Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma

Upregulation of CENPF is linked to aggressive features of osteosarcoma

Centromere protein F (CENPF) plays a key role in the regulation of the cell cycle. The present study revealed that CENPF was overexpressed in a variety of tumors and associated with the poor prognosis of osteosarcoma. The mRNA expression levels of CENPF were analyzed using the Gene Expression Profiling Interactive Analysis database and the protein levels of CENPF were detected in the specimens from patients with osteosarcoma using immunohistochemistry. Cell proliferation, cell cycle and flow cytometry assays were performed after the transfection of control or CENPF plasmids into osteosarcoma cells. A xenografts assay was used to determine the effects of CENPF on tumor growth in vivo. The results showed that CENPF was upregulated in osteosarcoma tissues and associated with high-grade tumor stage (P=0.023) and intraglandular dissemination (P=0.046). The transfection-induced depletion of CENPF in human osteosarcoma MG-63 and U-2 OS cell lines inhibited cell proliferation, stimulated apoptosis and induced cell cycle arrest. Induced CENPF depletion in MG-63 cells inhibited tumor growth of osteosarcoma cells in mice. These findings suggested that elevated CENPF levels contributed to increased cell proliferation by mediating apoptosis and cell cycle in osteosarcoma. Therefore, CENPF might be a potential biomarker for poor prognosis of osteosarcoma.

Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model

Pancreatic cancer (PanC) is an intractable malignancy with a high mortality. Metabolic processes contribute to cancer progression and therapeutic responses, and histopathological subtypes are insufficient for determining prognosis and treatment strategies. In this study, PanC subtypes based on metabolism-related genes were identified and further utilized to construct a prognostic model. Using a cohort of 171 patients from The Cancer Genome Atlas (TCGA) database, transcriptome data, simple nucleotide variants (SNV), and clinical information were analyzed. We divided patients with PanC into metabolic gene-enriched and metabolic gene-desert subtypes. The metabolic gene-enriched subgroup is a high-risk subtype with worse outcomes and a higher frequency of SNVs, especially in KRAS. After further characterizing the subtypes, we constructed a risk score algorithm involving multiple genes (i.e., NEU2, GMPS, PRIM2, PNPT1, LDHA, INPP4B, DPYD, PYGL, CA12, DHRS9, SULT1E1, ENPP2, PDE1C, TPH1, CHST12, POLR3GL, DNMT3A, and PGS1). We verified the reproducibility and reliability of the risk score using three validation cohorts (i.e., independent datasets from TCGA, Gene Expression Omnibus, and Ensemble databases). Finally, drug prediction was completed using a ridge regression model, yielding nine candidate drugs for high-risk patients. These findings support the classification of PanC into two metabolic subtypes and further suggest that the metabolic gene-enriched subgroup is associated with worse outcomes. The newly established risk model for prognosis and therapeutic responses may improve outcomes in patients with PanC.

TMC4 is a novel chloride channel involved in high-concentration salt taste sensation

"Salty taste" sensation is evoked when sodium and chloride ions are present together in the oral cavity. The presence of an epithelial cation channel that receives Na+ has previously been reported. However, no molecular entity involving Cl- receptors has been elucidated. We report the strong expression of transmembrane channel-like 4 (TMC4) in the circumvallate and foliate papillae projected to the glossopharyngeal nerve, mediating a high-concentration of NaCl. Electrophysiological analysis using HEK293T cells revealed that TMC4 was a voltage-dependent Cl- channel and the consequent currents were completely inhibited by NPPB, an anion channel blocker. TMC4 allowed permeation of organic anions including gluconate, but their current amplitudes at positive potentials were less than that of Cl-. Tmc4-deficient mice showed significantly weaker glossopharyngeal nerve response to high-concentration of NaCl than the wild-type littermates. These results indicated that TMC4 is a novel chloride channel that responds to high-concentration of NaCl.

Type II transmembrane serine proteases 4 (TMPRSS4) promotes proliferation, invasion and epithelial-mesenchymal transition in endometrial carcinoma cells (HEC1A and Ishikawa) via activation of MAPK and AKT

Endometrial cancer is the most common gynecological cancer in the developed countries. Type II transmembrane serine proteases 4 (TMPRSS4) is a newly discovered transmembrane protein, which may be related to the invasion, metastasis of the tumor and the poor prognosis. This study aims to investigate the role of TMPRSS4 in endometrial cancer and the detailed molecular mechanism. The results showed that TMPRSS4 was highly expressed in human endometrial cancer cells (HEC1A and Ishikawa). TMPRSS4 knockdown inhibited proliferation of endometrial cancer cells. In TMPRSS4 knockdown cells, the invasion of cells was significantly supressed. The expression of E-cadherin was significantly enhanced, while the levels of fibronectin and vimentin decreased in TMPRSS4 knockdown cells, which indicated thatTMPRSS4 knockdown attenuated the EMT of cancer cells. TMPRSS4 positively regulated the activation of MAPK and AKT signaling pathways in endometrial cancer. In conclusion, this study indicated that TMPRSS4 may be associated with the progression of endometrial cancer through promoting proliferation, invasion and EMT via activation of MAPK and AKT in endometrial cancer cells. TMPRSS4 may be a new and more effective target or therapeutic strategy for treating endometrial cancer.

miR-383-5p inhibits human malignant melanoma cells function via targeting CENPF

Human malignant melanoma (MM), is a type of skin cancer with high morbidity and mortality. In this study, we investigated the role of miR-383-5p in human MM cells in vitro. miR-383-5p expression was downregulated in MM cell lines compared with the human normal melanocyte cell line, and miR-383-5p overexpression inhibited the proliferation, migration, and invasion of M14 and A375 cells. Furthermore, miR-383-5p was able to effectively bind to the 3'UTR of CENPF mRNA. miR-383-5p expression was negatively correlated with CENPF expression and miR-383-5p overexpression inhibited CENPF protein expression in M14 and A375 cells. The overexpression of CENPF could effectively rescue the inhibitory effect on proliferation and invasion caused by miR-383-5p. Additionally, using publicly available databases, we showed that CENPF expression was upregulated in human MM tissues and could predict the prognosis of MM. In conclusion, miR-383-5p acts as a tumor suppressor in human MM by targeting CENPF, suggesting CENPF as a potential therapeutic target for human MM.

Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

BACKGROUND

The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs.

METHODS

Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established a prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. The relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.

RESULTS

The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5), and the risk score was demonstrated to be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The low-risk group had a better prognosis than the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and the adherens junction. The prognostic model may be associated with infiltration of immune cells such as M0 macrophages, M1 macrophages, CD4 + T cells and CD8 + T cells.

CONCLUSION

The ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis is an important marker, and immunotherapy may be a potential therapeutic target for pancreatic cancer.

Immune Cell Modulation of the Extracellular Matrix Contributes to the Pathogenesis of Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a five-year survival rate of only 9%. PDAC is characterized by a dense, fibrotic stroma composed of extracellular matrix (ECM) proteins. This desmoplastic stroma is a hallmark of PDAC, representing a significant physical barrier that is immunosuppressive and obstructs penetration of cytotoxic chemotherapy agents into the tumor microenvironment (TME). Additionally, dense ECM promotes hypoxia, making tumor cells refractive to radiation therapy and alters their metabolism, thereby supporting proliferation and survival. In this review, we outline the significant contribution of fibrosis to the pathogenesis of pancreatic cancer, with a focus on the cross talk between immune cells and pancreatic stellate cells that contribute to ECM deposition. We emphasize the cellular mechanisms by which neutrophils and macrophages, specifically, modulate the ECM in favor of PDAC-progression. Furthermore, we investigate how activated stellate cells and ECM influence immune cells and promote immunosuppression in PDAC. Finally, we summarize therapeutic strategies that target the stroma and hinder immune cell promotion of fibrogenesis, which have unfortunately led to mixed results. An enhanced understanding of the complex interactions between the pancreatic tumor ECM and immune cells may uncover novel treatment strategies that are desperately needed for this devastating disease.

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

Background

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

Methods

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

Results

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

Conclusion

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

Supplementary Information

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

Identification of potential diagnostic biomarkers in MMPs for pancreatic carcinoma

Pancreatic cancer (PC) is a malignant tumor which ranks fourth in cancer-related death. However, the specificity and sensitivity of traditional biomarkers such as carbohydrate antigen 19-9 no longer meet the clinical requirements.Tools as ONCOMINE and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the differential expression of matrix metalloproteinases (MMPs) in PC and adjacent tissues. For further analysis, we adopted database for annotation, visualization and integrated discovery (DAVID 6.8), transcriptional regulatory relationships unraveled by sentence-based text (TRRUST) and other tools. We also identified drugs targeted the selected MMPs.Eight MMPs (MMP1, MMP2, MMP7, MMP9, MMP11, MMP12, MMP14, and MMP28) were differentially expressed in PC and adjacent tissue. MMP1 (P = .0189), MMP7 (P = .000216), MMP11 (P = .0209), MMP14 (P = .00611) were correlated with the pathological stages of PC. Patients with higher expression of MMP1 (P = .0011), MMP2 (P = .011), MMP7 (P = .0081), MMP9 (P = .046), MMP11 (P = .0019), MMP12 (P = .0011), MMP14 (P = .0011), and MMP28 (P = 6.3e-06) showed poor prognosis. Ten transcription factors were associated with the up-regulation of selected MMPs. Marimastat (DB00786) was found to target selected MMPs.Our research revealed that selected MMPs played an important role in the early diagnosis and prognosis of PC.

Identification of Regeneration and Hub Genes and Pathways at Different Time Points after Spinal Cord Injury

Spinal cord injury (SCI) is a neurological injury that can cause neuronal loss around the lesion site and leads to locomotive and sensory deficits. However, the underlying molecular mechanisms remain unclear. This study aimed to verify differential gene time-course expression in SCI and provide new insights for gene-level studies. We downloaded two rat expression profiles (GSE464 and GSE45006) from the Gene Expression Omnibus database, including 1 day, 3 days, 7 days, and 14 days post-SCI, along with thoracic spinal cord data for analysis. At each time point, gene integration was performed using "batch normalization." The raw data were standardized, and differentially expressed genes at the different time points versus the control were analyzed by Gene Ontology enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes pathway analysis, and gene set enrichment analysis. A protein-protein interaction network was then built and visualized. In addition, ten hub genes were identified at each time point. Among them, Gnb5, Gng8, Agt, Gnai1, and Psap lack correlation studies in SCI and deserve further investigation. Finally, we screened and analyzed genes for tissue repair, reconstruction, and regeneration and found that Anxa1, Snap25, and Spp1 were closely related to repair and regeneration after SCI. In conclusion, hub genes, signaling pathways, and regeneration genes involved in secondary SCI were identified in our study. These results may be useful for understanding SCI-related biological processes and the development of targeted intervention strategies.

Role of tumor mutation burden-related signatures in the prognosis and immune microenvironment of pancreatic ductal adenocarcinoma

BACKGROUND

High tumor mutation burden (TMB) has gradually become a sensitive biomarker for predicting the response to immunotherapy in many cancers, including lung, bladder and head and neck cancers. However, whether high TMB predicts the response to immunotherapy and prognosis in pancreatic ductal adenocarcinoma (PDAC) remained obscure. Hence, it is significant to investigate the role of genes related to TMB (TRGs) in PDAC.

METHODS

The transcriptome and mutation data of PDAC was downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Five independent external datasets of PDAC were chosen to validate parts of our results. qRT-PCR and immunohistochemical staining were also performed to promote the reliability of this study.

RESULTS

The median overall survival (OS) was significantly increased in TMB_low group compared with the counterpart with higher TMB score after tumor purity adjusted (P = 0.03). 718 differentially expressed TRGs were identified and functionally enriched in some oncogenic pathways. 67 TRGs were associated with OS in PDAC. A prognostic model for the OS was constructed and showed a high predictive accuracy (AUC = 0.849). We also found TMB score was associated with multiple immune components and signatures in tumor microenvironment. In addition, we identified a PDAC subgroup featured with TMB^lowMicrosatellite instability^high (MSI^high) was associated with prolonged OS and a key molecule, ANKRD55, potentially mediating the survival benefits.

CONCLUSION

This study analyzed the biological function, prognosis value, implications for mutation landscape and potential influence on immune microenvironment of TRGs in PDAC, which contributed to get aware of the role of TMB in PDAC. Future studies are expected to investigate how these TRGs regulate the initiation, development or repression of PDAC.

Aberrantly DNA Methylated-Differentially Expressed Genes in Pancreatic Cancer Through an Integrated Bioinformatics Approach

Pancreatic cancer remains one of the chief contributors to cancer related deaths on a global scale, with its diagnosis often associated with poor prognosis and high mortality. Accumulating literature continues to highlight the role of aberrant DNA methylation in relation to pancreatic cancer progression. Integrated bioinformatics approaches in the characterization of methylated-differentially expressed genes (MeDEGs) in pancreatic cancer were employed to enhance our understanding of the potential underlying molecular mechanisms of this cancer. We initially identified differentially expressed genes (DEGs) between 178 pancreatic cancer samples and 4 normal samples and differentially methylated genes (DMGs) based on 185 pancreatic cancer samples as well as 10 normal samples by analyzing RNA sequencing data in the TCGA database. Eventually, 31 MeDEGs including 5 hypomethylated/upregulated genes and 26 hypermethylated/downregulated genes were identified. Univariate Cox model and Kaplan–Meier method revealed that, among 31 MeDEGs, 5 hypermethylated/downregulated genes (ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42) were correlated with poor survival of patients with pancreatic cancer. KEGG pathway enrichment analysis by GSEA 3.0 and the protein–protein interaction (PPI) network revealed that these 5 MeDEGs were enriched in numerous cancer-related pathways in addition to interacting with each other, highlighting a significant role in the development of pancreatic cancer. Taken together, the key findings of the current study demonstrate that ZNF804A, ZFP82, TRIM58, SOX17, and C12orf42 are hypermethylated/downregulated genes in pancreatic cancer and may be associated, through their modulation of specific pathways, with unfavorable pancreatic cancer prognosis.

TSPAN1, TMPRSS4, SDR16C5, and CTSE as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation

Pancreatic cancer is a lethal malignancy with a poor prognosis. This study aims to identify pancreatic cancer-related genes and develop a robust diagnostic model to detect this disease. Weighted gene co-expression network analysis (WGCNA) was used to determine potential hub genes for pancreatic cancer. Their mRNA and protein expression levels were validated through reverse transcription PCR (RT-PCR) and immunohistochemical (IHC). Diagnostic models were developed by eight machine learning algorithms and ten-fold cross-validation. Four hub genes (TSPAN1, TMPRSS4, SDR16C5, and CTSE) were identified based on bioinformatics. RT-PCR showed that the four hub genes were expressed at medium to high levels, IHC revealed that their protein expression levels were higher in pancreatic cancer tissues. For the panel of these four genes, eight models performed with 0.87-0.92 area under the curve value (AUC), 0.91-0.94 sensitivity, and 0.84-0.86 specificity in the validation cohort. In the external validation set, these models also showed good performance (0.86-0.98 AUC, 0.84-1.00 sensitivity, and 0.86-1.00 specificity). In conclusion, this study has identified four hub genes that might be closely related to pancreatic cancer: TSPAN1, TMPRSS4, SDR16C5, and CTSE. Four-gene panels might provide a theoretical basis for the diagnosis of pancreatic cancer.

TMPRSS4 Promotes Cell Proliferation and Inhibits Apoptosis in Pancreatic Ductal Adenocarcinoma by Activating ERK1/2 Signaling Pathway

Transmembrane protease serine 4 (TMPRSS4) is upregulated in various kinds of human cancers, including pancreatic cancer. However, its biological function in pancreatic ductal adenocarcinoma (PDAC) remains unclear. In the current study, real-time qPCR, immunohistochemical staining, Western blotting, and database (Cancer Genome Atlas and Gene Expression) analysis revealed remarkable overexpression of TMPRSS4 in PDAC tissue as compared to non-tumor tissue. The TMPRSS4 overexpression was associated with poor prognosis of PDAC patients. Moreover, multivariate analysis revealed that TMPRSS4 serves as an independent risk factor in PDAC. We performed gain-and loss-of-function analysis and found that TMPRSS4 promotes cellular proliferation and inhibits apoptosis of PDAC cells both in vitro and in vivo. Furthermore, we showed that TMPRSS4 might promote cell proliferation and inhibit apoptosis through activating ERK1/2 signaling pathway in pancreatic cancer cells. These findings were validated by using ERK1/2 phosphorylation inhibitor SCH772984 both in vitro and in vivo. Taken together, this study suggests that TMPRSS4 is a proto-oncogene, which promotes initiation and progression of PDAC by controlling cell proliferation and apoptosis. Our findings indicate that TMPRSS4 could be a promising prognostic biomarker and a therapeutic target for the treatment of pancreatic cancer.

A 14-gene gemcitabine resistance gene signature is significantly associated with the prognosis of pancreatic cancer patients

To identify a gemcitabine resistance-associated gene signature for risk stratification and prognosis prediction in pancreatic cancer. Pearson correlation analysis was performed with gemcitabine half maximal inhibitory concentration (IC50) data of 17 primary pancreatic cancer lines from Genomics of Drug Sensitivity in Cancer (GDSC) and the transcriptomic data from GDSC and Broad Institute Cancer Cell Line Encyclopedia, followed by risk stratification, expression evaluation, overall survival (OS) prediction, clinical data validation and nomogram establishment. Our biomarker discovery effort identified a 14-gene signature, most of which featured differential expression. The 14-gene signature was associated with poor OS in E-MTAB-6134 (HR 2.37; 95% CI 1.75–3.2; p < 0.0001), pancreatic cancer-Canada (PACA-CA) (HR 1.76; 95% CI 1.31–2.37; p = 0.00015), and 4 other independent validation cohorts: pancreatic cancer-Australia (PACA-AU) (HR 1.9; 95% CI 1.38–2.61; p < 0.0001), The Cancer Genome Atlas (TCGA) (HR 1.73; 95% CI 1.11–2.69; p = 0.014), GSE85916 (HR 1.97; 95% CI 1.14–3.42; p = 0.014) and GSE62452 (HR 1.82; 95% CI 1.02–3.24; p = 0.039). Multivariate analysis revealed that the 14-gene risk score was an independent pancreatic cancer outcome predictor in E-MTAB-6134 (p < 0.001) and TCGA (p = 0.006). A nomogram including the 14-gene was established for eventual clinical translation. We identified a novel gemcitabine resistance gene signature for risk stratification and robust categorization of pancreatic cancer patients with poor prognosis.

The Glycolytic Pathway as a Target for Novel Onco-Immunology Therapies in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDA) is one of the most lethal forms of human cancer, characterized by unrestrained progression, invasiveness and treatment resistance. To date, there are limited curative options, with surgical resection as the only effective strategy, hence the urgent need to discover novel therapies. A platform of onco-immunology targets is represented by molecules that play a role in the reprogrammed cellular metabolism as one hallmark of cancer. Due to the hypoxic tumor microenvironment (TME), PDA cells display an altered glucose metabolism-resulting in its increased uptake-and a higher glycolytic rate, which leads to lactate accumulation and them acting as fuel for cancer cells. The consequent acidification of the TME results in immunosuppression, which impairs the antitumor immunity. This review analyzes the genetic background and the emerging glycolytic enzymes that are involved in tumor progression, development and metastasis, and how this represents feasible therapeutic targets to counteract PDA. In particular, as the overexpressed or mutated glycolytic enzymes stimulate both humoral and cellular immune responses, we will discuss their possible exploitation as immunological targets in anti-PDA therapeutic strategies.

SLC6A14 and SLC38A5 Drive the Glutaminolysis and Serine-Glycine-One-Carbon Pathways in Cancer

The glutaminolysis and serine–glycine–one-carbon pathways represent metabolic reactions that are reprogramed and upregulated in cancer; these pathways are involved in supporting the growth and proliferation of cancer cells. Glutaminolysis participates in the production of lactate, an oncometabolite, and also in anabolic reactions leading to the synthesis of fatty acids and cholesterol. The serine–glycine–one-carbon pathway is involved in the synthesis of purines and pyrimidines and the control of the epigenetic signature (DNA methylation, histone methylation) in cancer cells. Methionine is obligatory for most of the methyl-transfer reactions in the form of S-adenosylmethionine; here, too, the serine–glycine–one-carbon pathway is necessary for the resynthesis of methionine following the methyl-transfer reaction. Glutamine, serine, glycine, and methionine are obligatory to fuel these metabolic pathways. The first three amino acids can be synthesized endogenously to some extent, but the need for these amino acids in cancer cells is so high that they also have to be acquired from extracellular sources. Methionine is an essential amino acid, thus making it necessary for cancer cells to acquire this amino acid solely from the extracellular milieu. Cancer cells upregulate specific amino acid transporters to meet this increased demand for these four amino acids. SLC6A14 and SLC38A5 are the two transporters that are upregulated in a variety of cancers to mediate the influx of glutamine, serine, glycine, and methionine into cancer cells. SLC6A14 is a Na⁺/Cl⁻ -coupled transporter for multiple amino acids, including these four amino acids. In contrast, SLC38A5 is a Na⁺-coupled transporter with rather restricted specificity towards glutamine, serine, glycine, and methionine. Both transporters exhibit unique functional features that are ideal for the rapid proliferation of cancer cells. As such, these two amino acid transporters play a critical role in promoting the survival and growth of cancer cells and hence represent novel, hitherto largely unexplored, targets for cancer therapy.

Role of Sciellin in gallbladder cancer proliferation and formation of neutrophil extracellular traps

Apart from primary tumor development and metastasis, cancer-associated thrombosis is the second cause of cancer death in solid tumor malignancy. However, the mechanistic insight into the development of gallbladder cancer (GBC) and cancer-associated thrombosis remains unclear. This study aimed to investigate the mechanistic role of Sciellin (SCEL) in GBC cell proliferation and the development of venous thromboembolism. The expression level of SCEL was determined by immunohistochemical staining. Roles of SCEL in gallbladder cancer cell were determined by molecular and cell biology methods. SCEL was markedly upregulated in GBC and associated with advanced TNM stages and a poor prognosis. Furthermore, SCEL interacted with EGFR and stabilized EGFR expression that activates downstream PI3K and Akt pathway, leading to cell proliferation. In addition, SCEL induces tumor cell IL-8 production that stimulates the formation of neutrophil extracellular traps (NETs), accelerating thromboembolism. In xenografts, SCEL-expressing GBCs developed larger tumors and thrombosis compared with control cells. The present results indicate that SCEL promotes GBC cell proliferation and induces NET-associated thrombosis, thus serving as a potential therapeutic target.

Development and clinical validation of a novel 9-gene prognostic model based on multi-omics in pancreatic adenocarcinoma

The prognoses of patients with pancreatic adenocarcinoma (PAAD) remain poor due to the lack of biomarkers for early diagnosis and effective prognosis prediction. RNA sequencing, single nucleotide polymorphism, and copy number variation data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes. GISTIC 2.0 was used to identify significantly amplified or deleted genes, and Mutsig 2.0 was used to analyze the mutation data. The Lasso method was used to construct a risk prediction model. The Rms package was used to evaluate the overall predictive performance of the signature. Finally, Western blot and polymerase chain reaction were performed to evaluate gene expression. A total of 54 candidate genes were obtained after integrating the genomic mutated genes and prognosis-related genes. The Lasso method was used to ascertain 9 characteristic genes, including UNC13B, TSPYL4, MICAL1, KLHDC7B, KLHL32, AIM1, ARHGAP18, DCBLD1, and CACNA2D4. The 9-gene signature model was able to help stratify samples at risk in the training and external validation cohorts. In addition, the overall predictive performance of our model was found to be superior to that of other models. KLHDC7B, AIM1, DCBLD1, TSPYL4, and MICAL1 were significantly highly expressed in tumor tissues compared to normal tissues. ARHGAP18 and CACNA2D4 had no difference in expression between tumor and normal tissues. UNC13B and KLHL32 expression in the normal group was higher than in the tumor group. The 9-gene signature constructed in this study can be used as a novel prognostic marker to predict the survival of patients with pancreatic adenocarcinoma.

Identification of novel prognostic biomarkers in renal cell carcinoma

Objective: To identify novel prognostic biomarkers in renal cell carcinoma (RCC).

Results: 12 coding genes and one miRNA were finally identified as prognostic biomarkers. All of them were related to a poor prognosis. Lower expression levels of the coding genes were observed in higher clinical stages. Prognostic signatures including 7 biomarkers were identified. Patients in the high-risk group had worse survival than those in the low-risk group. The areas under the curves in different years indicated that it was a valuable signature in prognosis. It was found that elevated WDR72 inhibited the survival and invasion of 786-O and 769P cells in vitro.

Conclusions: Thirteen prognostic biomarkers of RCC were identified. Among them, 7 biomarkers comprised a signature to evaluate the RCC prognosis. WDR72 was a cancer suppressor and a potential therapeutic target in RCC.

Methods: Differentially expressed genes/miRNAs (DEGs/DEMs) and prognosis-related genes/miRNAs were acquired from public database. Prognostic biomarkers were identified by overlapping the significant DEGs/DEMs and prognosis-related genes/miRNAs. The associations between these biomarkers and the clinical stages were analyzed. All of these prognostic biomarkers were further investigated with multi-variable Cox regression. Finally, the inhibitory effect of WDR72 on the growth and invasion of RCC cells was studied.

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.

Amino Acid Transporter SLC6A14 (ATB0,+) - A Target in Combined Anti-cancer Therapy

Cancer cells are characterized by quick growth and proliferation, demanding constant supply of various nutrients. Several plasma membrane transporters delivering such compounds are upregulated in cancer. Solute carrier family 6 member 14 (SLC6A14), known as amino acid transporter B^0,+ (ATB^0,+) transports all amino acids with exception of the acidic ones: aspartate and glutamate. Its malfunctioning is correlated with several pathological states and it is upregulated in solid tumors. The high expression of SLC6A14 is prognostic and unfavorable in pancreatic cancer, while in breast cancer it is expressed in estrogen receptor positive cells. As many plasma membrane transporters it resides in endoplasmic reticulum (ER) membrane after translation before further trafficking through Golgi to the cell surface. Transporter exit from ER is strictly controlled. The proper folding of SLC6A14 was shown to be controlled from the cytoplasmic side by heat shock proteins, further exit from ER and formation of coatomer II (COPII) coated vesicles depends on specific interaction with COPII cargo-recognizing subunit SEC24C, phosphorylated by kinase AKT. Inhibition of heat shock proteins, known to be upregulated in cancer, directs SLC6A14 to degradation. Targeting proteins regulating SLC6A14 trafficking is proposed as an additional pharmacological treatment of cancer.

Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools to quantify cancer stemness in clinical tumors, and perhaps lead considerable benefits that could predict tumor prognosis, identify new stemness-related targets and targeted therapies, or improve targeted therapy sensitivity. The five genes identified in this study are expected to be the targets of GBM stem cell therapy.

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

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

Knockdown EIF3C Suppresses Cell Proliferation and Increases Apoptosis in Pancreatic Cancer Cell

Increasing evidence shows that eukaryotic initiation factor subunit (EIF3C) plays a crucial role in development of tumors. However, the underlying roles of EIF3Cin the development of pancreatic cancer (PC) remain unknown. In this study, we examined the expression of EIF3C in PC tissues, their adjacent normal tissues and 3 cell lines (SW1990, PANC-1 and AsPC-1). Moreover, the EIF3C-shRNA lentivirus was constructed to suppress EIF3C expression. Following this, the cell colony formation assay was employed to evaluate proliferation ability of PC cells. Meanwhile, the cell cycle and apoptotic assays were also performed by flow cytometry. We found that level of EIF3C in PC tissues was significantly increased compared with that in adjacent normal tissues. Furthermore, the knockdown of EIF3C can significantly reduce cell proliferation, block cell cycle in G2/M and induce apoptosis in both SW1990 and PANC-1 cells. Our findings suggest that EIF3C plays a crucial role in the progression of PC and may be a potential target in the treatment of PC.

Identification and validation of a ten-gene set variation score as a diagnostic and prognostic stratification tool in hepatocellular carcinoma

We aimed to identify a hepatocellular carcinoma (HCC)-specific gene set during progression. Using the HCC data set from The Cancer Genome Atlas, we found that 10 genes were gradually upregulated with the progression of HCC and associated with survival, classified as HCC-unfavorable genes; 29 genes were gradually downregulated and associated with survival, classified as HCC-favorable genes. Gene set variation analysis (GSVA) was used to score individual samples against the two gene sets. Receiver operating characteristic (ROC) curve analysis showed that both the HCC-unfavorable GSVA score and HCC-favorable GSVA score were reliable biomarkers for diagnosing HCC. Moreover, tROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated that the HCC-unfavorable GSVA score was an independent prognostic biomarker. The results were validated in an external independent data set. Our results support a ten-gene set variation score as a diagnostic and predictive strategy tool in HCC.

Integrated Bioinformatic Analysis Identifies Networks and Promising Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma

Chronic infection with hepatitis B virus (HBV) has long been recognized as a dominant hazard factor for hepatocellular carcinoma (HCC) and accounts for at least half of HCC instances globally. However, the underlying molecular mechanism of HBV-linked HCC has not been completely elucidated. Here, three microarray datasets, totally containing 170 tumoral samples and 181 adjacent normal tissues from the liver of patients suffering from HBV-related HCC assembled from the Gene Expression Omnibus (GEO) database, were subjected to integrated analysis of differentially expressed genes (DEGs). Subsequently, the analysis of function and pathway enrichment as well as the protein-protein interaction network (PPI) was performed. The ten hub genes screened out from the PPI network were further subjected to expression profile and survival analysis. Overall, 329 DEGs (67 upregulated and 262 downregulated) were identified. Ten DEGs with the highest degree of connectivity included cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), PDZ-binding kinase (PBK), abnormal spindle microtubule assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F (CENPF). Kaplan-Meier analysis unveiled that overexpression levels of KIF2C and TPX2 were relevant to both the poor overall survival and relapse-free survival. In summary, the hub genes validated in the present study may provide promising targets for the diagnosis, prognosis, and therapy of HBV-associated HCC. Additionally, our work uncovers various crucial biological components (e.g., extracellular exosome) and signaling pathways that participate in the progression of HCC induced by HBV, serving comprehensive knowledge of the mechanisms regarding HBV-related HCC.

Novel Potential Biomarkers Associated With Epithelial to Mesenchymal Transition and Bladder Cancer Prognosis Identified by Integrated Bioinformatic Analysis

Bladder cancer (BC) is one of the most common malignancies in terms of incidence and recurrence worldwide. The aim of this study was to identify novel prognostic biomarkers related to BC progression utilizing weighted gene co-expression network analysis (WGCNA) and further bioinformatic analysis. First, we constructed a co-expression network by using WGCNA among 274 TCGA-BLCA patients and preliminarily screened out four genes (CORO1C, TMPRSS4, PIK3C2B, and ZNF692) associated with advanced clinical traits. In support, GSE19915 and specimens from 124 patients were used to validate the genes selected by WGCNA; then, CORO1C and TMPRSS4 were confirmed as hub genes with strong prognostic values in BC. Moreover, the result of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) indicated that CORO1C and TMPRSS4 might be involved in the process of epithelial to mesenchymal transition (EMT) reversely. In addition, high expression of CORO1C was found to be significantly correlated with tumor-infiltrating neutrophils (TINs), a negative regulatory component that facilitates tumor distant progression and induces poor clinical outcome. In conclusion, our study first identified CORO1C and TMPRSS4 as vital regulators in the process of tumor progression through influencing EMT and could be developed to effective prognostic and therapeutic targets in future BC treatment.

Co-expression based cancer staging and application

A novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample. The R source code used in this study is available at GitHub (https://github.com/yxchspring/CECS).

Establishment and Characterisation by Expression Microarray of Patient-Derived Xenograft Panel of Human Pancreatic Adenocarcinoma Patients

Pancreatic cancer remains among the most lethal cancers worldwide, with poor early detection rates and poor survival rates. Patient-derived xenograft (PDX) models have increasingly been used in preclinical and clinical research of solid cancers to fulfil unmet need. Fresh tumour samples from human pancreatic adenocarcinoma patients were implanted in severe combined immunodeficiency (SCID) mice. Samples from 78% of treatment-naïve pancreatic ductal adenocarcinoma patients grew as PDX tumours and were confirmed by histopathology. Frozen samples from F1 PDX tumours could be later successfully passaged in SCID mice to F2 PDX tumours. The human origin of the PDX was confirmed using human-specific antibodies; however, the stromal component was replaced by murine cells. Cell lines were successfully developed from three PDX tumours. RNA was extracted from eight PDX tumours and where possible, corresponding primary tumour (T) and adjacent normal tissues (N). mRNA profiles of tumour vs. F1 PDX and normal vs. tumour were compared by Affymetrix microarray analysis. Differential gene expression showed over 5000 genes changed across the N vs. T and T vs. PDX samples. Gene ontology analysis of a subset of genes demonstrated genes upregulated in normal vs. tumour vs. PDX were linked with cell cycle, cycles cell process and mitotic cell cycle. Amongst the mRNA candidates elevated in the PDX and tumour vs. normal were SERPINB5, FERMT1, AGR2, SLC6A14 and TOP2A. These genes have been associated with growth, proliferation, invasion and metastasis in pancreatic cancer previously. Cumulatively, this demonstrates the applicability of PDX models and transcriptomic array to identify genes associated with growth and proliferation of pancreatic cancer.

TMPRSS4: A Novel Tumor Prognostic Indicator for the Stratification of Stage IA Tumors and a Liquid Biopsy Biomarker for NSCLC Patients

Relapse rates in surgically resected non-small-cell lung cancer (NSCLC) patients are between 30% and 45% within five years of diagnosis, which shows the clinical need to identify those patients at high risk of recurrence. The eighth TNM staging system recently refined the classification of NSCLC patients and their associated prognosis, but molecular biomarkers could improve the heterogeneous outcomes found within each stage. Here, using two independent cohorts (MDA and CIMA-CUN) and the eighth TNM classification, we show that TMPRSS4 protein expression is an independent prognostic factor in NSCLC, particularly for patients at stage I: relapse-free survival (RFS) HR, 2.42 (95% CI, 1.47-3.99), p < 0.001; overall survival (OS) HR, 1.99 (95% CI, 1.25-3.16), p = 0.004). In stage IA, high levels of this protein remained associated with worse prognosis (p = 0.002 for RFS and p = 0.001 for OS). As TMPRSS4 expression is epigenetically regulated, methylation status could be used in circulating tumor DNA from liquid biopsies to monitor patients. We developed a digital droplet PCR (ddPCR) method to quantify absolute copy numbers of methylated and unmethylated CpGs within the TMPRSS4 and SHOX2 (as control) promoters in plasma and bronchoalveolar lavage (BAL) samples. In case-control studies, we demonstrated that TMPRSS4 hypomethylation can be used as a diagnostic tool in early stages, with an AUROC of 0.72 (p = 0.008; 91% specificity and 52% sensitivity) for BAL and 0.73 (p = 0.015; 65% specificity and 90% sensitivity) for plasma, in early stages. In conclusion, TMPRSS4 protein expression can be used to stratify patients at high risk of relapse/death in very early stages NSCLC patients. Moreover, analysis of TMPRSS4 methylation status by ddPCR in blood and BAL is feasible and could serve as a non-invasive biomarker to monitor surgically resected patients.

HnRNPR-CCNB1/CENPF axis contributes to gastric cancer proliferation and metastasis

Gastric cancer (GC) is a common disease globally with high mortality rate. It is therefore necessary to develop novel therapies targeting specific events in the pathogenesis of GC. Some hnRNP family members are involved in multiple cancer biological behaviors. However, the potential function and mechanism of hnRNPR, a new molecule of hnRNP family in GC remains unknown. We found that the expression of hnRNPR was significantly overexpressed in multiple cancers compared to the normal tissues. Functionally, hnRNPR promoted cancer cell proliferation, migration, and invasion. Knockdown of hnRNPR in two type mice models, with two types of tumors models decreased the tumor aggressiveness and metastasis. Mechanistically, hnRNPR targeted oncogenic pathways by stabilizing the expression of CCNB1 and CENPF mRNA level. Knockdown of CCNB1 and CENPF abolished the hnRNPR-induced cell growth and invasion, respectively. Furthermore, the protein level of hnRNPR in the tumor was positively correlated with the expression of CCNB1 and CENPF in clinical samples. Together, these results indicate that overexpression of hnRNPR promoted the aggressiveness of GC by increasing the mRNA expression of CCNB1 and CENPF. HnRNPR-CCNB1/CENPF axis may be a potential therapeutic target for GC treatment.

Identification of potential diagnostic and prognostic biomarkers for prostate cancer

Prostate cancer (PCa) is one of the most common malignant tumors worldwide. The aim of the present study was to determine potential diagnostic and prognostic biomarkers for PCa. The GSE103512 dataset was downloaded, and the differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) analyses of DEGs were performed. The result of GO analysis suggested that the DEGs were mostly enriched in ‘carboxylic acid catabolic process’, ‘cell apoptosis’, ‘cell proliferation’ and ‘cell migration’. KEGG analysis results indicated that the DEGs were mostly concentrated in ‘metabolic pathways’, ‘ECM-receptor interaction’, the ‘PI3K-Akt pathway’ and ‘focal adhesion’. The PPI analysis results showed that Golgi membrane protein 1 (GOLM1), melanoma inhibitory activity member 3 (MIA3), ATP citrate lyase (ACLY) and G protein subunit β2 (GNB2) were the key genes in PCa, and the Module analysis revealed that they were associated with ‘ECM-receptor interaction’, ‘focal adhesion’, the ‘PI3K-Akt pathway’ and the ‘metabolic pathway’. Subsequently, the gene expression was confirmed using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. The results demonstrated that GOLM1 and ACLY expression was significantly upregulated (P<0.05) in PCa compared with that in normal tissues. Receiver operating characteristic and survival analyses were performed. The results showed that area under the curve values of these genes all exceeded 0.85, and high expression of these genes was associated with poor survival in patients with PCa. In conclusion, this study identified GOLM1 and ACLY in PCa, which may be potential diagnostic and prognostic biomarker of PCa.