A Novel Six-Gene-Based Prognostic Model Predicts Survival and Clinical Risk Score for Gastric Cancer

Background: Autophagy plays a vital role in cancer initiation, malignant progression, and resistance to treatment. However, autophagy-related genes (ARGs) have rarely been analyzed in gastric cancer (GC). The purpose of this study was to analyze ARGs in GC using bioinformatic analysis and to identify new biomarkers for predicting the overall survival (OS) of patients with GC.

Methods: The gene expression profiles and clinical data of patients with GC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and ARGs were obtained from two other datasets (the Human Autophagy Database and Molecular Signatures Database). Lasso, univariate, and multivariate Cox regression analyses were performed to identify the OS-related ARGs. Finally, a six-ARG model was identified as a prognostic indicator using the risk-score model, and survival and prognostic performance were analyzed based on the Kaplan-Meier test and ROC curve. Estimate calculations were used to assess the immune status of this model, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed for investigating the functions and terms associated with the model-related genes in GC.

Results: The six ARGs, DYNLL1, PGK2, HPR, PLOD2, PHYHIP, and CXCR4, were identified using Lasso and Cox regression analyses. Survival analysis revealed that the OS of GC patients in the high-risk group was significantly lower than that of the low-risk group (p < 0.05). The ROC curves revealed that the risk score model exhibited better prognostic performance with respect to OS. Multivariate Cox regression analysis indicated that the model was an independent predictor of OS and was not affected by most of the clinical traits (p < 0.05). The model-related genes were associated with immune suppression and several biological process terms, such as extracellular structure organization and matrix organization. Moreover, the genes were associated with the P13K-Akt signaling pathway, focal adhesion, and MAPK signaling pathway.

Conclusions: This study presents potential prognostic biomarkers for GC patients that would aid in determining the best patient-specific course of treatment.

Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for gastric cancer

Background

Gastric cancer (GC) is a common malignant cancer in the worldwide, especially in China. Patients with GC have poor prognosis, which is mainly due to lack of early diagnosis. Up to now, there is no good biomarker to detect GC at early stage. Apolipoprotein C1 (APOC1), a component of both triglyceride-rich lipoproteins and high-density lipoproteins, is reported to be involved in numerous biological processes.

Methods

Expression of APOC1 mRNA was analyzed by in silicon assay. Concentration of APOC1 in serum was measured by ELISA assay. Expression of APOC1 protein in GC tissue array was checked by immunohistochemistry.

Results

It was firstly found that concentration of APOC1 in serum was significantly higher in GC than that in control. Expression of APOC1 protein was also higher in GC than that in adjacent issues of GC and normal tissues using tissues array by immunohistochemistry. In addition, the expression of APOC1 is significantly associated with clinical stage (P=0.011), tumor classification (P=0.010), as well as with the lymph node metastasis (P=0.048). Area under the curve (AUC) of receiver operating characteristic (ROC) curve of APOC1 was 0.803. Furthermore, elevated APOC1 expression in GC was found to be correlated with decreased overall survival (P=0.00214).

Conclusions

All these results suggested that APOC1 might be a potential serum biomarker to diagnose GC and a potential prognostic marker for GC.

Is co-expression of USP22 and HSP90 more effective in predicting prognosis of gastric cancer?

The ubiquitin-specific peptidase 22 (USP22) belongs to the largest subfamily of deubiquitylases and recent studies indicate that overexpression of USP22 may promote gastric cancer progression and predict prognosis. But little is known about the interaction network of USP22 in gastric cancer. In this study, we applied bioinformatics methods and found that USP22 was correlated with the heat shock protein 90 (HSP90) which is now considered to be a biomarker to predict the prognosis of gastric cancer. Then the siRNA transfection and western blotting were used to testify the correlation of USP22 and HSP90 in gastric cancer cells. The immunohistochemistry staining of the microarrays was applied to confirm the correlation of USP22 and HSP90 expression in gastric cancer tissue and further analysis showed that co-expression of USP22 and HSP90 was related to lymph node metastasis and more effective in predicting the prognosis of gastric cancer. In summary, our data demonstrate that correlation exists between USP22 and HSP90 expressions in gastric cancer and co-expression of USP22 and HSP90 may be more effective in predicting prognosis of gastric cancer.

Identification of serum proteins AHSG, FGA and APOA-I as diagnostic biomarkers for gastric cancer

Background

The development of clinically accessible biomarkers is critical for the early diagnosis of gastric cancer (GC) in patients. High-throughput proteomics techniques could not only effectively generate a serum peptide profile but also provide a new approach to identify potentially diagnostic and prognostic biomarkers for cancer patients.

Methods

In this study, we aim to identify potentially discriminating serum biomarkers for GC. In the discovery cohort, we screened potential biomarkers using magnetic-bead-based purification and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry in 64 samples from 32 GC patients that were taken both pre- and post-operatively and 30 healthy volunteers that served as controls. In the validation cohort, the expression patterns and diagnostic values of serum FGA, AHSG and APOA-I were further confirmed by ELISA in 42 paired GC patients (pre- and post-operative samples from 16 patients with pathologic stage I/II and 26 with stage III/IV), 30 colorectal cancer patients, 30 hepatocellular carcinoma patients, and 28 healthy volunteers.

Results

ClinProTools software was used and annotated 107 peptides, 12 of which were differentially expressed among three groups (P < 0.0001, fold > 1.5). These 12 peptide peaks were further identified as FGA, AHSG, APOA-I, HBB, TXNRD1, GSPT2 and CAKP5. ELISA data suggested that the serum levels of FGA, AHSG and APOA-I in GC patients were significantly different compared with healthy controls and had favorable diagnostic values for GC patients. Moreover, we found that the serum levels of these three proteins were associated with TNM stages and could reflect tumor burden.

Conclusion

Our findings suggested that FGA, AHSG and APOA-I might be potential serum biomarkers for GC diagnosis.

Proteomics for early detection of colorectal cancer: recent updates

INTRODUCTION

Colorectal cancer (CRC) is a common type of cancer with a relatively poor survival rate. The survival rate of patients could be improved if CRC is detected early. Biomarkers associated with early stages of tumor development might provide useful tools for the early diagnosis of colorectal cancer. Areas covered: Online searches using PubMed and Google Scholar were performed using keywords and with a focus on recent proteomic studies. The aim of this review is to highlight the need for biomarkers to improve the detection rate of early CRC and provide an overview of proteomic technologies used for biomarker discovery and validation. This review will also discuss recent proteomic studies which focus on identifying biomarkers associated with the early stages of CRC development. Expert commentary: A large number of CRC biomarkers are increasingly being identified by proteomics using diverse approaches. However, the clinical relevance and introduction of these markers into clinical practice cannot be determined without a robust validation process. The size of validation cohorts remains a major limitation in many biomarker studies.

PFK15, a Small Molecule Inhibitor of PFKFB3, Induces Cell Cycle Arrest, Apoptosis and Inhibits Invasion in Gastric Cancer

PFKFB3 (6-phosphofructo-2-kinase) synthesizes fructose 2,6-bisphosphate (F2,6P₂), which is an allosteric activator of 6-phosphofructo-1-kinase (PFK-1), the rate-limiting enzyme of glycolysis. Overexpression of the PFKFB3 enzyme leads to high glycolytic metabolism, which is required for cancer cells to survive in the harsh tumor microenvironment. The objective of this study was to investigate the antitumor activity of PFK15 (1-(4-pyridinyl)-3-(2-quinolinyl)-2-propen-1-one), a small molecule inhibitor of PFKFB3, against gastric cancer and to explore its potential mechanisms. The effects of PFK15 on proliferation, apoptosis and cell cycle progression in gastric cancer cells were evaluated by cytotoxicity and apoptosis assays, flow cytometry, and western blotting. In addition, the invasion inhibition effects of PFK15 were measured by transwell invasion assay and western blot analysis, and a xenograft tumor model was used to verify the therapeutic effect of PFK15 in vivo. Results showed that PFK15 inhibited the proliferation, caused cell cycle arrest in G0/G1 phase by blocking the Cyclin-CDKs/Rb/E2F signaling pathway, and induced apoptosis through mitochondria in gastric cancer cells. Tumor volume and weight were also significantly reduced upon intraperitoneal injection with PFK15 at 25 mg/kg. In addition, PFK15 inhibited the invasion of gastric cancer cells by downregulating focal adhesion kinase (FAK) expression and upregulating E-cadherin expression. Taken together, our findings indicate that PFK15 is a promising anticancer drug for treating gastric cancer.

Integrative analysis of the colorectal cancer proteome: potential clinical impact

INTRODUCTION

Colorectal cancer (CRC) is one of the common types of cancer that affects a significant proportion of the population and is a major contributor to cancer related mortality. The relatively poor survival rate of CRC could be improved through the identification of clinically useful biomarkers. Areas covered: This review highlights the need for biomarkers and discusses recent proteomics discoveries in the aspects of CRC clinical practice including diagnosis, prognosis, therapy, screening and molecular pathological epidemiology (MPE). Studies have been evaluated in relation to biomarker target, methodology, sample selection, limitations, and potential impact. Finally, the progress in proteomic approaches is briefly discussed and the main difficulties facing the translation of proteomics biomarkers into the clinical practice are highlighted. Expert commentary: The establishment of specific guidelines, best practice recommendations and the improvement in proteomic strategies will significantly improve the prospects for developing clinically useful biomarkers.

A simple biomarker scoring matrix for early gastric cancer detection

Gastric cancer (GC) is a major cause of death in many parts of the world. While 90% of early GC is curable by resection, only about 5% of patients diagnosed in the late stages survive beyond five years. This provides strong impetus to push for early GC detection through the use of non-invasive biomarkers, before metastatic complications arise. It is also of strong medical interest to identify patients of the diffuse subtype at the earliest time possible, since the disease variant progresses very rapidly and is associated with much higher mortality rate. In this study, we compared quantitatively the gastric fluid proteome of 70 GC patients to 17 individuals with benign gastritis in search of potential biomarkers that aid in GC diagnosis, prognosis and subtype stratification. We report that as much as half of the gastric fluid proteome is subject to regulation in diseased states, and propose a simple biomarker panel scoring matrix for early GC detection with diagnostic sensitivity of 95.7%. We also demonstrate as proof-of-concept that a digitised record generated with SWATH-MS based on 380 protein abundance signatures from the gastric fluid could segregate patients with diffuse-type GC.

The Discovery and Validation of Biomarkers for the Diagnosis of Esophageal Squamous Dysplasia and Squamous Cell Carcinoma

The 5-year survival rate of esophageal cancer is less than 10% in developing countries, where more than 90% of these cancers are esophageal squamous cell carcinomas (ESCC). Endoscopic screening is undertaken in high incidence areas. Biomarker analysis could reduce the subjectivity associated with histologic assessment of dysplasia and thus improve diagnostic accuracy. The aims of this study were therefore to identify biomarkers for esophageal squamous dysplasia and carcinoma. A publicly available dataset was used to identify genes with differential expression in ESCC compared with normal esophagus. Each gene was ranked by a support vector machine separation score. Expression profiles were examined, before validation by qPCR and IHC. We found that 800 genes were overexpressed in ESCC compared with normal esophagus (P < 10(-5)). Of the top 50 genes, 33 were expressed in ESCC epithelium and not in normal esophagus epithelium or stroma using the Protein Atlas website. These were taken to qPCR validation, and 20 genes were significantly overexpressed in ESCC compared with normal esophagus (P < 0.05). TNFAIP3 and CHN1 showed differential expression with IHC. TNFAIP3 expression increased gradually through normal esophagus, mild, moderate and severe dysplasia, and SCC (P < 0.0001). CHN1 staining was rarely present in the top third of normal esophagus epithelium and extended progressively towards the surface in mild, moderate, and severe dysplasia, and SCC (P < 0.0001). Two novel promising biomarkers for ESCC were identified, TNFAIP3 and CHN1. CHN1 and TNFAIP3 may improve diagnostic accuracy of screening methods for ESCC. Cancer Prev Res; 9(7); 558-66. ©2016 AACR.