Laminin, gamma 2 (LAMC2): a promising new putative pancreatic cancer biomarker identified by proteomic analysis of pancreatic adenocarcinoma tissues

Knockdown of ribosome RNA processing protein 15 suppresses migration of hepatocellular carcinoma through inhibiting PATZ1-associated LAMC2/FAK pathway

BACKGROUND

Ribosomal RNA processing protein 15 (RRP15) has been found to regulate the progression of hepatocellular carcinoma (HCC). Nevertheless, the extent to which it contributes to the spread of HCC cells remains uncertain. Thus, the objective of this research was to assess the biological function of RRP15 in the migration of HCC.

METHODS

The expression of RRP15 in HCC tissue microarray (TMA), tumor tissues and cell lines were determined. In vitro, the effects of RRP15 knockdown on the migration, invasion and adhesion ability of HCC cells were assessed by wound healing assay, transwell and adhesion assay, respectively. The effect of RRP15 knockdown on HCC migration was also evaluated in vivo in a mouse model.

RESULTS

Bioinformatics analysis showed that high expression of RRP15 was significantly associated with low survival rate of HCC. The expression level of RRP15 was strikingly upregulated in HCC tissues and cell lines compared with the corresponding controls, and TMA data also indicated that RRP15 was a pivotal prognostic factor for HCC. RRP15 knockdown in HCC cells reduced epithelial-to-mesenchymal transition (EMT) and inhibited migration in vitro and in vivo, independent of P53 expression. Mechanistically, blockade of RRP15 reduced the protein level of the transcription factor POZ/BTB and AT hook containing zinc finger 1 (PATZ1), resulting in decreased expression of the downstream genes encoding laminin 5 subunits, LAMC2 and LAMB3, eventually suppressing the integrin β4 (ITGB4)/focal adhesion kinase (FAK)/nuclear factor κB kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway.

CONCLUSIONS

RRP15 promotes HCC migration by activating the LAMC2/ITGB4/FAK pathway, providing a new target for future HCC treatment.

Desmoglein-2 as a cancer modulator: friend or foe?

Desmoglein-2 (DSG2) is a calcium-binding single pass transmembrane glycoprotein and a member of the large cadherin family. Until recently, DSG2 was thought to only function as a cell adhesion protein embedded within desmosome junctions designed to enable cells to better tolerate mechanical stress. However, additional roles for DSG2 outside of desmosomes are continuing to emerge, particularly in cancer. Herein, we review the current literature on DSG2 in cancer and detail its impact on biological functions such as cell adhesion, proliferation, migration, invasion, intracellular signaling, extracellular vesicle release and vasculogenic mimicry. An increased understanding of the diverse repertoire of the biological functions of DSG2 holds promise to exploit this cell surface protein as a potential prognostic biomarker and/or target for better patient outcomes. This review explores the canonical and non-canonical functions of DSG2, as well as the context-dependent impacts of DSG2 in the realm of cancer.

Secreted protein profiling of human aortic smooth muscle cells identifies vascular disease associations

Background

Smooth muscle cells (SMCs), which make up the medial layer of arteries, are key cell types involved in cardiovascular diseases (CVD), the leading cause of mortality and morbidity worldwide. In response to microenvironment alterations, SMCs dedifferentiate from a "contractile" to a "synthetic" phenotype characterized by an increased proliferation, migration, production of extracellular matrix (ECM) components, and decreased expression of SMC-specific contractile markers. These phenotypic changes result in vascular remodeling and contribute to the pathogenesis of CVD, including coronary artery disease (CAD), stroke, hypertension, and aortic aneurysms. Here, we aim to identify the genetic variants that regulate ECM secretion in SMCs and predict the causal proteins associated with vascular disease-related loci identified in genome-wide association studies (GWAS).

Methods

Using human aortic SMCs from 123 multi-ancestry healthy heart transplant donors, we collected the serum-free media in which the cells were cultured for 24 hours and conducted Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic analysis of the conditioned media.

Results

We measured the abundance of 270 ECM and related proteins. Next, we performed protein quantitative trait locus mapping (pQTL) and identified 20 loci associated with secreted protein abundance in SMCs. We functionally annotated these loci using a colocalization approach. This approach prioritized the genetic variant rs6739323-A at the 2p22.3 locus, which is associated with lower expression of LTBP1 in SMCs and atherosclerosis-prone areas of the aorta, and increased risk for SMC calcification. We found that LTBP1 expression is abundant in SMCs, and its expression at mRNA and protein levels was reduced in unstable and advanced atherosclerotic plaque lesions.

Conclusions

Our results unravel the SMC proteome signature associated with vascular disorders, which may help identify potential therapeutic targets to accelerate the pathway to translation.

Using Proteome Microarray and Gene Expression Omnibus Database to Screen Tumour-Associated Antigens to Construct the Optimal Diagnostic Model of Oesophageal Squamous Cell Carcinoma

AIMS

Autoantibodies against tumour-associated antigens (TAAs) are promising biomarkers for early immunodiagnosis of cancers. This study was designed to screen and verify autoantibodies against TAAs in sera as diagnostic biomarkers for oesophageal squamous cell carcinoma (ESCC).

MATERIALS AND METHODS

The customised proteome microarray based on cancer driver genes and the Gene Expression Omnibus database were used to identify potential TAAs. The expression levels of the corresponding autoantibodies in serum samples obtained from 243 ESCC patients and 243 healthy controls were investigated by enzyme-linked immunosorbent assay (ELISA). In total, 486 serum samples were randomly divided into the training set and the validation set in the ratio of 2:1. Logistic regression analysis, recursive partition analysis and support vector machine were performed to establish different diagnostic models.

RESULTS

Five and nine candidate TAAs were screened out by proteome microarray and bioinformatics analysis, respectively. Among these 14 anti-TAAs autoantibodies, the expression level of nine (p53, PTEN, GNA11, SRSF2, CXCL8, MMP1, MSH6, LAMC2 and SLC2A1) anti-TAAs autoantibodies in the cancer patient group was higher than that in the healthy control group based on the results from ELISA. In the three constructed models, a logistic regression model including four anti-TAA autoantibodies (p53, SLC2A1, GNA11 and MMP1) was considered to be the optimal diagnosis model. The sensitivity and specificity of the model in the training set and the validation set were 70.4%, 72.8% and 67.9%, 67.9%, respectively. The area under the receiver operating characteristic curve for detecting early patients in the training set and the validation set were 0.84 and 0.85, respectively.

CONCLUSIONS

This approach to screen novel TAAs is feasible, and the model including four autoantibodies could pave the way for the diagnosis of ESCC.

Engineered TCR T-cell therapy targeting mass spectrometry-identified natural epitope in PDAC

Tumor antigens are crucial targets for T-cell-based therapy to induce tumor-specific rejection. However, identifying pancreatic ductal adenocarcinoma (PDAC)-specific T-cell epitopes has been challenging. Using advanced mass spectrometry (MS) analysis, we previously identified cancer-associated, class I MHC-bound epitopes shared by multiple PDAC patients with different HLA-A types. Here, we investigated one of these epitopes, LAMC2203-211, a naturally occurring nonmutated epitope on the LAMC2 protein. Following stimulation with the LAMC2203-211 peptide, we cloned T-cell receptors (TCRs) and transduced them into the Jurkat human T-cell line using a lentiviral vector. We found that Jurkat cells expressing LAMC2203-211-specific TCRs resulted in potent, LAMC2 specific, in vitro cytotoxic effects on PDAC cells. Furthermore, in mice that harbored either subcutaneously or orthotopically implanted tumors originating from both HLA-A allele-matched and unmatched PDAC patients, tumor growth was suppressed in a LAMC2-dependent manner following the infusion of LAMC2-targeting T cells. We have therefore developed a LAMC2-specific TCR-based T-cell therapy strategy likely suitable for many PDAC patients. This is the first study to adopt MS analysis to identify natural CD8+ T-cell epitopes in PDAC that could potentially serve as targets for PDAC immunotherapy.

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.

S100A10 Promotes Pancreatic Ductal Adenocarcinoma Cells Proliferation, Migration and Adhesion through JNK/LAMB3-LAMC2 Axis

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive tumors, characterized by diagnosis at an advanced stage and a poor prognosis. As a member of the S100 protein family, S100A10 regulates multiple biological functions related to cancer progression and metastasis. However, the role of S100A10 in PDAC is still not completely elucidated. In this study, we reported that S100A10 was significantly up-regulated in PDAC tissue and associated with a poor prognosis by integrated bioinformatic analysis and human PDAC tissue samples. In vitro, down-regulation of S100A10 reduced the proliferation, migration, and adhesion of PDAC cell lines, whereas up-regulation of S100A10 showed the opposite effect. Furthermore, LAMB3 was proved to be activated by S100A10 using RNA-sequencing and western blotting. The effect of LAMB3 on the proliferation, migration, and adhesion of PDAC cells was similar to that of S100A10. Up-regulation or down-regulation of LAMB3 could reverse the corresponding effect of S100A10. Moreover, we validated S100A10 activates LAMB3 through the JNK pathway, and LAMB3 was further proved to interact with LAMC2. Mice-bearing orthotopic pancreatic tumors showed that S100A10 knocked-down PANC-1 cells had a smaller tumor size than the control group. In conclusion, S100A10 promotes PDAC cells proliferation, migration, and adhesion through JNK/LAMB3-LAMC2 axis.

LAMC2 marks a tumor-initiating cell population with an aggressive signature in pancreatic cancer

BACKGROUND

Tumor-initiating cells (TIC), also known as cancer stem cells, are considered a specific subpopulation of cells necessary for cancer initiation and metastasis; however, the mechanisms by which they acquire metastatic traits are not well understood.

METHODS

LAMC2 transcriptional levels were evaluated using publicly available transcriptome data sets, and LAMC2 immunohistochemistry was performed using a tissue microarray composed of PDAC and normal pancreas tissues. Silencing and tracing of LAMC2 was performed using lentiviral shRNA constructs and CRISPR/Cas9-mediated homologous recombination, respectively. The contribution of LAMC2 to PDAC tumorigenicity was explored in vitro by tumor cell invasion, migration, sphere-forming and organoids assays, and in vivo by tumor growth and metastatic assays. mRNA sequencing was performed to identify key cellular pathways upregulated in LAMC2 expressing cells. Metastatic spreading induced by LAMC2- expressing cells was blocked by pharmacological inhibition of transforming growth factor beta (TGF-β) signaling.

RESULTS

We report a LAMC2-expressing cell population, which is endowed with enhanced self-renewal capacity, and is sufficient for tumor initiation and differentiation, and drives metastasis. mRNA profiling of these cells indicates a prominent squamous signature, and differentially activated pathways critical for tumor growth and metastasis, including deregulation of the TGF-β signaling pathway. Treatment with Vactosertib, a new small molecule inhibitor of the TGF-β type I receptor (activin receptor-like kinase-5, ALK5), completely abrogated lung metastasis, primarily originating from LAMC2-expressing cells.

CONCLUSIONS

We have identified a highly metastatic subpopulation of TICs marked by LAMC2. Strategies aimed at targeting the LAMC2 population may be effective in reducing tumor aggressiveness in PDAC patients. Our results prompt further study of this TIC population in pancreatic cancer and exploration as a potential therapeutic target and/or biomarker.

Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including 'stress', 'interferon response', 'epithelial-mesenchymal transition', 'metal response', 'basal' and 'ciliated'. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

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.

Oncogenic KRAS-Induced Protein Signature in the Tumor Secretome Identifies Laminin-C2 and Pentraxin-3 as Useful Biomarkers for the Early Diagnosis of Pancreatic Cancer

KRAS mutations characterize pancreatic cell transformation from the earliest stages of carcinogenesis, and are present in >95% of pancreatic ductal adenocarcinoma (PDAC) cases. In search of novel biomarkers for the early diagnosis of PDAC, we identified the proteins secreted by the normal human pancreatic cell line (HPDE) recently transformed by inducing the overexpression of the KRASG12V oncogene. We report a proteomic signature of KRAS-induced secreted proteins, which was confirmed in surgical tumor samples from resected PDAC patients. The putative diagnostic performance of three candidates, Laminin-C2 (LAMC2), Tenascin-C (TNC) and Pentraxin-3 (PTX3), was investigated by ELISA quantification in two cohorts of PDAC patients (n = 200) eligible for surgery. Circulating levels of LAMC2, TNC and PTX3 were significantly higher in PDAC patients compared to the healthy individuals (p < 0.0001). The Receiver Operating Characteristics (ROC) curve showed good sensitivity (1) and specificity (0.63 and 0.85) for LAMC2 and PTX3, respectively, but not for TNC, and patients with high levels of LAMC2 had significantly shorter overall survival (p = 0.0007). High levels of LAMC2 and PTX3 were detected at early stages (I−IIB) and in CA19-9-low PDAC patients. In conclusion, pancreatic tumors release LAMC2 and PTX3, which can be quantified in the systemic circulation, and may be useful in selecting patients for further diagnostic imaging.

Serum DSG2 as a potential biomarker for diagnosis of esophageal squamous cell carcinoma and esophagogastric junction adenocarcinoma

BACKGROUND

Exploration of serum biomarkers for early detection of upper gastrointestinal cancer is required. Here, we aimed to evaluate the diagnostic potential of serum desmoglein-2 (DSG2) in patients with esophageal squamous cell carcinoma (ESCC) and esophagogastric junction adenocarcinoma (EJA).

METHODS

Serum DSG2 levels were measured by enzyme-linked immunosorbent assay (ELISA) in 459 participants including 151 patients with ESCC, 96 with EJA, and 212 healthy controls. Receiver operating characteristic (ROC) curves were used to evaluate diagnostic accuracy.

RESULTS

Levels of serum DSG2 were significantly higher in patients with ESCC and EJA than those in healthy controls (P<0.001). Detection of serum DSG2 demonstrated an area under the ROC curve (AUC) value of 0.724, sensitivity of 38.1%, and specificity of 84.8% for the diagnosis of ESCC in the training cohort, and AUC 0.736, sensitivity 58.2%, and specificity 84.7% in the validation cohort. For diagnosis of EJA, measurement of DSG2 provided a sensitivity of 29.2%, a specificity of 90.2%, and AUC of 0.698. Similar results were observed for the diagnosis of early-stage ESCC (AUC 0.715 and 0.722, sensitivity 36.3 and 50%, and specificity 84.8 and 84.7%, for training and validation cohorts, respectively) and early-stage EJA (AUC 0.704, sensitivity 44.4%, and specificity 86.9%). Analysis of clinical data indicated that DSG2 levels were significantly associated with patient age and histological grade in ESCC (P<0.05).

CONCLUSION

Serum DSG2 may be a diagnostic biomarker for ESCC and EJA.

Development and validation of prognostic and diagnostic model for pancreatic ductal adenocarcinoma based on scRNA-seq and bulk-seq datasets

The 5-year overall survival (OS) of pancreatic ductal adenocarcinoma (PDAC) is only 10%, partly owing to the lack of reliable diagnostic and prognostic biomarkers. The raw gene-cell matrix for single-cell RNA-seq (scRNA-seq) analysis was downloaded from the GSA database. We drew cell atlas for PDAC and normal pancreatic tissues. The inferCNV analysis was used to distinguish tumor cells from normal ductal cells. We identified differential expression genes (DEGs) by comparing tumor cells and normal ductal cells. The common DEGs were used to conduct prognostic and diagnostic model using univariate and multivariate Cox or logistic regression analysis. Four genes, MET, KLK10, PSMB9 and ITGB6, were utilized to create risk score formula to predict OS and to establish diagnostic model for PDAC. Finally, we drew an easy-to-use nomogram to predict 2-year and 3-year OSs. In conclusion, we developed and validated the prognostic and diagnostic model for PDAC based on scRNA-seq and bulk-seq datasets.

Novel insight into pancreatic adenocarcinoma pathogenesis using liquid association analysis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy associated with a poor prognosis. High-throughput disease-related-gene expression data provide valuable information on gene interaction, which consequently lead to deeper insight about pathogenesis. The co-expression analysis is a common approach that is used to investigate gene interaction. However, such an approach solely is inadequate to reveal the complexity of the gene interaction. The three-way interaction model is known as a novel approach applied to decode the complex relationship between genes.

METHODS

In the current study, the liquid association method was used to capture the statistically significant triplets involved in the PDAC pathogenesis. Subsequently, gene set enrichment and gene regulatory network analyses were performed to trace the biological relevance of the statistically significant triplets.

RESULTS

The results of the current study suggest that "response to estradiol" and "Regulation of T-cell proliferation" are two critical biological processes that may be associated with the PDAC pathogenesis. Additionally, we introduced six switch genes, namely Lamc2, Klk1, Nqo1, Aox1, Tspan1, and Cxcl12, which might be involved in PDAC triggering.

CONCLUSION

In the current study, for the first time, the critical genes and pathways involved in the PDAC pathogenesis were investigated using the three-way interaction approach. As a result, two critical biological processes, as well as six potential biomarkers, were suggested that might be involved in the PDAC triggering. Surprisingly, strong evidence for the biological relevance of our results can be found in the literature.

Prognostic utility of the ovarian cancer secretome: a systematic investigation

BACKGROUND

Ovarian cancer is usually detected at an advanced stage with frequent recurrence. The recurrence-free survival and overall survival is influenced by the age at diagnosis, tumor stage and histological subtype. Nonetheless, quantifiable prognostic biomarkers are needed for early identification of the high-risk patients and for personalized medicine. Several studies link tumor-specific dysregulated expression of certain proteins with ovarian cancer prognosis. However, careful investigation of presence of these prognostically relevant proteins in ovarian cancer secretome is lacking.

OBJECTIVE

To critically analyze the recent published data on prognostically relevant proteins for ovarian cancer and to carefully search how many of them are reported in the published ovarian cancer secretome datasets.

DESIGN

A search for relevant studies in the past 2 years was conducted in PubMed and a comprehensive list of proteins associated with the ovarian cancer prognosis was prepared. These were cross-referred to the published ovarian cancer secretome profiles. The proteins identified in the secretome were further shortlisted based on a scoring strategy employing stringent criteria.

RESULTS

A panel of seven promising secretory biomarkers associated with ovarian cancer prognosis is proposed.

CONCLUSION

Scanning the ovarian cancer secretome datasets provides the opportunity to identify if tumor-specific biomarkers could be tested as secretory biomarkers. Detecting their levels in the body fluid would be more advantageous than evaluating the expression in the tissue, since it could be monitored multiple times over the course of the disease to have a better judgment of the prognosis and response to therapy.

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.

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).

Desmoglein-2 expression is an independent predictor of poor prognosis patients with multiple myeloma

Multiple myeloma (MM) is the second most common haematological malignancy and is an incurable disease of neoplastic plasma cells (PC). Newly diagnosed MM patients currently undergo lengthy genetic testing to match chromosomal mutations with the most potent drug/s to decelerate disease progression. With only 17% of MM patients surviving 10‐years postdiagnosis, faster detection and earlier intervention would unequivocally improve outcomes. Here, we show that the cell surface protein desmoglein‐2 (DSG2) is overexpressed in ~ 20% of bone marrow biopsies from newly diagnosed MM patients. Importantly, DSG2 expression was strongly predictive of poor clinical outcome, with patients expressing DSG2 above the 70^th percentile exhibiting an almost 3‐fold increased risk of death. As a prognostic factor, DSG2 is independent of genetic subtype as well as the routinely measured biomarkers of MM activity (e.g. paraprotein). Functional studies revealed a nonredundant role for DSG2 in adhesion of MM PC to endothelial cells. Together, our studies suggest DSG2 to be a potential cell surface biomarker that can be readily detected by flow cytometry to rapidly predict disease trajectory at the time of diagnosis.

Qualitative diagnostic signature for pancreatic ductal adenocarcinoma based on the within-sample relative expression orderings

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) accounts for about 90% of pancreatic cancer, which is one of the most aggressive malignant neoplasms with a 9.3% five-year survival rate. The pathological biopsy is the current golden standard for confirming suspicious lesions of PDAC, but it is not entirely reliable because of the insufficient sampling amount and inaccurate sampling location. Therefore, developing a robust signature to aid the accurate diagnosis of PDAC is critical.

METHODS

Based on the within-sample relative expression orderings of gene pairs, we identified a qualitative signature to discriminate both PDAC and adjacent samples from both chronic pancreatitis and normal samples in the training datasets and validated it in other independent datasets produced by different laboratories with different measuring platforms.

RESULTS

A six-gene-pair signature was identified in the training data and validated in eight independent datasets. For surgical samples, 96.63% of 356 PDAC tissues, 100% of 11 pancreatitis tissues of non-cancer patients, and 23 of 24 normal pancreatic tissues were correctly classified. Especially, 59 of 60 cancer-adjacent normal tissues of PDAC patients were correctly identified as PDAC. For biopsy samples, all of 11 PDAC biopsy tissues were correctly classified as PDAC.

CONCLUSION

The signature can distinguish both PDAC and PDAC-adjacent normal tissues from both chronic pancreatitis and normal tissues of non-cancer patients even when the sampling locations are inaccurate, which can aid the diagnosis of PDAC.

LAMC2 promotes cancer progression and gemcitabine resistance through modulation of EMT and ATP-binding cassette transporters in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor prognosis. Gemcitabine remains an effective option for the majority of PDAC patients. Unfortunately, currently no reliable prognostic and predictive biomarkers of therapeutic response are available for the patients with PDAC. Laminin γ2 (LAMC2) is overexpressed in several cancers, and its high expression facilitates cancer development and chemoresistance. However, its functional role in PDAC remains unclear, and a better understanding of this will likely help improve the prognosis of PDAC patients. This study aimed to elucidate the clinical and biological role of LAMC2 in PDAC. We first analyzed the expression levels of LAMC2 by real-time reverse transcription PCR in a cohort of 114 PDAC patients. Interestingly, higher expression of LAMC2 significantly correlated with poor survival in PDAC cohort. In addition, elevated LAMC2 expression served as a potential prognostic marker for survival. Subsequently, functional characterization for the role of LAMC2 in PDAC was performed by small interfering RNA knockdown in pancreatic cancer (PC) cell lines. Interestingly, inhibition of LAMC2 in PC cells enhanced the gemcitabine sensitivity and induction of apoptosis. Moreover, it inhibited colony formation ability, migration and invasion potential. Furthermore, LAMC2 regulated the expression of epithelial-mesenchymal transition (EMT) phenotype. In addition, LAMC2 significantly correlated with genes associated with the expression of ATP-binding cassette (ABC) transporters in PC cells and PDAC patients. In conclusion, these results suggest that LAMC2 regulates gemcitabine sensitivity through EMT and ABC transporters in PDAC and may be a novel therapeutic target in PDAC patients.

hsa‑miR‑5580‑3p inhibits oral cancer cell viability, proliferation and migration by suppressing LAMC2

The present study aimed to explore whether and how microRNA-5580-3p (miR-5580-3p) affected oral cancer (OC) cell phenotypes via regulation of laminin subunit γ2 (LAMC2). Bioinformatics analysis was used to identify miR-5580-3p/LAMC2, a novel interactome that, to the best of our knowledge, has not been studied previously in OC. In the present study, the expression levels of miR-5580-3p and LAMC2 were detected by reverse transcription-quantitative PCR, while the protein expression levels of LAMC2 were identified using western blotting. To determine the effects of miR-5580-3p and LAMC2 in OC, a number of experiments, including Cell Counting Kit-8, 5-bromo-2′-deoxyuridine cell proliferation and wound healing migration assays, were performed using OC SCC-4 and Cal-27 cell lines. Additionally, luciferase reporter assays were employed to examine the interaction between miR-5580-3p and LAMC2 mRNA. The results demonstrated that miR-5580-3p expression was downregulated, while LAMC2 expression was upregulated in OC tissues and cell lines. In addition to the observation that miR-5580-3p promoted the malignant phenotypes of OC, it was also revealed that miR-5580-3p inhibited OC cell viability, proliferation and migration by suppressing LAMC2. Therefore, the present study suggested that miR-5580-3p and LAMC2 may be potential biomarkers and therapeutic targets for OC diagnosis and therapies in the future.

Review of clinical and emerging biomarkers for early diagnosis and treatment management of pancreatic cancer: towards personalised medicine

Background:

Pancreatic cancer is the 12th most commonly diagnosed cancer and the 3rd leading cause of cancer mortality and accounts for approximately 2·7% of all newly diagnosed cancer cases and 6·4% of all cancer mortalities in Canada. It has a very poor survival rate mainly due to the difficulty of detecting the disease at an early stage. Consequently, in the advancement of disease management towards the concept of precision medicine that takes individual patient variabilities into account, several investigators have focused on the identification of effective clinical biomarkers with high specificity and sensitivity, capable of early diagnosis of symptomatic patients and early detection of the disease in asymptomatic individuals at high risk for developing pancreatic cancer.

Materials and methods:

We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on biomarkers for the management of pancreatic cancer. In this narrative review paper, we describe 13 clinical and emerging biomarkers for pancreatic cancers used in screening for early detection and diagnosis, to identify patients’ risk for metastatic disease and subsequent relapse, to monitor patient response to specific treatment and to provide clinicians the possibility of prospectively identifying groups of patients who will benefit from a particular treatment.

Conclusions:

Current and emerging biomarkers for pancreatic cancer with high specificity and sensitivity has the potential to account for individual patient variabilities, for early detection of disease before the onset of metastasis to improve treatment outcome and patients’ survival, help screen high-risk populations, predict prognosis, provide accurate information of patient response to specific treatment and improve patients monitoring during treatment. Thus, the future holds promise for the use of effective clinical biomarkers or a panel of biomarkers for personalised patient-specific targeted medicine for pancreatic cancer.

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.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.

Establishment and Investigation of a Multiple Gene Expression Signature to Predict Long-Term Survival in Pancreatic Cancer

Pancreatic cancer remains a lethal type of cancer with poor prognosis. Molecular classification enables in-depth, precise prognostic assessment. This study is aimed at identifying a robust and simple mRNA signature to predict the overall survival (OS) of pancreatic cancer (PC) patients. Differentially expressed genes (DEGs) between 45 paired pancreatic tumor samples and adjacent healthy tissues were selected. For risk determination, a LASSO Cox regression model with DEGs was used to generate the OS-associated risk score formula for the training cohort containing 177 PC patients. Another five independent datasets were used as the testing cohort to determine the predictive efficiency for further validation. In total, 441 DEGs were selected after considering the enrichment of classical pathways, such as EMT, cell cycle, cell adhesion, and PI3K-AKT. A five-gene signature for risk discrimination was established with high efficacy using LASSO Cox regression in the training group. External validation showed that patients identified by the gene expression signature to be in the high-risk group had poorer prognosis compared with the low-risk patients. Further investigation identified the differential epigenetic modification patterns of the five genes, which indicated their roles in tumor progression and their effect on therapy. In conclusion, we constructed a robust five-gene expression signature that could predict the OS of PC patients, offering a new insight for risk discrimination in daily clinical practice.

A Transcriptomics-Based Meta-Analysis Combined With Machine Learning Identifies a Secretory Biomarker Panel for Diagnosis of Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is generally incurable due to the late diagnosis and absence of markers that are concordant with expression in several sample sources (i.e., tissue, blood, plasma) and platforms (i.e., Microarray, sequencing). We optimized meta-analysis of 19 PDAC (tissue and blood) transcriptome studies from multiple platforms. The key biomarkers for PDAC diagnosis with secretory potential were identified and validated in different cohorts. Machine learning approach i.e., support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, GGH, PLBD1, HTATIP2, IL1R2, CTSA) that achieved ∼0.92 average sensitivity and ∼0.90 average specificity in distinguishing PDAC from healthy samples in five training sets using cross-validation. These markers were also validated in proteomics and single-cell transcriptomics studies suggesting their prognostic role in the diagnosis of PDAC. Our 9-gene classifier can not only clearly discriminate between better and poor survivors but can also precisely discriminate PDAC from chronic pancreatitis (AUC = 0.95), early stages of progression [Stage I and II (AUC = 0.82), IPMA and IPMN (AUC = 1), and IPMC (AUC = 0.81)]. The 9-gene marker outperformed the previously known markers in blood studies particularly (AUC = 0.84). The discrimination of PDAC from early precursor lesions in non-malignant tissue (AUC > 0.81) and peripheral blood (AUC > 0.80) may assist in an early diagnosis of PDAC in blood samples and thus will also facilitate risk stratification upon validation in clinical trials.

The implication of long non-coding RNAs in the diagnosis, pathogenesis and drug resistance of pancreatic ductal adenocarcinoma and their possible therapeutic potential

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies with the lowest median and overall survival rate among all human malignancies. The major problems with the PDAC are the late diagnosis, metastasis, and acquired resistance to chemotherapeutic agents in the clinic. Over the last decade, the long non-coding RNAs (lncRNAs) have been discovered and occupies a significantly large proportion of the human genome. Recent studies have proved that lncRNAs can play a crucial role in the majority of key cellular processes involved in the maintenance of cellular homeostasis by regulating various molecular mechanisms. The deregulation of lncRNAs has been associated with various chronic diseases including human malignancies. Several lncRNAs have tumor-specific expression making them an ideal and excellent target for designing the novel therapeutic strategies against human malignancies. We have discussed how lncRNA expression can be used for the diagnosis and prognosis of PDAC. The current review discusses the potential role and molecular mechanism of lncRNA in regulating the prominent hallmarks of cancer including abnormal growth, survival, metastasis, and drug-resistance in PDAC. Importantly, we also highlight the possible application of various therapeutic strategies including small interfering RNA, CRISPR-Cas9, antisense oligonucleotides, locked nucleic acid Gapmers, small molecules, aptamers, lncRNA promoter to target the lncRNA as a novel and viable options for treatment of PDAC.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

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

A poor outcome for pancreatic ductal adenocarcinoma (PDAC) patients is still a challenge worldwide. The aim of our study is to investigate the potential of key laminin subunits for being used both as a diagnostic and prognostic biomarker for PDAC patients. We evaluated the mRNA expression and prognostic value of laminin gene family in PDAC tissues using online public databases. Moreover, the relationship between key laminin subunits in PDAC blood cells and circulating tumor cells (CTCs) and the distinguishing ability of joint serum levels with carbohydrate antigen 19-9 (CA19-9) was analyzed. Two key differentially expressed subunits (LAMA3 and LAMC2) that are associated with prognosis of PDAC patients were found to show a potential for distinguishing between PDAC and non-tumor tissues. LAMA3 and LAMC2 expression were found to be positively related with CTC quantity in PDAC blood (R=0.628, p=0.029; R=0.776, p=0.003, respectively) using IgG chips. Furthermore, serum LAMC2 levels offered significant improvement over using CA19-9 alone for the discrimination of PDAC. Joint serum LAMC2 and CA19-9 levels increased the net benefit proportion in early stage/operational PDAC patients. Using integrated profiling, we identified LAMA3 and LAMC2 as potential therapeutic targets and prognostic markers for PDAC, for which further validation is warranted.

Exosomal microRNA‑146a derived from mesenchymal stem cells increases the sensitivity of ovarian cancer cells to docetaxel and taxane via a LAMC2‑mediated PI3K/Akt axis

The carrier role of exosomes from human umbilical cord mesenchymal stem cells (hUCMSCs) containing microRNAs (miRNAs) has been implicated in gene and drug therapy. The aim of the present study was to investigate the role of exosomal microRNA-146a (miR-146a) from hUCMSCs in ovarian cancer (OC). Following the generation of docetaxel (DTX)-resistant SKOV3 cells and taxane-resistant A2780 cells, exosomes were isolated from hUCMSCs and added to the chemoresistant cells. Microarray analysis revealed that miR-146a expression was upregulated in DTX/SKOV3 cells among 15 ectopically expressed miRNAs. Analysis using the StarBase and miRSearch databases demonstrated that miR-146a targeted laminin γ2 (LAMC2), which was further verified using dual-luciferase reporter assays. Subsequently, miR-146a inhibitor or LAMC2 overexpression vectors were transfected into hUCMSCs or OC cells, respectively, and their effects on growth and chemoresistance in OC cells were assessed. The hUCMSC-derived exosomes reduced cell growth and chemoresistance in OC. Furthermore, hUCMSC-derived exosomes with miR-146a expression knocked down increased OC cell growth and chemoresistance, which was mediated by the PI3K/Akt signaling pathway via LAMC2.

Laminin 332 in cancer: When the extracellular matrix turns signals from cell anchorage to cell movement

Laminin 332 is crucial in the biology of epithelia. This large extracellular matrix protein consists of the heterotrimeric assembly of three subunits - α3, β3, and γ2 - and its multifunctionality relies on a number of extracellular proteolytic processing events. Laminin 332 is central to normal epithelium homeostasis by sustaining cell adhesion, polarity, proliferation, and differentiation. It also supports a major function in epithelial tissue formation, repair, and regeneration by buttressing cell migration and survival and basement membrane assembly. Interest in this protein increased after the discovery that its expression is perturbed in tumor cells, cancer-associated fibroblasts, and the tumor microenvironment. This review summarizes current knowledge regarding the established involvement of the laminin 332 γ2 chain in tumor invasiveness and discusses the role of its α3 and β3 subunits.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Silencing of LAMC2 Reverses Epithelial-Mesenchymal Transition and Inhibits Angiogenesis in Cholangiocarcinoma via Inactivation of the Epidermal Growth Factor Receptor Signaling Pathway

Cholangiocarcinoma (CCA) is a malignant cancer that is associated with high mortality rates. The relationship between laminin γ 2 chain gene (LAMC2) and epidermal growth factor receptor (EGFR) has been previously documented in gastric cancer and oral squamous cell carcinoma. This study investigates the role of LAMC2 in epithelial-mesenchymal transition (EMT) and angiogenesis in CCA and explores the underlying mechanism(s). Differentially expressed genes related to CCA were initially screened using a microarray analysis, and the interaction between LAMC2 and the EGFR signaling pathway was identified. To determine the regulatory effects of LAMC2 on CCA progression, LAMC2 was silenced or overexpressed and the EGFR signaling pathway was activated or blocked. Subsequently, the regulation effects of LAMC2 were evaluated on the expression of EMT markers, invasion and migration of CCA cells, as well as microvessel density in nude mice. Microarray analysis demonstrated that highly expressed LAMC2 is linked to CCA development, which involves the EGFR signaling pathway. When LAMC2 expression was increased, the EGFR signaling pathway and EMT were activated in CCA tissues. Silencing of LAMC2 as well as EGFR signaling pathway inhibition led to suppression of EMT, cell invasion, and migration abilities in vitro, as well as angiogenesis in vivo. This study demonstrates that LAMC2 silencing suppresses the activity of the EGFR signaling pathway, thus functioning as a tumor suppressor in CCA.

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.

Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer

BACKGROUND

The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers.

METHODS

Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model.

RESULTS

Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = - 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count).

CONCLUSION

Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.

LAMC2 regulated by microRNA-125a-5p accelerates the progression of ovarian cancer via activating p38 MAPK signalling

AIMS

Laminin γ2 (LAMC2) is over-expressed in ovarian cancer, and its high expression facilitates cell invasion. Nevertheless, the effects of LAMC2 on other ovarian cancer cell functions and its underlying mechanism remain largely unclear. Bioinformatics analysis shows that LAMC2 is a predicted target of miR-125a-5p and miR-193a-3p. Therefore, the present study aimed to investigate the effects of LAMC2 in ovarian cancer progression and determine whether LAMC2 expression is under the regulation of miR-125a-5p or miR-193a-3p in ovarian cancer.

MATERIALS AND METHODS

Immunohistochemistry staining, western blot and qPCR were used to detect LAMC2 expression profiles. CCK-8, flow cytometry and tumour formation assays were used to assess cell proliferation, apoptosis and tumorigenesis. The interaction between miR-125a-5p/miR-193a-3p and LAMC2 were determined by the luciferase gene reporter assay.

KEY FINDINGS

The results showed that LAMC2 was over-expressed in ovarian cancer tissues and cell lines. Over-expression of LAMC2 significantly promoted cell proliferation and repressed cell apoptosis, as well as increased the expression levels of p38, p-p38, c-myc and CREB, and translocated p38 protein to the nucleus. In addition, the promotion of cell proliferation and repression of cell apoptosis mediated by LAMC2 over-expression were all weakened when p38 was downregulated. Moreover, LAMC2 expression was negatively regulated by miR-125a-5p, which inhibited the nuclear accumulation of p38 protein. Upregulation of LAMC2 significantly abolished the effects of miR-125a-5p on cell proliferation inhibition and cell apoptosis promotion, as well as tumourigenesis repression.

SIGNIFICANCE

The present study clarified that LAMC2 functioned as an oncogene in ovarian cancer through upregulating p38 under the regulation of miR-125a-5p.

Pancreatic Ductal Deletion of Hnf1b Disrupts Exocrine Homeostasis, Leads to Pancreatitis, and Facilitates Tumorigenesis

BACKGROUND & AIMS

The exocrine pancreas consists of acinar cells that produce digestive enzymes transported to the intestine through a branched ductal epithelium. Chronic pancreatitis is characterized by progressive inflammation, fibrosis, and loss of acinar tissue. These changes of the exocrine tissue are risk factors for pancreatic cancer. The cause of chronic pancreatitis cannot be identified in one quarter of patients. Here, we investigated how duct dysfunction could contribute to pancreatitis development.

METHODS

The transcription factor Hnf1b, first expressed in pancreatic progenitors, is strictly restricted to ductal cells from late embryogenesis. We previously showed that Hnf1b is crucial for pancreas morphogenesis but its postnatal role still remains unelucidated. To investigate the role of pancreatic ducts in exocrine homeostasis, we inactivated the Hnf1b gene in vivo in mouse ductal cells.

RESULTS

We uncovered that postnatal Hnf1b inactivation in pancreatic ducts leads to chronic pancreatitis in adults. Hnf1bΔ^duct mutants show dilatation of ducts, loss of acinar cells, acinar-to-ductal metaplasia, and lipomatosis. We deciphered the early events involved, with down-regulation of cystic disease-associated genes, loss of primary cilia, up-regulation of signaling pathways, especially the Yap pathway, which is involved in acinar-to-ductal metaplasia. Remarkably, Hnf1bΔ^duct mutants developed pancreatic intraepithelial neoplasia and promote pancreatic intraepithelial neoplasia progression in concert with KRAS. We further showed that adult Hnf1b inactivation in pancreatic ducts is associated with impaired regeneration after injury, with persistent metaplasia and initiation of neoplasia.

CONCLUSIONS

Loss of Hnf1b in ductal cells leads to chronic pancreatitis and neoplasia. This study shows that Hnf1b deficiency may contribute to diseases of the exocrine pancreas and gains further insight into the etiology of pancreatitis and tumorigenesis.

Quantitative proteomics identifies brain acid soluble protein 1 (BASP1) as a prognostic biomarker candidate in pancreatic cancer tissue

BACKGROUND

Pancreatic cancer is a heterogenous disease with a poor prognosis. This study aimed to discover and validate prognostic tissue biomarkers in pancreatic cancer using a mass spectrometry (MS) based proteomics approach.

METHODS

Global protein sequencing of fresh frozen pancreatic cancer and healthy pancreas tissue samples was conducted by MS to discover potential protein biomarkers. Selected candidate proteins were further verified by targeted proteomics using parallel reaction monitoring (PRM). The expression of biomarker candidates was validated by immunohistochemistry in a large tissue microarray (TMA) cohort of 141 patients with resectable pancreatic cancer. Kaplan-Meier and Cox proportional hazard modelling was used to investigate the prognostic utility of candidate protein markers.

FINDINGS

In the initial MS-discovery phase, 165 proteins were identified as potential biomarkers. In the subsequent MS-verification phase, a panel of 45 candidate proteins was verified by the development of a PRM assay. Brain acid soluble protein 1 (BASP1) was identified as a new biomarker candidate for pancreatic cancer possessing largely unknown biological and clinical functions and was selected for further analysis. Importantly, bioinformatic analysis indicated that BASP1 interacts with Wilms tumour protein (WT1) in pancreatic cancer. TMA-based immunohistochemistry analysis showed that BASP1 was an independent predictor of prolonged survival (HR 0.468, 95% CI 0.257-0.852, p = .013) and predicted favourable response to adjuvant chemotherapy, whereas WT1 indicated a worsened survival (HR 1.636, 95% CI 1.083-2.473, p = .019) and resistance to chemotherapy. Interaction analysis showed that patients with negative BASP1 and high WT1 expression had the poorest outcome (HR 3.536, 95% CI 1.336-9.362, p = .011).

INTERPRETATION

We here describe an MS-based proteomics platform for developing biomarkers for pancreatic cancer. Bioinformatic analysis and clinical data from our study suggest that BASP1 and its putative interaction partner WT1 can be used as biomarkers for predicting outcomes in pancreatic cancer patients.

A Systematic Investigation of the Malignant Functions and Diagnostic Potential of the Cancer Secretome

The collection of proteins secreted from a cell-the secretome-is of particular interest in cancer pathophysiology due to its diagnostic potential and role in tumorigenesis. However, cancer secretome studies are often limited to one tissue or cancer type or focus on biomarker prediction without exploring the associated functions. We therefore conducted a pan-cancer analysis of secretome gene expression changes to identify candidate diagnostic biomarkers and to investigate the underlying biological function of these changes. Using transcriptomic data spanning 32 cancer types and 30 healthy tissues, we quantified the relative diagnostic potential of secretome proteins for each cancer. Furthermore, we offer a potential mechanism by which cancer cells relieve secretory pathway stress by decreasing the expression of tissue-specific genes, thereby facilitating the secretion of proteins promoting invasion and proliferation. These results provide a more systematic understanding of the cancer secretome, facilitating its use in diagnostics and its targeting for therapeutic development.

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)_OS = 2.2, p-value < 0.001), ANXA2 (HR_OS = 2.1, p-value < 0.001), and LAMC2 (HR_DFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

Unique Biological Activity and Potential Role of Monomeric Laminin-γ2 as a Novel Biomarker for Hepatocellular Carcinoma: A Review

Laminin (Ln)-332 consists of α3, β3, and γ2 chains, which mediate epithelial cell adhesion to the basement membrane. Ln-γ2, a component of Ln-332, is frequently expressed as a monomer in the invasion front of several types of malignant tissues without simultaneous expression of Ln-α3 and/or Ln-β3 chains. Moreover, monomeric Ln-γ2 induces tumor cell proliferation and migration in vitro. These unique biological activities indicate that monomeric Ln-γ2 could be a candidate biomarker for early cancer surveillance. However, the present immune method for monomeric Ln-γ2 detection can only predict its expression, since no antibody that specifically reacts with monomeric γ2, but not with heterotrimeric γ2 chain, is commercially available. We have, therefore, developed monoclonal antibodies to specifically detect monomeric Ln-γ2, and devised a highly sensitive method to measure serum monomeric Ln-γ2 levels using a fully automated chemiluminescent immunoassay (CLIA). We evaluated its diagnostic value in sera from patients with several digestive cancers, including hepatocellular carcinoma (HCC), and found serum monomeric Ln-γ2 to be a clinically available biomarker for HCC surveillance. The combination of monomeric Ln-γ2 and prothrombin induced by Vitamin K Absence II (PIVKA-II) may be more sensitive for clinical diagnosis of HCC than any currently used combination.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

Immune Cell Production of Interleukin 17 Induces Stem Cell Features of Pancreatic Intraepithelial Neoplasia Cells

BACKGROUND & AIMS

Little is known about how the immune system affects stem cell features of pancreatic cancer cells. Immune cells that produce interleukin 17A (IL17A) in the chronically inflamed pancreas (chronic pancreatitis) contribute to pancreatic interepithelial neoplasia (PanIN) initiation and progression. We investigated the effects that IL17A signaling exerts on pancreatic cancer progenitor cells and the clinical relevance of this phenomena.

METHODS

We performed studies with Mist1Cre;LSLKras;Rosa26mTmG (KC^iMist;G) and Kras(G12D);Trp53(R172H);Pdx1-Cre (KPC) mice (which upon tamoxifen induction spontaneously develop PanINs) and control littermates. Some mice were injected with neutralizing antibodies against IL17A or control antibody. Pancreata were collected, PanIN epithelial cells were isolated by flow cytometry based on lineage tracing, and gene expression profiles were compared. We collected cells from pancreatic tumors of KPC mice, incubated them with IL17 or control media, measured expression of genes regulated by IL17 signaling, injected the cancer cells into immune competent mice, and measured tumor growth. IL17A was overexpressed in pancreata of KC^iMist mice from an adenoviral vector. Pancreata were collected from all mice and analyzed by histology and immunohistochemistry. Levels of DCLK1 and other proteins were knocked down in KPC pancreatic cancer cells using small interfering or short hairpin RNAs; cells were analyzed by immunoblotting. We obtained 65 pancreatic tumor specimens from patients, analyzed protein levels by immunohistochemistry, and compared results with patient survival times. We also analyzed gene expression levels and patient outcome using The Cancer Genome Atlas database.

RESULTS

PanIN cells from KC^iMist;G mice had a gene expression pattern associated with embryonic stem cells. Mice given injections of IL17-neutralizing antibodies, or with immune cells that did not secrete IL17, lost this expression pattern and had significantly decreased expression of DCLK1 and POU2F3, which regulate tuft cell development. KC^iMist mice that overexpressed IL17 formed more PanINs, with more DCLK1-positive cells, than control mice. Pancreatic tumor cells from KPC mice and human Capan-2 cells exposed to IL17A had increased activation of NF-κB and mitogen-activated protein kinase signaling and increased expression of DCLK1 and ALDH1A1 (a marker of embryonic stem cells) compared with cells in control media. These cells also formed tumors faster that cells not exposed to IL17 when they were injected into immunocompetent mice. KPC cells with knockdown of DCLK1 expressed lower levels of ALDH1A1 after incubation with IL17 than cells without knockdown. Expression of the IL17 receptor C was higher in DCLK1-positive PanIN cells from mice compared with DCLK1-negative PanIN cells. In human pancreatic tumor tissues, high levels of DCLK1 associated with a shorter median survival time of patients (17.7 months, compared with 26.6 months of patients whose tumors had low levels of DCLK1). Tumor levels of POU2F3 and LAMC2 were also associated with patient survival time.

CONCLUSIONS

In studies of mouse and human pancreatic tumors and precursors, we found that immune cell-derived IL17 regulated development of tuft cells and stem cell features of pancreatic cancer cells via increased expression of DCLK1, POU2F3, ALDH1A1, and IL17RC. Strategies to disrupt this pathway might be developed to prevent pancreatic tumor growth and progression.

Identification of molecular biomarkers for pancreatic cancer with mRMR shortest path method

The high mortality rate of pancreatic cancer makes it one of the most studied diseases among all cancer types. Many researches have been conducted to understand the mechanism underlying its emergence and pathogenesis of this disease. Here, by using minimum-redundancy-maximum-relevance (mRMR) method, we studied a set of transcriptome data of pancreatic cancer. As we gradually added features to achieve the most accurate classification results of Jackknife, a gene set of 9 genes was identified. They were NHS, SCML2, LAMC2, S100P, COL17A1, AMIGO2, PTPRR, KPNA7 and KCNN4. Through STRING 2.0 protein-protein interactions (PPIs) analysis, 40 proteins were identified in the shortest paths between genes in the gene set, 30 of them passed the permutation test, which indicated they were hubs in the background network. Those genes in the protein-protein interaction network were enriched to 37 functional modules, such as: negative regulation of transcription from RNA polymerase II promoter, negative regulation of ERK1 and ERK2 cascade and BMP signaling pathway. Our study indicated new mechanism of pancreatic cancer, suggesting potential therapeutic targets for further study.

LINC00511 interacts with miR-765 and modulates tongue squamous cell carcinoma progression by targeting LAMC2

BACKGROUND

Aimed at underlying the molecular regulatory mechanism and overall biological functions of LINC00511 in tongue squamous cell carcinoma (TSCC).

METHODS

The expression level of LINC00511 was examined by QRT-PCR. In particular, Tca-8113 cell line was selected for subsequent experiments, in which the expression level of LINC00511 was the most significant. Meanwhile, the effects of LINC00511 on cells proliferation, cell cycle distribution, and invasion of TSCC cells were explored using RNA knockdown tools with CCK-8, flow cytometry analysis, colony formation, and transwell assay. Further, bioinformatic analysis and the dual-luciferase reporter assay both were conducted to invalidate the ceRNAs regulatory mechanism of LINC00511 in TSCC.

RESULTS

LINC00511 was obviously upregulated in TSCC tissues and cell lines. Moreover, it was found that LINC00511 served as a competing endogenous RNA (ceRNA) through sponging miR-765 and ultimately modulated the derepression of laminin subunit gamma 2 (LAMC2). The inhibitory effects of miR-765 on TSCC cells proliferation, invasion as well as cell cycle distribution can be restored by the ectopic overexpression of LINC00511. Additionally, the restored capacity of LINC00511 promoted the expression of LAMC2, which was a downstream target of miR-765 and can be negatively regulated by miR-765.

CONCLUSIONS

A novel molecular axis of LINC00511/miR-765/LAMC2 was investigated to regulate the tumor development of TSCC. LINC00511 promoted the expression of LAMC2 via the ceRNA mechanism of sponging miR-765. The ceRNA regulatory network provided a novel understanding of TSCC pathogenesis and also shed light on exploiting the new field of lncRNA-directed therapy against TSCC.

LncRNA CASC9 promotes esophageal squamous cell carcinoma metastasis through upregulating LAMC2 expression by interacting with the CREB-binding protein

Esophageal squamous cell carcinoma (ESCC) is the main subtype of esophageal cancer. Long noncoding RNAs (lncRNAs) are thought to play a critical role in cancer development. Recently, lncRNA CASC9 was shown to be dysregulated in many cancer types, but the mechanisms whereby this occurs remain largely unknown. In this study, we found that CASC9 was significantly upregulated in ESCC tissues, with further analysis revealing that elevated CASC9 expression was associated with ESCC prognosis and metastasis. Furthermore, we found that CASC9 knockdown significantly repressed ESCC migration and invasion in vitro and metastasis in nude mice in vivo. A microarray analysis and mechanical experiments indicated that CASC9 preferentially affected gene expression linked to ECM–integrin interactions, including LAMC2, an upstream inducer of the integrin pathway. We demonstrated that LAMC2 was consistently upregulated in ESCC and promoted ESCC metastasis. LAMC2 overexpression partially compromised the decrease of cell migration and invasion capacity in CASC9 knockdowns. In addition, we found that both CASC9 and LAMC2 depletion reduced the phosphorylation of FAK, PI3K, and Akt, which are downstream effectors of the integrin pathway. Moreover, the reduction in phosphorylation caused by CASC9 depletion was rescued by LAMC2 overexpression, further confirming that CASC9 exerts a pro-metastatic role through LAMC2. Mechanistically, RNA pull-down and RNA-binding protein immunoprecipitation (RIP) assay indicated that CASC9 could bind with the transcriptional coactivator CREB-binding protein (CBP) in the nucleus. Chromatin immunoprecipitation (ChIP) assay additionally illustrated that CASC9 increased the enrichment of CBP and H3K27 acetylation in the LAMC2 promoter, thereby upregulating LAMC2 expression. In conclusion, we demonstrate that CASC9 upregulates LAMC2 expression by binding with CBP and modifying histone acetylation. Our research reveals the prognostic and pro-metastatic roles for CASC9 in ESCC, suggesting that CASC9 could serve as a biomarker for prognosis and a target for metastasis treatment.

In vivo protein allylation to capture protein methylation candidates

An approach combining in vivo protein allylation, chemical tagging and affinity enrichment was devised to capture protein methylation candidates in yeast S. cerevisiae. The study identified 167 hits, covering many proteins with known methylation events on different types of amino acid residues.

Development of a fully automated chemiluminescence immunoassay for urine monomeric laminin-γ2 as a promising diagnostic tool of non-muscle invasive bladder cancer

BACKGROUND

Monomeric laminin-γ2 in urine is a potential biomarker for bladder cancer. However, the current detection system uses an antibody that cannot discriminate between monomeric laminin-γ2 and the heterotrimeric γ2 chain of laminin-332, which may cause false-positive reactions. The present study aimed to develop a fully automated chemiluminescence immunoassay system using a specific monoclonal antibody against monomeric laminin-γ2.

METHODS

In total, 237 urine specimens (84 from patients with bladder cancer, 48 from patients with benign urological disease, and 105 from healthy donors) were collected, and monomeric laminin-γ2 values in the urine were measured using a fully automated chemiluminescence immunoassay.

RESULTS

The results revealed that laminin-γ2 values in patients with benign urological disease were comparable to those of healthy donors and that the chemiluminescence immunoassay's lower limit of detection was 10 pg/mL (approximately 20-fold better than the sandwich enzyme-linked immunosorbent assay's limit of 200 pg/mL). Moreover, the chemiluminescence immunoassay demonstrated that patients with bladder cancer, including non-muscle invasive bladder cancer (≤pT1), had higher laminin-γ2 values than patients with benign urological disease or healthy donors.

CONCLUSIONS

These results suggest that urine monomeric laminin-γ2 may be a promising biomarker to diagnose cases of non-muscle invasive bladder cancer using a fully automated chemiluminescence immunoassay system.

Chemical composition and the potential for proteomic transformation in cancer, hypoxia, and hyperosmotic stress

The changes of protein expression that are monitored in proteomic experiments are a type of biological transformation that also involves changes in chemical composition. Accompanying the myriad molecular-level interactions that underlie any proteomic transformation, there is an overall thermodynamic potential that is sensitive to microenvironmental conditions, including local oxidation and hydration potential. Here, up- and down-expressed proteins identified in 71 comparative proteomics studies were analyzed using the average oxidation state of carbon (Z_C) and water demand per residue (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O), calculated using elemental abundances and stoichiometric reactions to form proteins from basis species. Experimental lowering of oxygen availability (hypoxia) or water activity (hyperosmotic stress) generally results in decreased Z_C or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${\overline{n}}_{{\mathrm{H}}_{2}\mathrm{O}}$\end{document}n¯H2O of up-expressed compared to down-expressed proteins. This correspondence of chemical composition with experimental conditions provides evidence for attraction of the proteomes to a low-energy state. An opposite compositional change, toward higher average oxidation or hydration state, is found for proteomic transformations in colorectal and pancreatic cancer, and in two experiments for adipose-derived stem cells. Calculations of chemical affinity were used to estimate the thermodynamic potentials for proteomic transformations as a function of fugacity of O₂ and activity of H₂O, which serve as scales of oxidation and hydration potential. Diagrams summarizing the relative potential for formation of up- and down-expressed proteins have predicted equipotential lines that cluster around particular values of oxygen fugacity and water activity for similar datasets. The changes in chemical composition of proteomes are likely linked with reactions among other cellular molecules. A redox balance calculation indicates that an increase in the lipid to protein ratio in cancer cells by 20% over hypoxic cells would generate a large enough electron sink for oxidation of the cancer proteomes. The datasets and computer code used here are made available in a new R package, canprot.