloaded from the TCGA portal as the training cohort, and the GSE17538 and GSE29621 datasets were obtained from the GEO database as the validation cohort. The best EMTRGs prognostic signature was constructed by differential expression analysis, Cox, and LASSO regression analysis. Gene set enrichment analysis (GSEA) is used to reveal pathways that are enriched in high-risk and low-risk groups. Differences in tumor immune cell levels were analyzed using microenvironmental cell population counter and single sample gene set enrichment analysis. Subclass mapping analysis and Genomics of Drug Sensitivity in Cancer were applied for prediction of immunotherapy response and chemotherapy response, respectively. Results: A total of 77 differentially expressed EMTRGs were identified in the TCGA-COAD cohort, and they were significantly associated with functions and pathways related to cancer cell metastasis, proliferation, and apoptosis. We constructed EMTRGs prognostic signature with COMP, MYL9, PCOLCE2, SCG2, and TIMP1 as new COAD prognostic biomarkers. The high-risk group had a poorer prognosis with enhanced immune cell infiltration. The GSEA demonstrated that the high-risk group was involved in "ECM Receptor Interaction," "WNT Signaling Pathway" and "Colorectal Cancer." Furthermore, patients with high risk scores may respond to anti-CTLA4 therapy and may be more resistant to targeted therapy agents BI 2536 and ABT-888. Conclusion: Together, we developed a new EMTRGs prognostic signature that can be an independent prognostic factor for COAD. This study has guiding implications for individualized counseling and treatment of COAD patients.

Classification prediction of early pulmonary nodes based on weighted gene correlation network analysis and machine learning

OBJECTIVE

To use weighted gene correlation network analysis (WGCNA) and machine learning algorithm to predict classification of early pulmonary nodes with public databases.

METHODS

The expression data and clinical data of lung cancer patients were firstly extracted from public database (GTEx and TCGA) to study the differentially expressed genes (DEGs) of lung adenocarcinoma (LUAD). The intersection of three R packages (Dseq2, Limma, EdgeR) methods were selected as candidate DEGs for further study. WGCNA was used to obtain relevant modules and key genes of lung cancer classification, GO and KEGG enrichment analysis was performed. The model was built using two machine learning methods, Least Absolute Shrinkage and Selection Operator (LASSO) regression and tumor classification was also predicted with extreme Gradient Boosting (XGBoost) algorithm.

RESULTS

DEGs analysis revealed that there were 1306 LUAD genes. WGCNA module analysis showed that a total of 116 genes were significantly related to classification, and module genes were mainly related to 14 KEGG pathways. The machine learning algorithm identified 10 target genes by LASSO regression analysis of differential genes, and 18 genes were identified by XGBoost model. A total of 6 genes were found from the intersection of the above methods as classification signatures of early pulmonary nodules, including "HMGB3" "ARHGAP6" "TCF21" "FCN3" "COL6A6" "GOLM1".

CONCLUSION

Using DEGs analysis, WGCNA method and machine learning algorithm, six gene signatures related to early stage of LUAD, which can assist clinicians in disease classification prediction.

Overexpression of laminin-5 gamma-2 promotes tumorigenesis of pancreatic ductal adenocarcinoma through EGFR/ERK1/2/AKT/mTOR cascade

Pancreatic ductal adenocarcinoma (PDAC) is correlated with poor outcomes because of limited therapeutic options. Laminin-5 gamma-2 (LAMC2) plays a critical role in key biological processes. However, the detailed molecular mechanism and potential roles of LAMC2 in PDAC stay unexplored. The present study examines the essential role and molecular mechanisms of LAMC2 in the tumorigenesis of PDAC. Here, we identified that LAMC2 is significantly upregulated in microarray cohorts and TCGA RNA sequencing data of PDAC patients compared to non-cancerous/normal tissues. Patients with higher transcript levels of LAMC2 were correlated with clinical stages; dismal overall, as well as, disease-free survival. Additionally, we confirmed significant upregulation of LAMC2 in a panel of PDAC cell lines and PDAC tumor specimens in contrast to normal pancreatic tissues and cells. Inhibition of LAMC2 significantly decreased cell growth, clonogenic ability, migration and invasion of PDAC cells, and tumor growth in the PDAC xenograft model. Mechanistically, silencing of LAMC2 suppressed expression of ZEB1, SNAIL, N-cadherin (CDH2), vimentin (VIM), and induced E-cadherin (CDH1) expression leading to a reversal of mesenchymal to an epithelial phenotype. Interestingly, co-immunoprecipitation experiments demonstrated LAMC2 interaction with epidermal growth factor receptor (EGFR). Further, stable knockdown of LAMC2 inhibited phosphorylation of EGFR, ERK1/2, AKT, mTOR, and P70S6 kinase signaling cascade in PDAC cells. Altogether, our findings suggest that silencing of LAMC2 inhibited PDAC tumorigenesis and metastasis through repression of epithelial-mesenchymal transition and modulation of EGFR/ERK1/2/AKT/mTOR axis and could be a potential diagnostic, prognostic, and therapeutic target for PDAC.