A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma

MicroRNA-130b Suppresses Malignant Behaviours and Inhibits the Activation of the PI3K/Akt Signaling Pathway by Targeting MET in Pancreatic Cancer

There has been interested in the microRNAs' roles in pancreatic cancer (PC) cell biology, particularly in regulating pathways related to tumorigenesis. The study aimed to explore the hub miRNAs in PC and underlying mechanisms by bioinformatics and fundamental experiments. RNA datasets collected from the Gene Expression Omnibus were analysed to find out differentially expressed RNAs (DERNAs). The miRNA-mRNA and protein-protein interaction (PPI) networks were built. The clinicopathological features and expressions of hub miRNAs and hub mRNAs were explored. Dual-luciferase reporter gene assay was performed to assess the interaction between microRNA and target gene. RT-qPCR and western blot were employed to explore RNA expression. The roles of RNA were detected by CCK-8 test, wound healing, transwell, and flow cytometry experiment. We verified 40 DEmiRNAs and 1613 DEmRNAs, then detected a total of 69 final functional mRNAs (FmRNAs) and 23 DEmiRNAs. In the miRNA-mRNA networks, microRNA-130b (miR-130b) was the hub RNA with highest degrees. Clinical analysis revealed that miR-130b was considerably lower expressed in cancerous tissues than in healthy ones, and patients with higher-expressed miR-130b had a better prognosis. Mechanically, miR-130b directly targeted MET in PC cells. Cell functional experiments verified that miR-130b suppressed cell proliferation, migration, promoted apoptosis, and inhibited the PI3K/Akt pathway by targeting MET in PC cells. Our findings illustrated the specific molecular mechanism of miR-130b regulating PC progress. The miR-130b/MET axis may be an alternative target in the therapeutic intervention of PC and provide an opportunity to deepen our understanding of the pathogenesis of PC.

Deciphering of SOX9 Functions in Pancreatic Cancer Cells

SOX9 is widely regarded as a key master regulator of gene transcription, responsible for the development and differentiation programs within tissue and organogenesis, particularly in the pancreas. SOX9 overexpression has been observed in multiple tumor types, including pancreatic cancer, and is discussed as a prognostic marker. In order to gain a more profound understanding of the role of SOX9 in pancreatic cancer, we have performed SOX9 knockdown in the COLO357 and PANC-1 cells using RNA interference, followed by full-transcriptome analysis of the siRNA-transfected cells. The molecular pathway enrichment analysis between SOX9-specific siRNA-transfected cells and control cells reveals the activation of processes associated with cellular signaling, cell differentiation, transcription, and methylation, alongside the suppression of genes involved in various stages of the cell cycle and apoptosis, upon the SOX9 knockdown. Alterations of the expression of transcription factors, epithelial-mesenchymal transition markers, oncogenes, tumor suppressor genes, and drug resistance-related genes upon SOX9 knockdown in comparison of primary and metastatic pancreatic cancer cells are discovered. The expression levels of genes comprising prognostic signatures for pancreatic cancer were also evaluated following SOX9 knockdown. Additional studies are needed to assess the properties and prognostic significance of SOX9 in pancreatic cancer using other biological models.

Longitudinal Circulating Tumor Cell Collection, Culture, and Characterization in Pancreatic Adenocarcinomas

BACKGROUND/OBJECTIVES

Pancreatic adenocarcinoma (PDAC) remains one of the most lethal cancers, with limited advancements in treatment efficacy due to high rates of chemoresistance. Circulating tumor cells (CTCs) derived from liquid biopsies offer a non-invasive approach to monitoring tumor evolution and identifying molecular mechanisms of resistance. This study aims to longitudinally collect, culture, and characterize CTCs from PDAC patients to elucidate resistance mechanisms and tumor-specific gene expression profiles.

METHODS

Blood samples from 10 PDAC patients were collected across different treatment stages, yielding 16 CTC cultures. Differential gene expression, pathway dysregulation, and protein-protein interaction studies were utilized, highlighting patient-specific and disease progression-associated changes. Longitudinal comparisons within five patients provided further insights into dynamic molecular changes associated with therapeutic resistance.

RESULTS

CTC cultures exhibited the activation of key pathways implicated in PDAC progression and resistance, including TNFα/NF-kB, hedgehog signaling, and the epithelial-to-mesenchymal transition. Longitudinal samples revealed dynamic changes in signaling pathways, highlighting upregulated mechanisms of chemoresistance, including PI3K/Akt/mTOR and TGF-β pathways. Additionally, protein-protein interaction analysis emphasized the role of the immune system in PDAC progression and therapy response. Patient-specific gene expression patterns therefore suggest potential applications for precision medicine.

CONCLUSIONS

This proof-of-concept study demonstrates the feasibility of longitudinally capturing and analyzing CTCs from PDAC patients. The findings provide critical insights into molecular drivers of chemoresistance and highlight the potential of CTC profiling to inform personalized therapeutic strategies. Future large-scale studies are warranted to validate these findings and further explore CTC-based approaches in PDAC management.

Investigating the function and targeting of MET protein as an oncogene kinase in pancreatic ductal adenocarcinoma: A microarray data integration

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a poor prognosis. Kinase proteins are essential regulators of cellular processes and potential targets for drug development.

Methods

Integration of multiple microarray datasets was screened to find differentially expressed kinases (DE-Kinases) across adjacent normal and tumor tissue samples in PDAC. The most effective kinase for drug design and docking in this study was selected by investigating biological mechanisms and survival analyses. Forty phytochemicals were extracted from the yellow sweet clover, Melilotus officinalis (Linn.) Pall, and were then subjected to in silico screening and molecular docking studies against a specific potent kinase.

Results

MET, PAK3, and PDK4 were identified as the DE-Kinases. After examining the pathways and biological processes, up-regulated MET had the most significant survival analysis and became our primary kinase for drug design and docking in this study. Four of the extracted phytocompounds of Melilotus officinalis (Linn.) Pall that exhibited high binding affinities with MET and were selected for toxicity analysis. Finally, the stability and mobility of the two nontoxic compounds that passed the toxicity test (dicumarol PubChem CID: 54676038 and melilotigenin PubChem CID: 14059499) were studied by molecular dynamics simulation.

Conclusion

This study's results identified two phytochemicals in yellow sweet clover that could be used to develop an anticancer drug, but experimental evaluation is necessary to confirm their efficacy.

Transcriptomic subtyping of gastrointestinal malignancies

Gastrointestinal (GI) cancers are highly heterogeneous at multiple levels. Tumor heterogeneity can be captured by molecular profiling, such as genetic, epigenetic, proteomic, and transcriptomic classification. Transcriptomic subtyping has the advantage of combining genetic and epigenetic information, cancer cell-intrinsic properties, and the tumor microenvironment (TME). Unsupervised transcriptomic subtyping systems of different GI malignancies have gained interest because they reveal shared biological features across cancers and bear prognostic and predictive value. Importantly, transcriptomic subtypes accurately reflect complex phenotypic states varying not only per tumor region, but also throughout disease progression, with consequences for clinical management. Here, we discuss methodologies of transcriptomic subtyping, proposed taxonomies for GI malignancies, and the challenges posed to clinical implementation, highlighting opportunities for future transcriptomic profiling efforts to optimize clinical impact.

Co‑expression of SLC20A1 and ALDH1A3 is associated with poor prognosis, and SLC20A1 is required for the survival of ALDH1‑positive pancreatic cancer stem cells

Solute carrier family 20 member 1 (SLC20A1) is a sodium/inorganic phosphate symporter, which has been identified as a prognostic marker in several types of cancer, including pancreatic cancer. However, to the best of our knowledge, the association between SLC20A1 expression and cancer stem cell (CSC) markers, such as aldehyde dehydrogenase 1 (ALDH1), in pancreatic ductal adenocarcinoma (PDAC), and the role of SLC20A1 in PDAC CSCs remains unclear. In the present study, a genomic dataset of primary pancreatic cancer (The Cancer Genome Atlas, Pan-Cancer Atlas) was downloaded and analyzed. Kaplan-Meier analysis and multivariate Cox regression analysis were performed to evaluate the overall survival, disease-specific survival (DSS), disease-free interval (DFI) and progression-free interval (PFI). Subsequently, SLC20A1 small interfering RNA (siRNA) knockdown (KD) was induced in the PANC-1 and MIA-PaCa-2 PDAC cell lines, and in sorted high ALDH1 activity (ALDH1high) cells, after which, cell viability, in vitro tumor sphere formation, cell death and caspase-3 activity were examined. The results revealed that patients with high expression of SLC20A1 (SLC20A1 high) at tumor stage I had a poor prognosis compared with patients with low expression of SLC20A1 (SLC20A1 low) in terms of DSS, DFI and PFI. In addition, patients with high expression of SLC20A1 and ALDH1A3 (SLC20A1 high ALDH1A3 high) exhibited poorer clinical outcomes than patients with high expression of SLC20A1 and low expression of ALDH1A3 (SLC20A1 high ALDH1A3 low), low expression of SLC20A1 and high expression of ALDH1A3 (SLC20A1 low ALDH1A3 high) and SLC20A1 low ALDH1A3 low. SLC20A1 siRNA KD in ALDH1high cells isolated from PANC-1 and MIA-PaCa-2 cell lines resulted in suppression of in vitro tumorsphere formation, and enhancement of cell death and caspase-3 activity. These results suggested that SLC20A1 was involved in cell survival via the suppression of caspase-3-dependent apoptosis, and contributed to cancer progression and poor clinical outcomes in PDAC. In conclusion, SLC20A1 may be used as a prognostic marker and novel therapeutic target of ALDH1-positive pancreatic CSCs.

Patient-derived organoids of pancreatic ductal adenocarcinoma for subtype determination and clinical outcome prediction

BACKGROUND

Recently, two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the "Classical" and "Basal-like" subtypes, with the former showing better clinical outcomes than the latter. However, the "molecular" classification has not been applied in real-world clinical practice. This study aimed to establish patient-derived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction.

METHODS

We utilized tumor samples acquired through endoscopic ultrasound-guided fine-needle biopsy and established a PDO library for subsequent use in morphological assessments, RNA-seq analyses, and in vitro drug response assays. We also conducted a prospective clinical study to evaluate whether analysis using PDOs can predict treatment response and prognosis.

RESULTS

PDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells. Morphologically, PDOs were classified as gland-like structures (GL type) and densely proliferating inside (DP type) less than 2 weeks after tissue sampling. RNA-seq analysis revealed that the "morphological" subtype (GL vs. DP) corresponded to the "molecular" subtype ("Classical" vs. "Basal-like"). The "morphological" classification predicted the clinical treatment response and prognosis; the median overall survival of patients with GL type was significantly longer than that with DP type (P < 0.005). The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK inhibitor and chloroquine.

CONCLUSIONS

PDAC PDOs help in subtype determination and clinical outcome prediction, thereby facilitating the bench-to-bedside precision medicine for PDAC.

Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer

BACKGROUND

Invasive Lobular Carcinoma (ILC) is a morphologically distinct breast cancer subtype that represents up to 15% of all breast cancers. Compared to Invasive Breast Carcinoma of No Special Type (IBC-NST), ILCs exhibit poorer long-term outcome and a unique pattern of metastasis. Despite these differences, the systematic discovery of robust prognostic biomarkers and therapeutically actionable molecular pathways in ILC remains limited.

METHODS

Pathway-centric multivariable models using statistical machine learning were developed and tested in seven retrospective clinico-genomic cohorts (n = 996). Further external validation was performed using a new RNA-Seq clinical cohort of aggressive ILCs (n = 48).

RESULTS AND CONCLUSIONS

mRNA dysregulation scores of 25 pathways were strongly prognostic in ILC (FDR-adjusted P < 0.05). Of these, three pathways including Cell-cell communication, Innate immune system and Smooth muscle contraction were also independent predictors of chemotherapy response. To aggregate these findings, a multivariable machine learning predictor called PSILC was developed and successfully validated for predicting overall and metastasis-free survival in ILC. Integration of PSILC with CRISPR-Cas9 screening data from breast cancer cell lines revealed 16 candidate therapeutic targets that were synthetic lethal with high-risk ILCs. This study provides interpretable prognostic and predictive biomarkers of ILC which could serve as the starting points for targeted drug discovery for this disease.

Role of glycosphingolipid biosynthesis coregulators in malignant progression of thymoma

As the most common malignancy from mediastinum, the metabolic reprogramming of thymoma is important in its development. Nevertheless, the connection between the metabolic map and thymoma development is yet to be discovered. Thymoma was categorized into three subcategories by unsupervised clustering of molecular markers for metabolic pathway presentation in the TCGA dataset. Different genes and functions enriched were demonstrated through the utilization of metabolic Gene Ontology (GO) analysis. To identify the main contributors in the development of thymic malignancy, we utilized Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The prognosis of thymoma was evaluated by screening the essential pathways and genes using GSVA scores and machine learning classifiers. Furthermore, we integrated the transcriptomics findings with spectrum metabolomics investigation, detected through LC-MS/MS, in order to establish the essential controller network of metabolic reprogramming during thymoma progression. The thymoma prognosis is related to glycosphingolipid biosynthesis-lacto and neolacto series pathway, of what high B3GNT5 indicate poor survival. The investigation revealed that glycosphingolipid charts have a significant impact on metabolic dysfunction and could potentially serve as crucial targets in the clinical advancement of metabolic therapy.

Pancreatic cancer cell-derived semaphorin 3A promotes neuron recruitment to accelerate tumor growth and dissemination

Perineural invasion and neurogenesis are frequently observed in pancreatic ductal adenocarcinoma (PDAC), and they are associated with a poor prognosis. Axon guidance factor semaphorin 3A (SEMA3A) is upregulated in PDAC. However, it remains unclear whether cancer-derived SEMA3A influences nerve innervation and pancreatic tumorigenesis. In silico analyses were performed using PROGgene and NetworkAnalyst to clarify the importance of SEMA3A and its receptors, plexin A1 (PLXNA1) and neuropilin 2 (NRP2), in pancreatic cancer. In vitro assays, including migration, neurite outgrowth, and 3D recruitment, were performed to study the effects of SEMA3A on neuronal behaviors. Additionally, an orthotopic animal study using C57BL/6 mice was performed to validate the in vitro findings. Expression of SEMA3A and its receptors predicted worse prognosis for PDAC. Cancer-derived SEMA3A promoted neural migration, neurite outgrowth, and neural recruitment. Furthermore, SEMA3A-induced effects depended on PLXNA1, NRP2, and MAPK activation. Trametinib, an approved MAPK kinase (MEK) inhibitor, counteracted SEMA3A-enhanced neuronal activity in vitro. Inhibition of SEMA3A by shRNA in pancreatic cancer cells resulted in decreased neural recruitment, tumor growth, and dissemination in vivo. Our results suggested that cancer-secreted SEMA3A plays an important role in promoting neo-neurogenesis and progression of PDAC.

Identification of GRIN2D as a novel therapeutic target in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a dismal prognosis, and despite significant advances in our understanding of its genetic drivers, like KRAS, TP53, CDKN2A, and SMAD4, effective therapies remain limited. Here, we identified a new therapeutic target GRIN2D and then explored its functions and mechanisms in PDAC progression.

METHODS

We performed a genome-wide RNAi screen in a PDAC xenograft model and identified GRIN2D, which encodes the GluN2D subunit of N-methyl-D-aspartate receptors (NMDARs), as a potential oncogene. Western blot, immunohistochemistry, and analysis on Gene Expression Omnibus were used for detecting the expression of GRIN2D in PDAC. Cellular experiments were conducted for exploring the functions of GRIN2D in vitro while subcutaneous and orthotopic injections were used in in vivo study. To clarify the mechanism, we used RNA sequencing and cellular experiments to identify the related signaling pathway. Cellular assays, RT-qPCR, and western blot helped identify the impacts of the NMDAR antagonist memantine.

RESULTS

We demonstrated that GRIN2D was highly expressed in PDAC cells, and further promoted oncogenic functions. Mechanistically, transcriptome profiling identified GRIN2D-regulated genes in PDAC cells. We found that GRIN2D promoted PDAC progression by activating the p38 MAPK signaling pathway and transcription factor CREB, which in turn promoted the expression of HMGA2 and IL20RB. The upregulated GRIN2D could effectively promote tumor growth and liver metastasis in PDAC. We also investigated the therapeutic potential of NMDAR antagonism in PDAC and found that memantine reduced the expression of GRIN2D and inhibited PDAC progression.

CONCLUSION

Our results suggested that NMDA receptor GRIN2D plays important oncogenic roles in PDAC and represents a novel therapeutic target.

Gene signature developed for predicting early relapse and survival in early-stage pancreatic cancer

BACKGROUND

The aim of this study was to construct a predictive signature integrating tumour-mutation- and copy-number-variation-associated features using machine learning to precisely predict early relapse and survival in patients with resected stage I-II pancreatic ductal adenocarcinoma.

METHODS

Patients with microscopically confirmed stage I-II pancreatic ductal adenocarcinoma undergoing R0 resection at the Chinese PLA General Hospital between March 2015 and December 2016 were enrolled. Whole exosome sequencing was performed, and genes with different mutation or copy number variation statuses between patients with and without relapse within 1 year were identified using bioinformatics analysis. A support vector machine was used to evaluate the importance of the differential gene features and to develop a signature. Signature validation was performed in an independent cohort. The associations of the support vector machine signature and single gene features with disease-free survival and overall survival were assessed. Biological functions of integrated genes were further analysed.

RESULTS

Overall, 30 and 40 patients were included in the training and validation cohorts, respectively. Some 11 genes with differential patterns were first identified; using a support vector machine, four features (mutations of DNAH9, TP53, and TUBGCP6, and copy number variation of TMEM132E) were further selected and integrated to construct a predictive signature (the support vector machine classifier). In the training cohort, the 1-year disease-free survival rates were 88 per cent (95 per cent c.i. 73 to 100) and 7 per cent (95 per cent c.i. 1 to 47) in the low-support vector machine subgroup and the high-support vector machine subgroup respectively (P < 0.001). Multivariable analyses showed that high support vector machine was significantly and independently associated with both worse overall survival (HR 29.20 (95 per cent c.i. 4.48 to 190.21); P < 0.001) and disease-free survival (HR 72.04 (95 per cent c.i. 6.74 to 769.96); P < 0.001). The area under the curve of the support vector machine signature for 1-year disease-free survival (0.900) was significantly larger than the area under the curve values of the mutations of DNAH9 (0.733; P = 0.039), TP53 (0.767; P = 0.024), and TUBGCP6 (0.733; P = 0.023), the copy number variation of TMEM132E (0.700; P = 0.014), TNM stage (0.567; P = 0.002), and differentiation grade (0.633; P = 0.005), suggesting higher predictive accuracy for prognosis. The value of the signature was further validated in the validation cohort. The four genes included in the support vector machine signature (DNAH9, TUBGCP6, and TMEM132E were novel in pancreatic ductal adenocarcinoma) were significantly associated with the tumour immune microenvironment, G protein-coupled receptor binding and signalling, cell-cell adhesion, etc.

CONCLUSION

The newly constructed support vector machine signature precisely and powerfully predicted relapse and survival in patients with stage I-II pancreatic ductal adenocarcinoma after R0 resection.

Systematic Analysis of the Prognostic Significance and Roles of the Integrin Alpha Family in Non-Small Cell Lung Cancers

INTRODUCTION

Lung cancer is one of the most common cancer malignancies and the principal cause of cancer-associated deaths worldwide. Non-small cell lung cancers (NSCLCs) account for more than 80% of all lung cancer cases. Recent studies showed that the genes of the integrin alpha (α) (ITGA) subfamily play a fundamental role in various cancers. However, little is known about the expression and roles of distinct ITGA proteins in NSCLCs.

METHODS

Gene Expression Profiling Interactive Analysis and UALCAN (University of ALabama at Birmingham CANcer) web resources and The Cancer Genome Atlas (TCGA), ONCOMINE, cBioPortal, GeneMANIA, and Tumor Immune Estimation Resource databases were used to evaluate differential expression, correlations between the expression levels of individual genes, the prognostic value of overall survival (OS) and stage, genetic alterations, protein-protein interactions, and the immune cell infiltration of ITGAs in NSCLCs. We used R (v. 4.0.3) software to conduct gene correlation, gene enrichment, and clinical correlation of RNA sequencing data of 1016 NSCLCs from TCGA. To evaluate the expression of ITGA5/8/9/L at the expression and protein levels, qRT-PCR, immunohistochemistry (IHC), and hematoxylin and eosin (H&E) were performed, respectively.

RESULTS

Upregulated levels of ITGA11 messenger RNA and downregulated levels of ITGA1/3/5/7/8/9/L/M/X were observed in the NSCLC tissues. Lower expression of ITGA5/6/8/9/10/D/L was discovered to be expressively associated with advanced tumor stage or poor patient prognosis in patients with NSCLC. A high mutation rate (44%) of the ITGA family was observed in the NSCLCs. Gene Ontology functional enrichment analyses results revealed that the differentially expressed ITGAs could be involved in roles related to extracellular matrix (ECM) organization, collagen-containing ECM cellular components, and ECM structural constituent molecular functions. The results of the Kyoto Encyclopedia of Genes and Genomes analysis revealed that ITGAs may be involved in focal adhesion, ECM-receptor interaction, and amoebiasis; the expression of ITGAs was significantly correlated with the infiltration of diverse immune cells in NSCLCs. ITGA5/8/9/L was also highly correlated with PD-L1 expression. The validation results for marker gene expression in NSCLC tissues by qRT-PCR, IHC, and H&E staining indicated that the expression of ITGA5/8/9/L decreased compared with that in normal tissues.

CONCLUSION

As potential prognostic biomarkers in NSCLCs, ITGA5/8/9/L may fulfill important roles in regulating tumor progression and immune cell infiltration.

Combating pancreatic cancer with ovarian cancer cells

With overall five-year survival rate less than 10%, pancreatic cancer (PC) represents the most lethal one in all human cancers. Given that the incidence of PC is still increasing and current cancer treatment strategies are often inefficacious, its therapy is still a huge challenge. Here, we first revealed ovarian serous carcinoma is mostly anti-correlated with pancreatic cancer in gene expression signatures. Based on this observation, we proposed that ovarian cancer cells could defend PC. To confirm this strategy, we first showed that ovarian cancer cell SKOV3 can significantly inhibit the proliferation of pancreatic cancer cell SW1990 when they were co-cultured. We further validated this strategy by an animal model of pancreatic cancer xenografts. The result showed that the injection of SKOV3 significantly inhibits pancreatic cancer xenografts. Moreover, we found that SKOV3 with transgenic African elephant TP53 gene further enhances the therapeutic effect. RNA-sequencing analysis revealed that the ovarian cancer cell treatment strikingly induced changes of genes being involved in pancreas function and phenotype (e.g. enhancing pancreas function, pancreas regeneration, and cell adhesion) but not immune and inflammation-related functions, suggesting that the proposed strategy is different from immunotherapy and could be a novel strategy for cancer treatment.

Involvement of abnormal dystroglycan expression and matriglycan levels in cancer pathogenesis

Dystroglycan (DG) is a glycoprotein composed of two subunits that remain non-covalently bound at the plasma membrane: α-DG, which is extracellular and heavily O-mannosyl glycosylated, and β-DG, an integral transmembrane polypeptide. α-DG is involved in the maintenance of tissue integrity and function in the adult, providing an O-glycosylation-dependent link for cells to their extracellular matrix. β-DG in turn contacts the cytoskeleton via dystrophin and participates in a variety of pathways transmitting extracellular signals to the nucleus. Increasing evidence exists of a pivotal role of DG in the modulation of normal cellular proliferation. In this context, deficiencies in DG glycosylation levels, in particular those affecting the so-called matriglycan structure, have been found in an ample variety of human tumors and cancer-derived cell lines. This occurs together with an underexpression of the DAG1 mRNA and/or its α-DG (core) polypeptide product or, more frequently, with a downregulation of β-DG protein levels. These changes are in general accompanied in tumor cells by a low expression of genes involved in the last steps of the α-DG O-mannosyl glycosylation pathway, namely POMT1/2, POMGNT2, CRPPA, B4GAT1 and LARGE1/2. On the other hand, a series of other genes acting earlier in this pathway are overexpressed in tumor cells, namely DOLK, DPM1/2/3, POMGNT1, B3GALNT2, POMK and FKTN, hence exerting instead a pro-oncogenic role. Finally, downregulation of β-DG, altered β-DG processing and/or impaired β-DG nuclear levels are increasingly found in human tumors and cell lines. It follows that DG itself, particular genes/proteins involved in its glycosylation and/or their interactors in the cell could be useful as biomarkers of certain types of human cancer, and/or as molecular targets of new therapies addressing these neoplasms.

Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

Nitric-Oxide Synthase trafficking inducer (NOSTRIN) is an emerging negative regulator of colon cancer progression

Background

NOSTRIN, abundantly expressed in colon, was reported to be anti-angiogenic, anti-invasive and anti-inflammatory. NOSTRIN expression was inversely related to survival of pancreatic ductal adeno-carcinoma patients. Yet its function and regulatory mechanism in CRC remains elusive.

Methods

NOSTRIN’s influence on EMT of CRC cells were analysed using realtime PCR array containing the functional EMT-transcriptome followed by western blotting. Regulation of oncogenic potential of CRC cells by NOSTRIN was elucidated using soft agar colony formation, trans-well invasion, wound healing and colonosphere formation assays. Biochemical assays were used to reveal mechanism of NOSTRIN function. Human CRC tissue array was used to test NOSTRIN mark in control and CRC disease stages.

Results

We showed here that CRC cell lines with less NOSTRIN expression has more invasive and migratory potential. NOSTRIN affected EMT-associated transcriptome of CRC cells by down regulating 33 genes that were functionally annotated to transcription factors, genes important for cell growth, proliferation, migration, cell adhesion and cytoskeleton regulators in CRC cells. NOSTRIN over-expression significantly reduced soft agar colony formation, wound healing and cell invasion. In line with this, RNA interference of Nostrin enhanced metastatic potential of CRC cells. Furthermore, stable overexpression of NOSTRIN in CRC cell line not only curtailed its ability to form colonosphere but also decreased expression of stemness markers CD133, CD44 and EpCAM. NOSTRIN’s role in inhibiting self-renewal was further confirmed using BrdU incorporation assay. Interestingly, NOSTRIN formed immune-complex with Cdk1 in CRC cells and aided in increase of inhibitory Y15 and T14 phosphorylation of Cdk1 that halts cytokinesis. These ex vivo findings were substantiated using human colon cancer tissue array containing cDNAs from patients’ samples with various stages of disease progression. Significant decrease in NOSTRIN expression was found with initiation and progression of advanced colon cancer disease stages.

Conclusion

We illustrate function of a novel molecule, NOSTRIN in curtailing EMT and maintenance of CRC cell stemness. Our data validates importance of NOSTRIN mark during onset and disease progression of CRC indicating its diagnostic potential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09670-6.

High expression of SLC20A1 is less effective for endocrine therapy and predicts late recurrence in ER-positive breast cancer

Estrogen receptor-positive (ER+) breast cancer intrinsically confers satisfactory clinical outcomes in response to endocrine therapy. However, a significant proportion of patients with ER+ breast cancer do not respond well to this treatment. Therefore, to evaluate the effects of endocrine therapy, there is a need for identification of novel markers that can be used at the time of diagnosis for predicting clinical outcomes, especially for early-stage and late recurrence. Solute carrier family 20 member 1 (SLC20A1) is a sodium/inorganic phosphate symporter that has been proposed to be a viable prognostic marker for the luminal A and luminal B types of ER+ breast cancer. In the present study, we examined the possible association of SLC20A1 expression with tumor staging, endocrine therapy and chemotherapy in the luminal A and luminal B subtypes of breast cancer. In addition, we analyzed the relationship between SLC20A1 expression and late recurrence in patients with luminal A and luminal B breast cancer following endocrine therapy. We showed that patients with higher levels of SLC20A1 expression (SLC20A1^high) exhibited poorer clinical outcomes in those with tumor stage I luminal A breast cancer. In addition, this SLC20A1^high subgroup of patients exhibited less responses to endocrine therapy, specifically in those with the luminal A and luminal B subtypes of breast cancer. However, patients with SLC20A1^high showed good clinical outcomes following chemotherapy. Patients tested to be in the SLC20A1^high group at the time of diagnosis also showed a higher incidence of recurrence compared with those with lower expression levels of SLC20A1, at >15 years for luminal A breast cancer and at 10–15 years for luminal B breast cancer. Therefore, we conclude that SLC20A1^high can be used as a prognostic biomarker for predicting the efficacy of endocrine therapy and late recurrence for ER+ breast cancer.

EZH2 suppresses insulinoma development by epigenetically reducing KIF4A expression via H3K27me3 modification

Kinesin family member 4A (KIF4A), located in the human chromosome band Xq13.1, is aberrantly overexpressed in various cancers. Our study intended to assess the expression of KIF4A in insulinoma and to gain new insights into the molecular mechanisms of this rare disease. First, KIF4A was significantly recruited in pancreatic endocrine cells relative to other cell types. A significant correlation existed between the overexpression of KIF4A and the poor survival of pancreatic adenocarcinoma patients. As revealed by CCK-8, TUNEL assay, flow cytometry, wound healing, Matrigel-transwell, senescence-associated β-galactosidase staining, ELISA, and subcutaneous tumor formation analysis in nude mice, knocking down KIF4A significantly inhibited the growth and metastasis of insulinoma cells in vivo and in vitro. Mechanistically, we observed that KIF4A promoter sequences had reduced H3K27me3 modifications, and decline in enhancer of zeste homolog-2 (EZH2) expression promoted KIF4A expression by reducing the modification, thus leading to insulinoma. Moreover, EZH2 knockdown-induced insulinoma cell proliferation was dependent on KIF4A overexpression since KIF4A knockdown eradicated shEZH2-induced proliferation of insulinoma cells. In summary, KIF4A was identified as a possible therapeutic target for insulinoma.

B cells in pancreatic cancer stroma

Pancreatic cancer is a disease with high unmet clinical need. Pancreatic cancer is also characterised by an intense fibrotic stroma, which harbours many immune cells. Studies in both human and animal models have demonstrated that the immune system plays a crucial role in modulating tumour onset and progression. In human pancreatic ductal adenocarcinoma, high B-cell infiltration correlates with better patient survival. Hence, B cells have received recent interest in pancreatic cancer as potential therapeutic targets. However, the data on the role of B cells in murine models is unclear as it is dependent on the pancreatic cancer model used to study. Nevertheless, it appears that B cells do organise along with other immune cells such as a network of follicular dendritic cells (DCs), surrounded by T cells and DCs to form tertiary lymphoid structures (TLS). TLS are increasingly recognised as sites for antigen presentation, T-cell activation, B-cell maturation and differentiation in plasma cells. In this review we dissect the role of B cells and provide directions for future studies to harness the role of B cells in treatment of human pancreatic cancer.

Development and validation of a novel 3-gene prognostic model for pancreatic adenocarcinoma based on ferroptosis-related genes

Background

Molecular markers play an important role in predicting clinical outcomes in pancreatic adenocarcinoma (PAAD) patients. Analysis of the ferroptosis-related genes may provide novel potential targets for the prognosis and treatment of PAAD.

Methods

RNA-sequence and clinical data of PAAD was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases. The PAAD samples were clustered by a non-negative matrix factorization (NMF) algorithm. The differentially expressed genes (DEGs) between different subtypes were used by “limma_3.42.2” package. The R software package clusterProfiler was used for functional enrichment analysis. Then, a multivariate Cox proportional and LASSO regression were used to develop a ferroptosis-related gene signature for pancreatic adenocarcinoma. A nomogram and corrected curves were constructed. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, migration and invasion assays.

Results

The 173 samples were divided into 3 categories (C1, C2, and C3) and a 3-gene signature model (ALOX5, ALOX12, and CISD1) was constructed. The prognostic model showed good independent prognostic ability in PAAD. In the GSE62452 external validation set, the molecular model also showed good risk prediction. KM-curve analysis showed that there were significant differences between the high and low-risk groups, samples with a high-risk score had a worse prognosis. The predictive efficiency of the 3-gene signature-based nomogram was significantly better than that of traditional clinical features. For comparison with other models, that our model, with a reasonable number of genes, yields a more effective result. The results obtained with qPCR and IHC assays showed that ALOX5 was highly expressed, whether ALOX12 and CISD1 were expressed at low levels in tissue samples. Finally, function assays results suggested that ALOX5 may be an oncogene and ALOX12 and CISD1 may be tumor suppressor genes.

Conclusions

We present a novel prognostic molecular model for PAAD based on ferroptosis-related genes, which serves as a potentially effective tool for prognostic differentiation in pancreatic cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02431-8.

T cells in pancreatic cancer stroma

Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with a dismal 5-year survival rate. PDAC has a complex tumour microenvironment; characterised by a robust desmoplastic stroma, extensive infiltration of immunesuppressive cells such as immature myeloid cells, tumour-associated macrophages, neutrophils and regulatory T cells, and the presence of exhausted and senescent T cells. The cross-talk between cells in this fibrotic tumour establishes an immune-privileged microenvironment that supports tumour cell escape from immune-surveillance, disease progression and spread to distant organs. PDAC tumours, considered to be non-immunogenic or cold, express low mutation burden, low infiltration of CD8+ cytotoxic lymphocytes that are localised along the invasive margin of the tumour border in the surrounding fibrotic tissue, and often display an exhausted phenotype. Here, we review the role of T cells in pancreatic cancer, examine the complex interactions of these crucial effector units within pancreatic cancer stroma and shed light on the increasingly attractive use of T cells as therapy.

The enhanced cell cycle related to the response to adjuvant therapy in pancreatic ductal adenocarcinoma

A major clinical challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is identifying those that may benefit from adjuvant chemotherapy versus those that will not. Thus, there is a need for a robust and convenient biomarker for predicting chemotherapy response in PDAC patients. In this study, network inference was conducted by integrating the differentially expressed cell cycle signatures and target genes between the basal-like subtype and classical subtype of PDAC. As a result from this statistical analysis, two dominant cell cycle genes, RASAL2 and ASPM, were identified. Based on the expression levels of these two genes, we constructed a "Enhanced Cell Cycle" scoring system (ECC score). Patients were given an ECC score, and respectively divided into ECC-high and ECC-low groups. Survival, pathway enrichment, immune environment characteristics, and chemotherapy response analysis' were performed between the two groups in a total of 891 patients across 5 cohorts. ECC-high patients exhibited shortened recurrence-free survival (RFS) and overall survival (OS) rates. In addition, it was found that adjuvant chemotherapy could significantly improve the outcome of the ECC-high patients while ECC-low patients did not benefit from adjuvant chemotherapy. It was also found that there was less CD8+ T cell, natural killer (NK) cell, M1 macrophage, and plasma cell infiltration in ECC-high patients when compared to ECC-low patients. Also, the expression of CD73, an immune suppressor gene, and it's related hypoxia pathway were elevated in the ECC-high group when compared to the ECC-low group. In conclusion, this study showed that patients characterized as ECC-high not only had reduced RFS and OS rates, but were also more sensitive to adjuvant chemotherapy and could potentially be less sensitive to immune checkpoint inhibitors. Being able to characterize patients by these parameters would allow doctors to make more informed decisions on patient treatment regimens.

KIF4A Regulates the Progression of Pancreatic Ductal Adenocarcinoma through Proliferation and Invasion

BACKGROUND

Pancreatic cancer is a malignant tumor of the digestive tract, which is difficult to diagnose and treat due to bad early diagnosis. We aimed to explore the role of kinesin superfamily 4A (KIF4A) in pancreatic ductal adenocarcinoma (PDAC).

METHODS

We first used the bioinformatic website to screen the data of pancreatic cancer in TCGA, and KIF4A protein was detected among the 86 specimens of patients in our hospital combined with clinic-pathological characteristics and survival analysis. KIF4A loss-expression cell lines were established by RNA interference (RNAi). In addition, we performed in vitro cell assays to detect the changes in cell proliferation, migration, and invasion. The proteins involved in the proliferation and metastasis of cancer cells were also detected by western blot. The above results could be proved in vivo. Further, the correlation between KIF4A and CDC5L was analyzed by TCGA and IHC data.

RESULTS

We first found a high expression of KIF4A in pancreatic cancer, suggesting a role of KIF4A in the development of pancreatic cancer. KIF4A was found to be differentially expressed (P < 0.05) among the 86 specimens of patients in our hospital and was significantly associated with PDAC TNM stages and tumor size. High KIF4A expression also significantly worsened overall survival (OS) and disease-free survival rate (DFS) (P < 0.05, respectively). In addition, cell proliferation, migration, and invasion were inhibited by the KIF4A-shRNA group compared with the control (P < 0.05, respectively). In the end, knockdown of KIF4A could inhibit tumor development and metastasis in vivo. Further, the positive correlation between KIF4A and CDC5L existed, and KIF4A might promote pancreatic cancer proliferation by affecting CDC5L expression.

CONCLUSION

In conclusion, the high expression level of KIF4A in PDAC was closely related to poor clinical and pathological status, lymphatic metastasis, and vascular invasion. KIF4A might be involved in promoting the development of PDAC in vitro and in vivo, which might be a new therapeutic target of PDAC.

Laryngeal Squamous Cell Carcinoma: Clinical Significance and Potential Mechanism of Cell Division Cycle 45

Background: Cell division cycle 45 (CDC45) plays an important role in the occurrence and development of numerous carcinomas, but its effect in laryngeal squamous cell carcinoma (LSCC) remains unclear. Materials and Methods: The messenger RNA and protein expression levels of CDC45 in LSCC were evaluated with a t test and the standard mean difference (SMD). The ability of CDC45 expression to distinguish the LSCC was assessed through receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA), protein-protein interaction, public databases, and online tools were used to explore the potential molecular mechanism of CDC45 in LSCC. Results: A high expression of CDC45 was identified in LSCC (SMD = 2.61, 95% confidence interval [1.62-3.61]). Through ROC curves, the expression of CDC45 makes it feasible to distinguish the LSCC group from the non-LSCC counterpart. CDC45 was relevant to the progression-free interval of LSCC patients (log-rank p = 0.03). GSEAs show that CDC45 is related to the cell cycle. CDC45, CDC6, KIF2C, and AURKB were identified as hub genes of LSCC. E2F1 may be the regulatory transcription factor of CDC45. Conclusions: High expression of CDC45 likely demonstrates carcinogenic effects in LSCC, and CDC45 is a potential target in screening and treatment of LSCC.

Clinical importance of preoperative red-cell volume distribution width as a prognostic marker in patients undergoing radical surgery for pancreatic cancer

Background and purpose

A new noninvasive biomarker is being sought to predict the prognosis of patients with pancreatic cancer. Red-cell volume distribution width (RDW), a descriptive parameter for erythrocyte variation, has been shown to have prognostic value for some tumor types. Our purpose was to assess the RDW value to predict the prognosis of patients with pancreatic cancer.

Methods

The subjects of this retrospective study were 792 patients who underwent radical surgery for pancreatic cancer, divided into high-RDW and low-RDW groups based on receiver operating characteristic (ROC) curve analysis (15.6%). The controlling nutritional status (CONUT) score was used to assess preoperative nutritional status. Statistical analysis was conducted to investigate the differences between the high and low RDW groups, and to explore the possibility of the RDW being used as prognostic predictor for patients with pancreatic cancer.

Results

The immune-nutritional status was worse in the high-RDW group than in the low-RDW group. The high-RDW group patients also had a poorer prognosis. Risk factor analysis showed that the RDW could be an independent risk factor for pancreatic cancer.

Conclusions

The RDW is associated with immune-nutritional status in pancreatic cancer patients and can be used as an independent prognostic factor for their postoperative survival.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00595-021-02374-7.

Genomic Heterogeneity of Pancreatic Ductal Adenocarcinoma and Its Clinical Impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer death due to limited advances in recent years in early diagnosis and personalized therapy capable of overcoming tumor resistance to chemotherapy. In the last decades, significant advances have been achieved in the identification of recurrent genetic and molecular alterations of PDAC including those involving the KRAS, CDKN2A, SMAD4, and TP53 driver genes. Despite these common genetic traits, PDAC are highly heterogeneous tumors at both the inter- and intra-tumoral genomic level, which might contribute to distinct tumor behavior and response to therapy, with variable patient outcomes. Despite this, genetic and genomic data on PDAC has had a limited impact on the clinical management of patients. Integration of genomic data for classification of PDAC into clinically defined entities-i.e., classical vs. squamous subtypes of PDAC-leading to different treatment approaches has the potential for significantly improving patient outcomes. In this review, we summarize current knowledge about the most relevant genomic subtypes of PDAC including the impact of distinct patterns of intra-tumoral genomic heterogeneity on the classification and clinical and therapeutic management of PDAC.

Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer

PURPOSE

To identify differentially expressed immune-related genes (DEIRGs) and construct a model with survival-related DEIRGs for evaluating the prognosis of patients with pancreatic cancer (PC).

METHODS

Six microarray gene expression datasets of PC from the Gene Expression Omnibus (GEO) and Immunology Database and Analysis Portal (ImmPort) were used to identify DEIRGs. RNA sequencing and clinical data from The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma (TCGA-PAAD) database were used to establish the prognostic model. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to determine the final variables of the prognostic model. The median risk score was used as the cut-off value to classify samples into low- and high-risk groups. The prognostic model was further validated using an internal validation set of TCGA and an external validation set of GSE62452.

RESULTS

In total, 142 DEIRGs were identified from six GEO datasets, 47 were survival-related DEIRGs. A prognostic model comprising five genes (i.e., ERAP2, CXCL9, AREG, DKK1, and IL20RB) was established. High-risk patients had poor survival compared with low-risk patients. The 1-, 2-, 3-year area under the receiver operating characteristic (ROC) curve of the model reached 0.85, 0.87, and 0.93, respectively. Additionally, the prognostic model reflected the infiltration of neutrophils and dendritic cells. The expression of most characteristic immune checkpoints was significantly higher in the high-risk group versus the low-risk group.

CONCLUSIONS

The five-gene prognostic model showed reliably predictive accuracy. This model may provide useful information for immunotherapy and facilitate personalized monitoring for patients with PC.

Construction and Validation of an Immune-Based Prognostic Model for Pancreatic Adenocarcinoma Based on Public Databases

Background

Pancreatic adenocarcinoma (PAAD) is a highly lethal and aggressive tumor with poor prognoses. The predictive capability of immune-related genes (IRGs) in PAAD has yet to be explored. We aimed to explore prognostic-related immune genes and develop a prediction model for indicating prognosis in PAAD.

Methods

The messenger (m)RNA expression profiles acquired from public databases were comprehensively integrated and differentially expressed genes were identified. Univariate analysis was utilized to identify IRGs that related to overall survival. Whereafter, a multigene signature in the Cancer Genome Atlas cohort was established based on the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Moreover, a transcription factors regulatory network was constructed to reveal potential molecular processes in PAAD. PAAD datasets downloaded from the Gene Expression Omnibus database were applied for the validations. Finally, correlation analysis between the prognostic model and immunocyte infiltration was investigated.

Results

Totally, 446 differentially expressed immune-related genes were screened in PAAD tissues and normal tissues, of which 43 IRGs were significantly related to the overall survival of PAAD patients. An immune-based prognostic model was developed, which contained eight IRGs. Univariate and multivariate Cox regression revealed that the risk score model was an independent prognostic indicator in PAAD (HR > 1, P < 0.001). Besides, the sensitivity of the model was evaluated by the receiver operating characteristic curve analysis. Finally, immunocyte infiltration analysis revealed that the eight-gene signature possibly played a pivotal role in the status of the PAAD immune microenvironment.

Conclusion

A novel prognostic model based on immune genes may serve to characterize the immune microenvironment and provide a basis for PAAD immunotherapy.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods

Background

Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.

Methods

Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.

Results

A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.

Conclusion

Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

Characterization of the Survival Influential Genes in Carcinogenesis

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

A bioinformatic analysis of the inhibin-betaglycan-endoglin/CD105 network reveals prognostic value in multiple solid tumors

Inhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG/CD105 in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients' response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.

Identification and Validation of the Prognostic Stemness Biomarkers in Bladder Cancer Bone Metastasis

Background

Bladder urothelial carcinoma (BLCA) is one of the most common urinary system malignancies with a high metastasis rate. Cancer stem cells (CSCs) play an important role in the occurrence and progression of BLCA, however, its roles in bone metastasis and the prognostic stemness biomarkers have not been identified in BLCA.

Method

In order to identify the roles of CSC in the tumorigenesis, bone metastasis and prognosis of BLCA, the RNA sequencing data of patients with BLCA were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between mRNAsi and the tumorigenesis, bone metastasis, clinical stage and overall survival (OS) were also established. The key prognostic stemness-related genes (PSRGs) were screened by Lasso regression, and based on them, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve and the risk score. Additionally, in order to explore the key regulatory network, the relationship among differentially expressing TFs, PSRGs, and absolute quantification of 50 hallmarks of cancer were also identified by Pearson correlation analysis. To verify the identified key TFs and PSRGs, their expression levels were identified by our clinical samples via immunohistochemistry (IHC).

Results

A total of 8,647 DEGs were identified between 411 primary BLCAs and 19 normal solid tissue samples. According to the clinical stage, mRNAsi and bone metastasis of BLCA, 2,383 stage-related DEGs, 3,680 stemness-related DEGs and 716 bone metastasis-associated DEGs were uncovered, respectively. Additionally, compared with normal tissue, mRNAsi was significantly upregulated in the primary BLCA and also associated with the prognosis (P = 0.016), bone metastasis (P < 0.001) and AJCC clinical stage (P < 0.001) of BLCA patients. A total of 20 PSRGs were further screened by Lasso regression, and based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.699). Moreover, we found two key TFs (EPO, ARID3A), four key PRSGs (CACNA1E, LINC01356, CGA and SSX3) and five key hallmarks of cancer gene sets (DNA repair, myc targets, E2F targets, mTORC1 signaling and unfolded protein response) in the regulatory network. The tissue microarray of BLCA and BLCA bone metastasis also revealed high expression of the key TFs (EPO, ARID3A) and PRSGs (SSX3) in BLCA.

Conclusion

Our study identifies mRNAsi as a reliable index in predicting the tumorigenesis, bone metastasis and prognosis of patients with BLCA and provides a well-applied model for predicting the OS for patients with BLCA based on 20 PSRGs. Besides, we also identified the regulatory network between key PSRGs and cancer gene sets in mediating the BLCA bone metastasis.

Identification of an Immune-Related Signature for Predicting Prognosis in Patients With Pancreatic Ductal Adenocarcinoma

PURPOSE

Pancreatic ductal adenocarcinoma (PDAC) is one of the highest fatality rate cancers with poor survival rates. The tumor microenvironment (TME) is vital for tumor immune responses, leading to resistance to chemotherapy and poor prognosis of PDAC patients. This study aimed to provide a comprehensive evaluation of the immune genes and microenvironment in PDAC that might help in predicting prognosis and guiding clinical treatments.

METHODS

We developed a prognosis-associated immune signature (i.e., PAIS) based on immune-associated genes to predict the overall survival of patients with PDAC. The clinical significance and immune landscapes of the signature were comprehensively analyzed.

RESULTS

Owing to gene expression profiles from TCGA database, functional enrichment analysis revealed a significant difference in the immune response between PDAC and normal pancreas. Using transcriptome data analysis of a training set, we identified an immune signature represented by 5 genes (ESR2, IDO1, IL20RB, PPP3CA, and PLAU) related to the overall survival of patients with PDAC, significantly. This training set was well-validated in a test set. Our results indicated a clear association between a high-risk score and a very poor prognosis. Stratification analysis and multivariate Cox regression analysis revealed that PAIS was an important prognostic factor. We also found that the risk score was positively correlated with the inflammatory response, antigen-presenting process, and expression level of some immunosuppressive checkpoint molecules (e.g., CD73, PD-L1, CD80, and B7-H3). These results suggested that high-risk patients had a suppressed immune response. However, they could respond better to chemotherapy. In addition, PAIS was positively correlated with the infiltration of M2 macrophages in PDAC.

CONCLUSIONS

This study highlighted the relationship between the immune response and prognosis in PDAC and developed a clinically feasible signature that might serve as a powerful prognostic tool and help further optimize the cancer therapy paradigm.

An Efficient and Easy-to-Use Network-Based Integrative Method of Multi-Omics Data for Cancer Genes Discovery

Identifying personalized driver genes is essential for discovering critical biomarkers and developing effective personalized therapies of cancers. However, few methods consider weights for different types of mutations and efficiently distinguish driver genes over a larger number of passenger genes. We propose MinNetRank (Minimum used for Network-based Ranking), a new method for prioritizing cancer genes that sets weights for different types of mutations, considers the incoming and outgoing degree of interaction network simultaneously, and uses minimum strategy to integrate multi-omics data. MinNetRank prioritizes cancer genes among multi-omics data for each sample. The sample-specific rankings of genes are then integrated into a population-level ranking. When evaluating the accuracy and robustness of prioritizing driver genes, our method almost always significantly outperforms other methods in terms of precision, F1 score, and partial area under the curve (AUC) on six cancer datasets. Importantly, MinNetRank is efficient in discovering novel driver genes. SP1 is selected as a candidate driver gene only by our method (ranked top three), and SP1 RNA and protein differential expression between tumor and normal samples are statistically significant in liver hepatocellular carcinoma. The top seven genes stratify patients into two subtypes exhibiting statistically significant survival differences in five cancer types. These top seven genes are associated with overall survival, as illustrated by previous researchers. MinNetRank can be very useful for identifying cancer driver genes, and these biologically relevant marker genes are associated with clinical outcome. The R package of MinNetRank is available at https://github.com/weitinging/MinNetRank.

Immune signature-based risk stratification and prediction of immune checkpoint inhibitor's efficacy for lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a common pulmonary malignant disease with a poor prognosis. There were limited studies investigating the influences of the tumor immune microenvironment on LUAD patients' survival and response to immune checkpoint inhibitors (ICIs).

METHODS

Based on TCGA-LUAD dataset, we constructed a prognostic immune signature and validated its predictive capability in the internal as well as total datasets. Then, we explored the differences of tumor-infiltrating lymphocytes, tumor mutation burden, and patients' response to ICI treatment between the high-risk score group and low-risk score group.

RESULTS

This immune signature consisted of 17 immune-related genes, which was an independent prognostic factor for LUAD patients. In the low-risk score group, patients had better overall survival. Although the differences were non-significant, patients with low-risk scores had more tumor-infiltrating follicular helper T cells and fewer macrophages (M0), which were closely related to clinical outcomes. Additionally, the total TMB was markedly decreased in the low-risk score group. Using immunophenoscore as a surrogate of ICI response, we found that patients with low-risk scores had significantly higher immunophenoscore.

CONCLUSION

The 17-immune-related genes signature may have prognostic and predictive relevance with ICI therapy but needs prospective validation.

B3GNT3: A prognostic biomarker associated with immune cell infiltration in pancreatic adenocarcinoma

Pancreatic cancer, one of the most malignant gastrointestinal tumors, is prone to liver metastasis. However, due to the lack of appropriate and comprehensive diagnostic methods, it is difficult to accurately predict the survival outcomes. Therefore, there is a need to identify effective biomarkers, such as UDP-GlcNAc: βGal β-1,3-N-acetylglucosaminyltransferase 3 (B3GNT3), that predict the survival outcome of patients with pancreatic cancer. In the present study, based on data from 171 cases of pancreatic cancer obtained from The Cancer Genome Atlas portal, the differential expression of mRNAs was screened by comparing cancerous tissues with adjacent tissues. Univariate Cox regression analysis demonstrated that B3GNT3 had prognostic capability and could be an independent prognostic factor for pancreatic adenocarcinoma (PAAD). Using the Tumor Immune Estimation Resource tool and Tumor-Immune System Interaction Database, a potential relationship between B3GNT3 expression and immune cell infiltration was identified in pancreatic carcinoma. Furthermore, 177 samples of pancreatic carcinoma were collected and the association of CD68 expression with B3GNT3 was assessed by immunohistochemical staining. B3GNT3 expression was associated with clinical outcomes in pancreatic carcinoma and related to infiltrating levels of immune cells, which indicated that B3GNT3 could be used as an immunotherapy target for PAAD.

miR-29c-3p regulates proliferation and migration in ovarian cancer by targeting KIF4A

Background

Increasing evidence suggested that microRNA and kinesin superfamily proteins play an essential role in ovarian cancer. The association between KIF4A and ovarian cancer (OC) was investigated in this study.

Methods

We performed bioinformatics analysis in the GEO database to screen out the differentially expressed miRNAs (DEmiRNAs) associated with ovarian cancer prognosis. Upstream targeting prediction for KIF4A was acquired by using the mirDIP database. The potential regulatory factor miR-29c-3p for KIF4A was obtained from the intersection of the above all miRNAs. The prognosis of KIF4A and target-miRNA in OC was obtained in the subsequent analysis. qRT-PCR and Western blot detected KIF4A expression level in IOSE80 (human normal ovarian epithelial cell line). In the meantime, the gene expression level was detected in A2780, HO-8910PM, COC1, and SKOV3 cell lines (human ovarian carcinoma cell line). MTT and colony formation assays were used to detect cell proliferation of SKOV3 cell line. The following assays detected cell migration through the use of transwell and wound heal assays. Targeted binding relationship between KIF4A and miRNA was detected by using the dual-luciferase reporter assay.

Results

Both high expression of KIF4A and lower expression of miR-29c-3p could be used as biomarkers indicating poor prognosis in OC patients. Cellular function tests confirmed that when KIF4A was silenced, it inhibited the proliferation and migration of OC cells. In addition, 3′-UTR of KIF4A had a direct binding site with miR-29c-3p, which indicated that the expression of KIF4A could be regulated by miR-29c-3p. In subsequent assays, the proliferation and migration of OC cells were inhibited by the overexpression of miR-29c-3p. At the same time, rescue experiments also confirmed that the promotion of KIF4A could be reversed by miR-29c-3p.

Conclusion

In a word, our data revealed a new mechanism for the role of KIF4A in the occurrence and development of OC.

Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues

A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed and 168 repressed genes in PDAC compared to the adjacent-to-tumor samples. The validation analysis using TCGA combined GTEx data confirmed 98.24% of the identified up-regulated and 73.88% of the down-regulated protein-coding genes in PDAC. Pathway enrichment analysis showed that “ECM-receptor interaction”, “PI3K-Akt signaling pathway”, and “focal adhesion” are the most enriched KEGG pathways in PDAC. Protein-protein interaction analysis identified FN1, TIMP1, and MSLN as the most highly ranked hub genes among the DEGs. Transcription factor enrichment analysis revealed that TCF7, CTNNB1, SMAD3, and JUN are significantly activated in PDAC, while SMAD7 is inhibited. The prognostic significance of the identified and validated differentially expressed genes in PDAC was evaluated via survival analysis of TCGA Pan-Cancer pancreatic ductal adenocarcinoma data. The identified candidate prognostic biomarkers were then validated in four external validation datasets (GSE21501, GSE50827, GSE57495, and GSE71729) to further improve reliability. A total of 28 up-regulated genes were found to be significantly correlated with worse overall survival in patients with PDAC. Twenty-one of the identified prognostic genes (ITGB6, LAMC2, KRT7, SERPINB5, IGF2BP3, IL1RN, MPZL2, SFTA2, MET, LAMA3, ARNTL2, SLC2A1, LAMB3, COL17A1, EPSTI1, IL1RAP, AK4, ANXA2, S100A16, KRT19, and GPRC5A) were also found to be significantly correlated with the pathological stages of the disease. The results of this study provided promising prognostic biomarkers that have the potential to differentiate PDAC from both healthy and adjacent-to-tumor pancreatic tissues. Several novel dysregulated genes merit further study as potentially promising candidates for the development of more effective treatment strategies for PDAC.

Comprehensive analysis of prognostic immune-related genes associated with the tumor microenvironment of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a specific tumor immune microenvironment (TIME). Therefore, investigating prognostic immune-related genes (IRGs) that are closely associated with TIME to predict PDAC clinical outcomes is necessary. In the present study, 459 samples of PDAC from the Genotype-Tissue Expression database, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) were included and a survival-associated module was identified using weighted gene co-expression network analysis. Based on the Cox regression analysis and least absolute shrinkage and selection operator analysis, four IRGs (2′-5′-oligoadenylate synthetase 1, MET proto-oncogene, receptor tyrosine kinase, interleukin 1 receptor type 2 and interleukin 20 receptor subunit β) were included in the prognostic model to calculate the risk score (RS), and patients with PDAC were divided into high- and low-RS groups. Kaplan-Meier survival and receiver operating characteristic curve analyses demonstrated that the low-RS group had significantly improved survival conditions compared with the high-RS group in TCGA training set. The prognostic function of the model was also validated using ICGC and GEO cohorts. To investigate the mechanism of different overall survival between the high- and low-RS groups, the present study included Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data and Cell Type Identification by Estimating Relative Subset of Known RNA Transcripts algorithms to investigate the state of the tumor microenvironment and immune infiltration inpatients in the cohort from TCGA. In summary, four genes associated with the TIME of PDAC were identified, which may provide a reference for clinical treatment.

CXCL5 expression in tumor tissues is associated with poor prognosis in patients with pancreatic cancer

Immunotherapy based on the tumor microenvironment is a feasible method for treating cancer; therefore, it is necessary to investigate the immune microenvironment of pancreatic cancer and the influencing factors of the immune microenvironment. Chemokines are an important factor affecting the tumor immune microenvironment. In the present study, chemokines or chemokine receptors were screened to identify those differentially expressed in pancreatic cancer compared with normal controls and associated with patient prognosis. Chemokines or chemokine receptors that are differentially expressed in pancreatic cancer tumor tissues were initially screened using the Gene Expression Omnibus database. Next, survival analysis was performed using GEPIA, a website based on The Cancer Genome Atlas (TCGA) database. Immunohistochemical staining of CXCL5 was performed in tissue microarrays (TMAs) containing 119 cases of pancreatic cancer. Histochemistry score (H-SCORE) was used to evaluate the expression of CXCL5. Next, association analysis of the H-SCORE of CXCL5 and the clinical characteristics of patients was performed, as well as Kaplan-Meier survival and Cox multivariate regression analyses. The results of the bioinformatics analysis demonstrated that CXCL5 was highly expressed in pancreatic cancer tissues. High expression of CXCL5 in pancreatic cancer tissues was associated with a poor prognosis in patients in TCGA cohort. The expression level of CXCL5 in tumor tissues was significantly higher compared with that in adjacent peritumoral normal tissues in the immunohistochemical analysis. There was no significant association between CXCL5 expression in pancreatic cancer tumor tissues and clinicopathological factors. Patients with pancreatic cancer with high CXCL5 expression had a poor prognosis, as determined by Kaplan-Meier survival analysis based on the TMA dataset. The results of Cox multivariate regression analysis showed that CXCL5 was an independent factor for a poor prognosis in patients with pancreatic cancer. In conclusion, the results of the present study revealed that the chemokine CXCL5 was highly expressed in pancreatic cancer tissues; high CXCL5 expression was associated with a poor prognosis in patients with pancreatic cancer.

Development and validation of a cancer stem cell-related signature for prognostic prediction in pancreatic ductal adenocarcinoma

Background

Cancer stem cells (CSCs) are crucial to the malignant behaviour and poor prognosis of pancreatic ductal adenocarcinoma (PDAC). In recent years, CSC biology has been widely studied, but practical prognostic signatures based on CSC-related genes have not been established or reported in PDAC.

Methods

A signature was developed and validated in seven independent PDAC datasets. The MTAB-6134 cohort was used as the training set, while one local Chinese cohort and five other public cohorts were used for external validation. CSC-related genes with credible prognostic roles were selected to form the signature, and their predictive performance was evaluated by Kaplan–Meier survival, receiver operating characteristic (ROC), and calibration curves. Correlation analysis was employed to clarify the potential biological characteristics of the gene signature.

Results

A robust signature comprising DCBLD2, GSDMD, PMAIP1, and PLOD2 was developed. It classified patients into high-risk and low-risk groups. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) than low-risk patients. Calibration curves and Cox regression analysis demonstrated powerful predictive performance. ROC curves showed the better survival prediction by this model than other models. Functional analysis revealed a positive association between risk score and CSC markers. These results had cross-dataset compatibility.

Impact

This signature could help further improve the current TNM staging system and provide data for the development of novel personalized therapeutic strategies in the future.

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.

Irinotecan and vandetanib create synergies for treatment of pancreatic cancer patients with concomitant TP53 and KRAS mutations

BACKGROUND

The most frequently mutated gene pairs in pancreatic adenocarcinoma (PAAD) are KRAS and TP53, and our goal is to illustrate the multiomics and molecular dynamics landscapes of KRAS/TP53 mutation and also to obtain prospective novel drugs for KRAS- and TP53-mutated PAAD patients. Moreover, we also made an attempt to discover the probable link amid KRAS and TP53 on the basis of the abovementioned multiomics data.

METHOD

We utilized TCGA & Cancer Cell Line Encyclopedia data for the analysis of KRAS/TP53 mutation in a multiomics manner. In addition to that, we performed molecular dynamics analysis of KRAS and TP53 to produce mechanistic descriptions of particular mutations and carcinogenesis.

RESULT

We discover that there is a significant difference in the genomics, transcriptomics, methylomics, and molecular dynamics pattern of KRAS and TP53 mutation from the matching wild type in PAAD, and the prognosis of pancreatic cancer is directly linked with a particular mutation of KRAS and protein stability. Screened drugs are potentially effective in PAAD patients.

CONCLUSIONS

KRAS and TP53 prognosis of PAAD is directly associated with a specific mutation of KRAS. Irinotecan and vandetanib are prospective drugs for PAAD patients with KRASG12Dmutation and TP53 mutation.

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.

The kinesin motor protein KIF4A as a potential therapeutic target in renal cell carcinoma

Kinesin family member 4A (KIF4A) is located in the human chromosome band Xq13.1. It has a highly conserved kinesin motor region at its N-terminus, which is followed by a central coiled-coil region and a C-terminus cargo-binding domain that contains a cysteine-rich motif. It is aberrantly expressed in a variety of cancers. Our study aimed to determine the expression of KIF4A in renal cell carcinoma (RCC) and to gain new insights into the underlying molecular mechanisms of this disease. Here, we found that KIF4A expression in RCC specimens increased relative to that in normal renal tissues. A significant correlation existed between the expression of KIF4A and the clinicopathologic features of RCC. Elevated KIF4A expression was associated with poor overall survival and disease-free survival. Univariate and multivariate Cox regression analysis revealed that KIF4A was an independent prognostic factor for poor survival in human patients with RCC. CCK-8 proliferation assay, cell cycle analysis, and subcutaneous tumor formation analysis in nude mice consistently showed that KIF4A promoted RCC proliferation. Our findings also indicated that KIF4A functions as an accelerator of RCC metastasis as certified through transwell chamber analysis, wound healing assay, and angiogenesis assay. The expression levels of cyclin D1, cyclin E2, matrix metalloproteinase-2, matrix metalloproteinase-9, hypoxia-inducible factor 1α, and vascular endothelial growth factor in the KIF4A knockdown group were lower than those in the control group and were consistent with those in classic oncogenic pathways. These findings implied that the expression of KIF4A was significantly related to the tumor incidence, metastasis, and prognosis of patients with RCC. Our work provides new breakthroughs for the diagnosis and treatment of RCC.

Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis

Pancreatic cancer is a malignancy of the digestive system characterized by poor prognosis. A number of prognostic messenger RNA (mRNA) signatures have been identified by using the high-throughput expression profiles. MicroRNAs (miRNA) play a critical role in regulating multiple cellular functions. However, no such integrated analysis of miRNAs and mRNAs for studying the prognostic mechanisms of pancreatic cancer has been reported. In this study, we first identified prognostic mRNAs and miRNAs based on The Cancer Genome Atlas datasets, and then performed an enrichment analysis to explore the underlying biological mechanisms involved in pancreatic cancer prognosis at the mRNA level. Furthermore, we performed an integrated analysis of mRNAs and miRNAs to identify prognostic subpathways, which were closely associated with pancreatic cancer genes and tumor hallmarks and involved in hypoxia, oxidative phosphyorylation and xenobiotic metabolisms. Meanwhile, we performed a random walk algorithm based on global network, prognostic mRNAs and miRNAs, and identified top risk mRNAs as the prognostic signature. Finally, an independent testing set was used to confirm the predictive power of the top mRNA signature, and most of these genes involved were known oncogenes. In conclusion, we performed a series of integrated analyses by comprehensively exploring pancreatic cancer prognosis and systematically optimized the prognostic signature for clinical use.