Evaluation of the diagnostic ability of laminin gene family for pancreatic ductal adenocarcinoma

Upregulated LAMA3 modulates proliferation, adhesion, migration and epithelial‑to‑mesenchymal transition of cholangiocarcinoma cells

A poor outcome for cholangiocarcinoma (CCA) patients is still a clinical challenge. CCA is typically recognized by the desmoplastic nature, which accounts for its malignancy. Among various extracellular matrix proteins, laminin is the most potent inducer for CCA migration. Herein, we accessed the expression profiles of laminin gene family and explored the significance of the key laminin subunit on CCA aggressiveness. Of all 11 laminin genes, LAMA3, LAMA5, LAMB3 and LAMC2 were concordantly upregulated based on the analysis of multiple public transcriptomic datasets and also overexpressed in Thai CCA cell lines and patient tissues in which LAMA3A upregulated in the highest frequency (97%) of the cases. Differential expression genes (DEGs) analysis of low and high laminin signature groups revealed LAMA3 as the sole common DEG in all investigated datasets. Restratifying CCA samples according to LAMA3 expression indicated the association of LAMA3 in the focal adhesion pathway. Silencing LAMA3 revealed that it plays important roles in CCA cell proliferation, adhesion, migration and epithelial-to-mesenchymal transition. Taken together, this research signifies the roles of dysregulated ECM homeostasis in CCA malignancy and highlights, for the first time, the potential usage of LAMA3 as the diagnostic biomarker and the therapeutic target to tackle the CCA stromal.

Laminin 332 expression levels predict clinical outcomes and chemotherapy response in patients with pancreatic adenocarcinoma

Poor outcomes and chemotherapy resistance for patients with pancreatic adenocarcinoma (PAAD) are a challenge worldwide, and new or improved prognostic biomarkers are urgently required. Individual laminin family members have been established as cancer-associated markers, predicting patient outcomes in many cancer types, including PAAD. Here, we used multiple modalities including RNAseq and gene chip, and genomic and proteomic data to examine the relationships of all laminin genes in PAAD with clinical outcomes. These analyses identified that LAMA3, LAMB3, and LAMC2 expression levels are increased at the mRNA and protein levels in PAAD tumours with evidence of co-regulation. Increased expression of all three genes was associated with decreased promoter methylation status, TP53 mutations, and altered receptor tyrosine kinase (RTK) pathways. Clinically, high LAMA3, LAMB3, and LAMC2 transcript abundance was each related to an advanced histological grade. Moreover, high expression of these genes individually predicted poor patient survival, while a signature of combined high expression of LAMA3, LAMB3, and LAMC2 was a stronger predictor of patient outcomes than each gene alone. Interestingly, cell lines with high expression of LM332 chains were not sensitive to the commonly used PAAD chemotherapy drugs paclitaxel and gemcitabine; however, increased sensitivity was evident for erlotinib, afatinib, gefitinib, and cetuximab epidermal growth factor (EGFR) RTK inhibitors. To explore possible mechanisms, we investigated co-expressed genes, identifying eight hub genes, namely, GJB3, ITGB6, SERPINB5, GPRC5A, PLEK2, TMPRSS4, P2RY2, and TRIM29, which are co-expressed with all three of LAMA3, LAMB3, and LAMC2. Of these, only SERPINB5 provided a stronger predictive value than the laminin-encoding genes. Together, these multiple integrated analyses suggest that the combined expression of LM332 is a useful prognostic biomarker for PAAD and could help patient stratification and therapeutic selection.

The immunomodulatory role of IDO1-Kynurenine-NAD+ pathway in switching cold tumor microenvironment in PDAC

Pancreatic ductal adenocarcinoma (PDAC) is the most common exocrine tumor of the pancreas characterized by late diagnosis, adverse overall 5-year survival, a higher propensity for metastatic disease, and lack of efficacy of systemic therapy options. These adverse outcomes can be partly attributed to complex tumor microenvironment (TME). Over the past decade, immunotherapy has revolutionized the management of certain cancers; thus far, the immunologically 'non-inflamed' tumor microenvironment in PDACs has proven to be challenging. Indolamine 2,3-dioxygenase 1 (IDO1) is the rate-limiting enzyme in the catabolic pathway of L-Tryptophan, an essential amino acid, that gives rise to the immunosuppressive metabolite Kynurenine. IDO1, Indolamine 2,3-dioxygenase 2 (IDO2), and Tryptophan 2,3-dioxygenase (TDO) are the key enzymes in the tryptophan catabolic pathway but we focus on the role of the predominant enzyme form IDO1 in this review. Nicotinamide phosphoribosyl transferase (iNAMPT) regulates the intracellular concentration of NAD and is upregulated in the tumor. In light of the potential role of IDO1 as a driver of hostile TME in PDAC and NAD⁺ as a key coenzyme in anti-tumor immune response, this review urges focus on extensive research and initiation of clinical trials using IDO1 and NAMPT inhibitors in pancreatic cancer in the future.

LAMC2 as a prognostic biomarker in human cancer: a systematic review and meta-analysis

OBJECTIVES

Accumulating evidence suggested that the laminin γ2 (LAMC2) expression level was upregulated in various cancers. However, the potential prognostic value of LAMC2 in cancers remains unclear. We conducted a meta-analysis to clarify the association of LAMC2 expression with prognosis.

DESIGN

We searched Embase, Web of Science and PubMed (up to 25 November 2021) to collect all eligible studies, and meta-analysis was performed to interpret the association of LAMC2 expression with clinicopathological parameters, overall survival (OS), disease-specific survival (DSS) and progression-free survival (PFS).

ELIGIBILITY CRITERIA FOR INCLUDING STUDIES

We included studies that investigate the relationship between LAMC2 and prognosis of cancers, patients were divided into two groups, and associations of LAMC2 expression with clinicopathological features were described.

RESULTS

Seven studies were finally included. We found that increased LAMC2 expression was significantly associated with lymph node metastasis (log OR 0.88, 95% CI 0.38 to 1.38, p<0.001), tumour-node-metastasis stages (log OR: 0.95, 95% CI 0.39 to 1.50, p<0.001) and tumour status (log OR 1.26, 95% CI 0.84 to 1.68, p<0.001), but not with age (log OR -0.05, 95% CI -0.37 to 0.27, p=0.75) or gender (log OR -0.07, 95% CI -0.52 to 0.38, p=0.75). In addition, higher LAMC2 expression was found to be significantly associated with OS/PFS/DSS (HR 1.85, 95% CI 1.31 to 2.40, p<0.001). A similar result was found in The Cancer Genome Atlas database. High LAMC2 expression was significantly associated with OS in lung adenocarcinoma, mesothelioma, skin cutaneous melanoma, neck squamous cell carcinoma and brain lower grade glioma.

CONCLUSION

Our results suggested that higher LAMC2 expression was correlated with worse survival, lymph node metastasis, tumour-node-metastasis stages and tumour status. This study was subject to inherent limitations, but the results presented here provide insights regarding the potential use of LAMC2 as a biomarker for human cancer.

STUDY REGISTRATION

researchregistry.com (researchregistry1319).

The Extracellular Matrix: A Key Accomplice of Cancer Stem Cell Migration, Metastasis Formation, and Drug Resistance in PDAC

Pancreatic ductal adenocarcinoma (PDAC) is rich in dense fibrotic stroma that are composed of extracellular matrix (ECM) proteins. A disruption of the balance between ECM synthesis and secretion and the altered expression of matrix remodeling enzymes lead to abnormal ECM dynamics in PDAC. This pathological ECM promotes cancer growth, survival, invasion, and alters the behavior of fibroblasts and immune cells leading to metastasis formation and chemotherapy resistance, which contribute to the high lethality of PDAC. Additionally, recent evidence highlights that ECM, as a major structural component of the tumor microenvironment, is a highly dynamic structure in which ECM proteins establish a physical and biochemical niche for cancer stem cells (CSCs). CSCs are characterized by self-renewal, tumor initiation, and resistance to chemotherapeutics. In this review, we will discuss the effects of the ECM on tumor biological behavior and its molecular impact on the fundamental signaling pathways in PDAC. We will also provide an overview of how the different ECM components are able to modulate CSCs properties and finally discuss the current and ongoing therapeutic strategies targeting the ECM. Given the many challenges facing current targeted therapies for PDAC, a better understanding of molecular events involving the interplay of ECM and CSC will be key in identifying more effective therapeutic strategies to eliminate CSCs and ultimately to improve survival in patients that are suffering from this deadly disease.

Pancreatic adenocarcinoma associated immune-gene signature as a novo risk factor for clinical prognosis prediction in hepatocellular carcinoma

Pancreatic adenocarcinoma (PAAD) has high mortality and a very poor prognosis. Both surgery and chemotherapy have a suboptimal therapeutic effect, and this caused a need to find new approaches such as immunotherapy. Therefore, it is essential to develop a new model to predict patient prognosis and facilitate early intervention. Our study screened out and validated the target molecules based on the TCGA-PAAD dataset. We established the risk signature using univariate and multivariate Cox regression analysis and used GSE62452 and GSE28735 to verify the accuracy and reliability of the model. Expanded application of PAAD-immune-related genes signature (-IRGS) on other datasets was conducted, and the corresponding nomograms were constructed. We also analyzed the correlation between immune-related cells/genes and potential treatments. Our research demonstrated that a high riskscore of PAAD-IRGS in patients with PAAD was correlated with poor overall survival, disease-specific survival and progression free interval. The same results were observed in patients with LIHC. The models constructed were confirmed to be accurate and reliable. We found various correlations between PAAD-IRGS and immune-related cells/genes, and the potential therapeutic agents. These findings indicate that PAAD-IRGS may be a promising indicator for prognosis and of the tumor-immune microenvironment status in PAAD.

lncRNAs Functioned as ceRNA to Sponge miR-15a-5p Affects the Prognosis of Pancreatic Adenocarcinoma and Correlates With Tumor Immune Infiltration

Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors with poor prognosis worldwide. Mounting evidence suggests that the expression of lncRNAs and the infiltration of immune cells have prognostic value for patients with PAAD. We used Gene Expression Omnibus (GEO) database and identified six genes (COL1A2, ITGA2, ITGB6, LAMA3, LAMB3, and LAMC2) that could affect the survival rate of pancreatic adenocarcinoma patients. Based on a series of in silico analyses for reverse prediction of target genes associated with the prognosis of PAAD, a ceRNA network of mRNA (COL1A2, ITGA2, LAMA3, LAMB3, and LAMC2)–microRNA (miR-15a-5p)–long non-coding RNA (LINC00511, LINC01578, PVT1, and TNFRSF14-AS1) was constructed. We used the algorithm “CIBERSORT” to assess the proportion of immune cells and found three overall survival (OS)–associated immune cells (monocytes, M1 macrophages, and resting mast cell). Moreover, the OS-associated gene level was significantly positively associated with immune checkpoint expression and biomarkers of immune cells. In summary, our results clarified that ncRNA-mediated upregulation of OS-associated genes and tumor-infiltration immune cells (monocytes, M1 macrophages M1, and resting mast cell resting) correlated with poor prognosis in PAAD.

Multiple roles for basement membrane proteins in cancer progression and EMT

Metastasis or the progression of malignancy poses a major challenge in cancer therapy and is the principal reason for increased mortality. The epithelial-Mesenchymal transition (EMT) of the Basement Membrane (BM) allows cells of epithelial phenotype to transform into a mesenchymal-like (quasi-mesenchymal) phenotype and metastasize via the lymphovascular system through a metastatic cascade by intravasation and extravasation. This helps in the progression of carcinoma from the primary site to distant organs. Collagen, laminin, and integrin are the prime components of BM and help in tumor cell metastasis, which makes them ideal cancer drug targets. Further, recent studies have shown that collagen, laminin, and integrin can be used as a biomarker for metastatic cells. In this review, we have summarized the current knowledge of such therapeutics, which are either currently in preclinical or clinical stages and could be promising cancer therapeutics. DATA AVAILABILITY: Not applicable.

Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer

Background

The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC.

Methods

The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort from GEO database were used for external validation.

Results

A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan–Meier analysis, age, T stage, N stage, AJCC stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-year, 2-year, and 3-year survival rates of PC patients. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves. 1-year, 2-year and 3-year AUC of nomogram model was 0.899, 0.765 and 0.776, respectively.

Conclusions

In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08097-w.

RUNX2 and LAMC2: promising pancreatic cancer biomarkers identified by an integrative data mining of pancreatic adenocarcinoma tissues

Pancreatic carcinoma (PC) is a severe disease associated with high mortality. Although strategies for cancer therapy have made great progress, outcomes of pancreatic carcinoma patients remain extremely poor. Therefore, it is urgent to find novel biomarkers and therapeutic targets. To identify biomarkers for early diagnosis and therapy, three mRNA microarray datasets and two miRNA datasets were selected, and combinative analysis was performed by GEO2R. Functional and pathway enrichment analysis were performed using DAVID database. MiRTarBase, miRWalk and Diana Tools were used to get key genes. TCGA, HPA and western blotting were used to verify diagnostic and prognostic value of key genes. By integrating mRNA and miRNA expression profiles, we identified 114 differentially expressed genes and 114 differentially expressed miRNAs, respectively. Then, three overlapping key genes, RUNX2, LAMC2 and FBXO32, were found. Their protein levels in pancreatic tissue from PC patients and normal people were analyzed by immunohistochemical staining and western blotting. RUNX2 showed a potential property to identify PC. Aberrant over-expression of LAMC2 was associated with poor prognosis of PC patients, tumor status and subtypes. In summary, our current study identified that RUNX2 and LAMC2 may be promising targets for early diagnosis and therapy of PC patients.

The Extracellular Matrix in Pancreatic Cancer: Description of a Complex Network and Promising Therapeutic Options

The stroma is a relevant player in driving and supporting the progression of pancreatic ductal adenocarcinoma (PDAC), and a large body of evidence highlights its role in hindering the efficacy of current therapies. In fact, the dense extracellular matrix (ECM) characterizing this tumor acts as a natural physical barrier, impairing drug penetration. Consequently, all of the approaches combining stroma-targeting and anticancer therapy constitute an appealing option for improving drug penetration. Several strategies have been adopted in order to target the PDAC stroma, such as the depletion of ECM components and the targeting of cancer-associated fibroblasts (CAFs), which are responsible for the increased matrix deposition in cancer. Additionally, the leaky and collapsing blood vessels characterizing the tumor might be normalized, thus restoring blood perfusion and allowing drug penetration. Even though many stroma-targeting strategies have reported disappointing results in clinical trials, the ECM offers a wide range of potential therapeutic targets that are now being investigated. The dense ECM might be bypassed by implementing nanoparticle-based systems or by using mesenchymal stem cells as drug carriers. The present review aims to provide an overview of the principal mechanisms involved in the ECM remodeling and of new promising therapeutic strategies for PDAC.

Whole genome sequencing identifies rare germline variants enriched in cancer related genes in first degree relatives of familial pancreatic cancer patients

First-degree relatives (FDRs) of familial pancreatic cancer (FPC) patients have increased risk of developing pancreatic ductal adenocarcinoma (PDAC). Investigating and understanding the genetic basis for PDAC susceptibility in FPC predisposed families may contribute toward future risk-assessment and management of high-risk individuals. Using a Danish cohort of 27 FPC families, we performed whole-genome sequencing of 61 FDRs of FPC patients focusing on rare genetic variants that may contribute to familial aggregation of PDAC. Statistical analysis was performed using the gnomAD database as external controls. Through analysis of heterozygous premature truncating variants (PTV), we identified cancer-related genes and cancer-driver genes harboring multiple germline mutations. Association analysis detected 20 significant genes with false discovery rate, q < 0.05 including: PALD1, LRP1B, COL4A2, CYLC2, ZFYVE9, BRD3, AHDC1, etc. Functional annotation showed that the significant genes were enriched by gene clusters encoding for extracellular matrix and associated proteins. PTV genes were over-represented by functions related to transport of small molecules, innate immune system, ion channel transport, and stimuli-sensing channels. In conclusion, FDRs of FPC patients carry rare germline variants related to cancer pathogenesis that may contribute to increased susceptibility to PDAC. The identified variants may potentially be useful for risk prediction of high-risk individuals in predisposed families.

Diarylpentanoid (1,5-bis(4-hydroxy-3-methoxyphenyl)-1,4-pentadiene-3-one) (MS13) Exhibits Anti-proliferative, Apoptosis Induction and Anti-migration Properties on Androgen-independent Human Prostate Cancer by Targeting Cell Cycle-Apoptosis and PI3K Signalling Pathways

Diarylpentanoids exhibit a high degree of anti-cancer activity and stability in vitro over curcumin in prostate cancer cells. Hence, this study aims to investigate the effects of a diarylpentanoid, 1,5-bis(4-hydroxy-3-methoxyphenyl)-1,4-pentadiene-3-one (MS13) on cytotoxicity, anti-proliferative, apoptosis-inducing, anti-migration properties, and the underlying molecular mechanisms on treated androgen-independent prostate cancer cells, DU 145 and PC-3. A cell viability assay has shown greater cytotoxicity effects of MS13-treated DU 145 cells (EC₅₀ 7.57 ± 0.2 µM) and PC-3 cells (EC₅₀ 7.80 ± 0.7 µM) compared to curcumin (EC₅₀: DU 145; 34.25 ± 2.7 µM and PC-3; 27.77 ± 6.4 µM). In addition, MS13 exhibited significant anti-proliferative activity against AIPC cells compared to curcumin in a dose- and time-dependent manner. Morphological observation, increased caspase-3 activity, and reduced Bcl-2 protein levels in these cells indicated that MS13 induces apoptosis in a time- and dose-dependent. Moreover, MS13 effectively inhibited the migration of DU 145 and PC-3 cells. Our results suggest that cell cycle-apoptosis and PI3K pathways were the topmost significant pathways impacted by MS13 activity. Our findings suggest that MS13 may demonstrate the anti-cancer activity by modulating DEGs associated with the cell cycle-apoptosis and PI3K pathways, thus inhibiting cell proliferation and cell migration as well as inducing apoptosis in AIPC cells.

Biomarkers in the diagnosis of pancreatic cancer: Are we closer to finding the golden ticket?

Pancreatic cancer (PC) is a leading cause of cancer related mortality on a global scale. The disease itself is associated with a dismal prognosis, partly due to its silent nature resulting in patients presenting with advanced disease at the time of diagnosis. To combat this, there has been an explosion in the last decade of potential candidate biomarkers in the research setting in the hope that a diagnostic biomarker may provide a glimmer of hope in what is otherwise quite a substantial clinical dilemma. Currently, serum carbohydrate antigen 19-9 is utilized in the diagnostic work-up of patients diagnosed with PC however this biomarker lacks the sensitivity and specificity associated with a gold-standard marker. In the search for a biomarker that is both sensitive and specific for the diagnosis of PC, there has been a paradigm shift towards a focus on liquid biopsy and the use of diagnostic panels which has subsequently proved to have efficacy in the diagnosis of PC. Currently, promising developments in the field of early detection on PC using diagnostic biomarkers include the detection of microRNA (miRNA) in serum and circulating tumour cells. Both these modalities, although in their infancy and yet to be widely accepted into routine clinical practice, possess merit in the early detection of PC. We reviewed over 300 biomarkers with the aim to provide an in-depth summary of the current state-of-play regarding diagnostic biomarkers in PC (serum, urinary, salivary, faecal, pancreatic juice and biliary fluid).

Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM–receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

High expression of LAMA3/AC245041.2 gene pair associated with KRAS mutation and poor survival in pancreatic adenocarcinoma: a comprehensive TCGA analysis

Background

Pancreatic adenocarcinoma (PAAD) is one of the most challenging cancers with high morbidity and mortality. KRAS mutations could occur as an early event in PAAD. The present study aimed to identify the differentially expressed lncRNAs (DE-lncRNAs) and differentially expressed mRNAs (DE-mRNAs) in KRAS-mutant PAAD to explore the pathogenesis and the underlying molecular mechanism of PAAD development.

Methods

Clinical data of TCGA–PAAD patients were downloaded from the TCGA database and subjected to survival analysis along with the KRAS mutation information data. Weighted gene correlation network analysis (WGCNA) and univariate Cox regression analysis were conducted to construct prognostic risk models to identify the hub DE-mRNAs and DE-lncRNAs associated with PAAD prognosis. GO and KEGG enrichment analyses of the identified hub DE-mRNAs were performed. Multivariate cox regression analysis was performed to analyze the overall prognosis of age, gender, pathologic_T, and KRAS mutations, following which the differences in the clinical characteristics of risk score1 and risk score2 were analyzed. Finally, the mRNAs–lncRNA–TFs regulatory network was constructed.

Results

Functional enrichment analysis was performed after screening 1671 DE-mRNAs and 324 DE-lncRNAs. It was observed that the associated pathways were enriched mainly in the modulation of chemical synaptic transmission, synaptic membrane, ion-gated channel activity, ligand−receptor interactions that stimulate neural tissue, among others. The univariate Cox regression analysis screened 117 mRNAs and 36 lncRNAs, and the risk ratio models of the mRNAs and lncRNAs were constructed. LAMA3 (mRNA) and AC245041.2 (lncRNA) exhibited a strong expression correlation in the respective two risk models. The genes in the samples with a high expression of these two genes were enriched in several pathways associated with transcription factors (TFs), among which the TFs ATF5, CSHL1, NR1I2, SIPA1, HOXC13, HSF2, and HOXA10 were shared by the two groups. The core enrichment genes in the common TF pathways were collated, and the mRNAs–lncRNAs–TFs regulatory network was constructed.

Conclusion

In the present study, novel prognostic mRNAs and lncRNAs were identified, and their respective prognostic models and nomograms were constructed to guide clinical practice. An mRNAs–lncRNAs–TFs regulatory network was also constructed, which could assist further research in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-021-00322-2.

Discovery and Validation of Circulating EVL mRNA as a Prognostic Biomarker in Pancreatic Cancer

Background

Circulating plasma mRNAs can be analyzed to identify putative cancer biomarkers. This study was conducted in an effort to detect plasma mRNA biomarkers capable of predicting pancreatic cancer (PACA) patient prognosis. Material and Methods. First, prognostic mRNAs that were differentially expressed in PACA in The Cancer Genome Atlas (TCGA) were established, after which microarray expression profiles from PACA patient plasma samples were utilized to specifically identify potential prognostic plasma mRNA biomarkers associated with this cancer type. In total, plasma samples were then collected from 79 PACA patients and 19 healthy controls to confirm differential mRNA expression via qPCR, while Kaplan–Meier analyses were used to examine the link between mRNA expression and patient overall survival.

Results

In total, three prognostic differentially expressed genes were identified in PACA patient plasma samples, including SMAP2, PTPN6, and EVL (Ena/VASP-like). Plasma EVL levels were confirmed via qPCR to be correlated with tumor pathology (p < 0.01), while the overall survival of patients with low plasma EVL levels was poor (p < 0.01). Multivariate Cox regression analyses further confirmed that plasma EVL levels were independent predictors of PACA patient prognosis.

Conclusion

We found that PACA is associated with the downregulation of plasma EVL mRNA levels, indicating that this mRNA may be a viable biomarker associated with patient prognosis.

Stroma Involvement in Pancreatic Ductal Adenocarcinoma: An Overview Focusing on Extracellular Matrix Proteins

Pancreatic cancer is the seventh leading cause of cancer-related deaths worldwide and is predicted to become second in 2030 in industrialized countries if no therapeutic progress is made. Among the different types of pancreatic cancers, Pancreatic Ductal Adenocarcinoma (PDAC) is by far the most represented one with an occurrence of more than 90%. This specific cancer is a devastating malignancy with an extremely poor prognosis, as shown by the 5-years survival rate of 2–9%, ranking firmly last amongst all cancer sites in terms of prognostic outcomes for patients. Pancreatic tumors progress with few specific symptoms and are thus at an advanced stage at diagnosis in most patients. This malignancy is characterized by an extremely dense stroma deposition around lesions, accompanied by tissue hypovascularization and a profound immune suppression. Altogether, these combined features make access to cancer cells almost impossible for conventional chemotherapeutics and new immunotherapeutic agents, thus contributing to the fatal outcomes of the disease. Initially ignored, the Tumor MicroEnvironment (TME) is now the subject of intensive research related to PDAC treatment and could contain new therapeutic targets. In this review, we will summarize the current state of knowledge in the field by focusing on TME composition to understand how this specific compartment could influence tumor progression and resistance to therapies. Attention will be paid to Tenascin-C, a matrix glycoprotein commonly upregulated during cancer that participates to PDAC progression and thus contributes to poor prognosis.

Identification of laminin γ2 as a prognostic and predictive biomarker for determining response to gemcitabine-based therapy in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies. While the extracellular matrix component plays an integral role in PDAC pathogenesis and mediating chemoresistance, its role in predicting response to chemotherapy in patients with PDAC remains unclear.

METHODS

We performed a systematic biomarker discovery by analysing genome-wide transcriptomic profiling data from 423 patients (GSE71729, GSE21501 and The Cancer Genome Atlas [TCGA]) for predicting overall survival (OS). This was subsequently validated in two independent clinical cohorts of 270 patients with PDAC (training cohort, n = 121, and validation cohort, n = 149). In addition, we investigated endoscopic ultrasound-fine needle aspiration biopsy specimens from 51 patients with PDAC with an unresectable cancer for predicting therapeutic response to gemcitabine-based therapy.

RESULTS

After rigorous bioinformatic analysis, we identified laminin γ2 (LAMC2) to be a significant prognostic factor in all three PDAC data sets (GSE71729: hazard ratio [HR] = 2.04, P = 0.002; GSE21501: HR = 2.17, P = 0.031; TCGA: HR = 2.57, P < 0.001). High LAMC2 expression in patients with PDAC was associated with a significantly poor OS and relapse-free survival in both the training (P < 0.001, P < 0.001) and validation cohorts (P = 0.001, P = 0.026). More importantly, LAMC2 expression robustly identified patients with PDAC and unresectable disease and those who responded to gemcitabine-based therapy (area under the curve = 0.79; 95% confidence interval [CI], 0.65-0.89). The univariate logistic regression analysis revealed that high LAMC2 expression was the only factor that predicted poor response to gemcitabine in patients with PDAC (odds ratio = 4.90; 95% CI, 1.45-16.6; P = 0.011).

CONCLUSION

We conclude that LAMC2 is a novel prognostic and predictive biomarker for gemcitabine-based therapy in both the adjuvant and palliative setting; which could have significant impact on precision and individualised treatment of patients with PDAC.

Early Diagnosis of Pancreatic Ductal Adenocarcinoma by Combining Relative Expression Orderings With Machine-Learning Method

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal cancer deeply affecting human health. Diagnosing early-stage PDAC is the key point to PDAC patients' survival. However, the biomarkers for diagnosing early PDAC are inexact in most cases. Therefore, it is highly desirable to identify an effective PDAC diagnostic biomarker. In the current work, we designed a novel computational approach based on within-sample relative expression orderings (REOs). A feature selection technique called minimum redundancy maximum relevance was used to pick out optimal REOs. We then compared the performances of different classification algorithms for discriminating PDAC and its adjacent normal tissues from non-PDAC tissues. The support vector machine algorithm is the best one for identifying early PDAC diagnostic biomarker. At first, a signature composed of nine gene pairs was acquired from microarray gene expression data sets. These gene pairs could produce satisfactory classification accuracy up to 97.53% in fivefold cross-validation. Subsequently, two types of data from diverse platforms, namely, microarray and RNA-Seq, were used to validate this signature. For microarray data, all (100.00%) of 115 PDAC tissues and all (100.00%) of 31 PDAC adjacent normal tissues were correctly recognized as PDAC. In addition, 88.24% of 17 non-PDAC (normal or pancreatitis) tissues were correctly classified. For the RNA-Seq data, all (100.00%) of 177 PDAC tissues and all (100.00%) of 4 PDAC adjacent normal tissues were correctly recognized as PDAC. Validation results demonstrated that the signature had a good cross-platform effect for early detection of PDAC. This work developed a new robust signature that might be a promising biomarker for early PDAC diagnosis.

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

BACKGROUND

Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment.

METHODS

We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples.

RESULTS

This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1.

CONCLUSION

The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.