Prognostic Significance of Preoperative and Postoperative Complement C3 Depletion in Gastric Cancer: A Three-Year Survival Investigation

Plasma-based proteomic profiling identifies the distinct regulation of proteins in hyperplasia and endometrial cancer

BACKGROUND

Among gynaecological malignancies, endometrial cancer (EC) is the most prevalent type of uterine cancer affecting women. This study explored the proteomic profiles of plasma samples obtained from EC patients, those with hyperplasia (Hy), and a control group (CO). A combination of techniques, such as 2D-DIGE, mass spectrometry, and bioinformatics, including pathway analysis, was used to identify proteins with modified expression levels, biomarkers and their associated metabolic pathways in these groups.

METHODS

Thirty-four patients, categorized into three groups-10 with EC, 12 with Hy, and 12 CO-between the ages of 46 and 75 years old were included in the study. Untargeted proteomic analysis was carried out using two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).

RESULTS

In all three groups, 114 proteins that were significantly (p ≤ 0.05 and fold change ≥ 1.5) altered were successfully identified using peptide mass fingerprints (PMFs). Compared with those in the control group (CO), the EC samples had 85 differentially expressed proteins (39 upregulated and 46 downregulated), and in the Hy group, 81 proteins were dysregulated (40 upregulated and 41 downregulated) compared to those in the CO group, while 33 proteins exhibited differential regulation (12 upregulated and 21 downregulated) in the EC plasma samples compared to those in the Hy group. Vitamin D binding protein and complement C3 distinguished Hy and EC from CO with the greatest changes in expression. Among the differentially expressed proteins identified, enzymes with catalytic activity represented the largest group (42.9%). In terms of biological processes, most of the proteins were involved in cellular processes (28.8%), followed by metabolic processes (16.7%). STRING analysis for protein interactions revealed that the significantly differentially abundant proteins in the three groups are involved in three main biological processes: signalling of complement and coagulation cascades, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), and plasma lipoprotein assembly, remodelling, and clearance.

CONCLUSION

The identified plasma protein markers have the potential to serve as biomarkers for differentiating between EC and Hy, as well as for early diagnosis and monitoring of cancer progression.

Excavation of diagnostic biomarkers and construction of prognostic model for clear cell renal cell carcinoma based on urine proteomics

Purpose

Clear cell renal cell carcinoma (ccRCC) is the most common pathology type in kidney cancer. However, the prognosis of advanced ccRCC is unsatisfactory. Thus, early diagnosis becomes one of the most important research priorities of ccRCC. However, currently available studies about ccRCC lack urine-related further studies. In this study, we applied proteomics to search urinary biomarkers to assist early diagnosis of ccRCC. In addition, we constructed a prognostic model to assist judge patients' prognosis.

Materials and methods

Urine which was used to perform 4D label-free quantitative proteomics was collected from 12 ccRCC patients and 11 non-tumor patients with no urinary system diseases. The urine of 12 patients with ccRCC confirmed by pathological examination after surgery was collected before operatoin. Bioinformatics analysis was used to describe the urinary proteomics landscape of these patients with ccRCC. The top ten proteins with the highest expression content were selected as the basis for subsequent validation. Urine from 46 ccRCC patients and 45 control patients were collected to use for verification by enzyme linked immunosorbent assay (ELISA). In order to assess the prognostic value of urine proteomics, a prognostic model was constructed by COX regression analysis on the intersection of RNA-sequencing data in The Cancer Genome Atlas (TCGA) database and our urine proteomic data.

Results

133 proteins differentially expressed in the urinary samples were found and 85 proteins (Fold Change, FC>1.5) were identified up-regulated while 48 down-regulated (FC<0.5). Top 10 proteins including S100A14, PKHD1L1, FABP4, ITIH2, C3, C8G, C2, ATF6, ANGPTL6, F13B were performed ELISA to verify. The results showed that PKHD1L1, ANGPTL6, FABP4 and C3 were statistically significant (P<0.05). We performed multivariate logistic regression analysis and plotted a nomogram. Receiver operating characteristic (ROC) curve indicted that the diagnostic efficiency of combined indicators is satisfactory (Aare under curve, AUC=0.835). Furthermore, the prognostic value of the urine proteomics was explored through the intersection between urine proteomics and TCGA RNA-seq data. Thus, COX regression analysis showed that VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 were statistically significant (P<0.05).

Conclusion

Our study indicated that the application of urine proteomics to explore diagnostic biomarkers and to construct prognostic models of renal clear cell carcinoma is of certain clinical value. PKHD1L1, ANGPTL6, FABP4 and C3 can assist to diagnose ccRCC. The prognostic model constituted of VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 can significantly predict the prognosis of ccRCC patients, but this still needs more clinical trials to verify.

Integrated analysis of Helicobacter pylori-related prognostic gene modification patterns in the tumour microenvironment of gastric cancer

Background

Helicobacter pylori (HP) infection is one of the leading causes of gastric cancer (GC). However, the interaction between HP and the TME, and its carcinogenic mechanism remains unknown.

Methods

The HP-related prognostic genes were identified based on HP infection-related gene markers and HP infection sample datasets by risk method and NMF algorithm. Principal component analysis (PCA) algorithm was used to constructed the HPscore system. The “limma” R package was employed to determine differentially expressed genes. In addition, the R packages, such as “xCell” and “GSVA”, was used to analyze the relationship between the HPscore and tumor microenvironment. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to verify the expression levels of 28 HP-related prognostic genes in tissues.

Results

We successfully identified 28 HP-related prognostic genes that accurately classified the GC population. There are significant differences in survival between different subgroups (high-, low-risk and cluster_1,2). Thereafter, the HPscore system was constructed to evaluate the signatures of the 28 HP-related prognostic genes. The overall survival rate in the high-HPscore group was poor and immunological surveillance was reduced, whereas the low-HPscore group had a survival advantage and was related to the inflammatory response. HPscore was also strongly correlated with the tumour stage, TME cell infiltration and stemness. The qRT-PCR results showed that DOCK4 expression level of 28 HP-related prognostic genes was higher in gastric cancer tissues than in adjacent tissues.

Conclusions

HP signatures play a crucial role in the TME and tumourigenesis. HPscore evaluation of a single tumour sample can help identify the TME characteristics and the carcinogenic mechanism of GC patients infected with HP, based on which personalized treatment can be administered.

In Silico Identification of Hub Genes as Observing Biomarkers for Gastric Cancer Metastasis

Perception of hub genes engaged in metastatic gastric cancer (mGC) promotes novel ways to diagnose and treat the illness. The goal of this investigation is to recognize the hub genes and reveal its molecular mechanism. In order to explore the potential facts for gastric cancer, the expression profiles of two different datasets were used (GSE161533 and GSE54129). The genes were confirmed to be part of the PPI network for gastric cancer pathogenesis and prognosis. In Cytoscape, the CytoHubba module was used to discover the hub genes. Responsible hub genes were identified. Data from Kaplan-Meier plotter confirmed the predictive value of these distinct genes in various stages of gastric malignancy. Upregulated and downregulated genes were identified to utilize for further analysis. Positive regulation by a host of viral process, positive regulation of granulocyte differentiation, negative regulation of histone H3-K9 methylation were found in DEGs analysis. In addition, five KEGG pathways were identified as an essential enhancer that include nucleotide excision repair; base excision repair; DNA replication; homologous recombination; and complement and coagulation cascades. POLE, BUB1B, POLD4, C3, BLM, CCT7, PRPF31, APEX1, PSMA7, and CDC45 were chosen as hub genes after combining the PPI results. Our study recommends that BUB1B, CCT7, APEX1, PSMA7, and CDC45 might be potential biomarkers for gastric cancer. These biomarkers are upregulated genes. Therefore, suppression of these genes will increase the survival rate in gastric cancer patients.

Comprehensive molecular characterization and identification of prognostic signature in stomach adenocarcinoma on the basis of energy-metabolism-related genes

BACKGROUND

Stomach adenocarcinoma (STAD) is a leading cause of cancer deaths, but its molecular and prognostic characteristics has never been fully illustrated.

AIM

To describe a molecular evaluation of primary STAD and develop new therapies and identify promising prognostic signatures.

METHODS

We describe a comprehensive molecular evaluation of primary STAD based on comprehensive analysis of energy-metabolism-related gene (EMRG) expression profiles.

RESULTS

On the basis of 86 EMRGs that were significantly associated to patients’ progression-free survival (PFS), we propose a molecular classification dividing gastric cancer into two subtypes: Cluster 1, most of which are young patients and display more immune and stromal cell components in tumor microenvironment and lower tumor priority; and Cluster 2, which show early stages and better PFS. Moreover, we construct a 6-gene signature that can classify the prognostic risk of patients after a three-phase training test and validation process. Compared with patients with low-risk score, patients with high-risk score had shorter overall survival. Furthermore, calibration and DCA analysis plots indicate the excellent predictive performance of the 6-gene signature, and which present higher robustness and clinical usability compared with three previous reported prognostic gene signatures. According to gene set enrichment analysis, gene sets related to the high-risk group were participated in the ECM receptor interaction and hedgehog signaling pathway.

CONCLUSION

Identification of the EMRG-based molecular subtypes and prognostic gene model provides a roadmap for patient stratification and trials of targeted therapies.

Development and Validation of Hub Genes for Adrenal Aldosterone-Producing Adenoma by Integrated Bioinformatics Analysis

OBJECTIVE

To develop and validate hub genes involving in the development and progression of primary aldosteronism (PA) and adrenal aldosterone-producing adenoma (APA).

MATERIALS AND METHODS

A total of four datasets of gene expression profiles related to APA were downloaded from GEO datasets. GSE60042 and GSE8514 were used to identify DEGs. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network module analysis were conducted. GO and KEGG enrichment analysis was performed. GSE10927 and GSE33371 were used for further external validation.

RESULTS

We identified a total of 892 DEGs from GSE60042 and 1167 DEGs from GSE8514. WGCNA analysis demonstrated that the blue module (255 genes) and turquoise module (303 genes) were significantly correlated with APA. PPI networks were then constructed. GO term enrichment analysis suggested that cellular divalent inorganic cation homeostasis, calcium ion homeostasis, collagen-containing extracellular matrix, transport vesicle and metal ion transmembrane transporter activity were the vital annotations. KEGG pathway analysis found that these genes were significantly enriched in neuroactive ligand-receptor interaction, calcium signaling pathway. Finally, we identified a total of 11 candidate genes involving in the development and progression of APA and PA. Besides, two independent datasets (GSE10927 and GSE33371) were used for external validation, and there were seven hub genes successfully verified, including C3, GRM3, AVPR1A, WFS1, PTGFR, NTSR2, and JUN.

CONCLUSION

These newly identified genes could contribute to the understanding of potential mechanism in APA and PA and might be promising targets for the treatment of APA and PA.

MALDI-TOF-MS Analysis in the Discovery and Identification of the Serum Peptide Pattern of Pancreatic Ductal Adenocarcinoma

OBJECTIVE

To explore the application of serum peptidomics in the early diagnosis of pancreatic ductal adenocarcinoma (PDAC).

METHODS

The serum specimens from 176 patients with PDAC and 158 healthy control patients were subjected to matrix-assisted laser desorption ionization time-of-flight mass spectrometry to obtain serum peptide profiles. Next, a classification model by differentiated peptides was established and verified to distinguish the 2 groups. Finally, the peptides were identified by tandem mass spectrometry.

RESULTS

A classification model was established by 13 peptides. For patients with PDAC in the early stage, the sensitivity and specificity of the model reached 100% and 96.7%, respectively. The amino acid sequences of the 13 peptides were then determined and the types of proteins were identified, including platelet basic protein, fibrinogen alpha, complement C3, and secreted frizzled-related protein 4. Some of the 13 peptides could be potential PDAC biomarkers.

CONCLUSION

Serum peptidomics may have potential application in the early diagnosis of PDAC.

Label-free peptide quantification coupled with in silico mapping of proteases for identification of potential serum biomarkers in gastric adenocarcinoma patients

OBJECTIVES

We aimed to identify serum level variations in protein-derived peptides between patients diagnosed with gastric adenocarcinoma (GAC) and non-cancer persons (control) to detect the activity changes of proteases and explore the auxiliary diagnostic value in the context of GAC physiopathology.

METHODS

The label-free quantitative peptidome approach was applied to identify variants in serum levels of peptides that can differentiate GAC patients from the control group. Peptide sequences were submitted against Proteasix tool predicting proteases potentially involved in their generation. The activity change of proteases was subsequently estimated based on the peptides with significantly altered relative abundance. In turn, activity change prediction of proteases was correlated with relevant protease expression data from the literature.

RESULTS

A total of 191 peptide sequences generated by the cleavage of 36 precursor proteins were identified. Using the label-free quantification approach, 33 peptides were differentially quantified (adjusted fold change ≥ 1.5 and p-value < 0.05) in which 19 were up-regulated and 14 were down-regulated in GAC samples. Of these peptides, fibrinopeptide A was significantly decreased and its phosphorylated form ADpSGEGDFLAEGGGVR was upregulated in GAC samples. Activity change prediction yielded 10 proteases including 6 Matrix Metalloproteinases (MMPs), Thrombin, Plasmin, and kallikreins 4 and 14. Among predicted proteases in our analysis, MMP-7 was presented as a more promising biomarker associated with useful assays of clinical practice for GAC diagnosis.

CONCLUSION

Our experimental results demonstrate that the serum levels of peptides were significantly differentiated in GAC physiopathology. The hypotheses built on protease regulation could be used for further investigations to measure proteases and their activity levels that have been poorly studied for GAC diagnosis.

Prognostic Significance of Serum Complement Component 3 in Chronic Rhinosinusitis with Nasal Polyps

OBJECTIVE

The aim of this study was to assess the relationship between serum complement component 3 (C3) levels and disease recurrences in patients with chronic rhinosinusitis with nasal polyps (NPs).

METHODS

Ninety-seven patients with NPs and 30 controls were recruited. Clinical features were collected. Serum concentrations of C3 and C4 were measured before and after endoscopic sinus surgery.

RESULTS

Compared to the controls, increased C3 levels were found in patients with NPs. Patients with polyp recurrences had higher pre- and postoperative serum C3 levels than patients without polyp recurrences. Serum C3 levels dropped after surgery. After polyp regrowth, the mean C3 level in the recurrent group elevated again to the degree similar to that before surgery. When patients were stratified by tissue eosinophilia, no significant difference was seen in pre-/postoperative, absolute change after surgery, and post-recurrent C3 levels between patients without and with eosinophilic NPs in the group with disease recurrences.

CONCLUSION

Serum C3 may be involved in the pathogenesis of NPs. Higher serum C3 levels may pinpoint patients at high risk of recurrence as an independent factor. Furthermore, the change in C3 levels after surgery may have the potential to serve as a predictor for polyp progression. Adding serum C3 measurement to the routine walk-up in the clinical management of NPs is worth further investigation and may help physicians make a more rational diagnostic and/or therapeutic decision regarding this disease.

Complement C3 overexpression activates JAK2/STAT3 pathway and correlates with gastric cancer progression

Background

Localized C3 deposition is a well-known factor of inflammation. However, its role in oncoprogression of gastric cancer (GC) remains obscured. This study aims to explore the prognostic value of C3 deposition and to elucidate the mechanism of C3-related oncoprogression for GC.

Methods

From August to December 2013, 106 GC patients were prospectively included. The regional expression of C3 and other effectors in gastric tissues were detected by WB, IHC, qRT-PCR and other tests. The correlation of localized C3 deposition and oncologic outcomes was determined by 5-year survival significance. Human GC and normal epithelial cell lines were employed to detect a relationship between C3 and STAT3 signaling pathway in vitro experiments.

Results

C3 and C3a expression were markedly enhanced in GC tissues at both mRNA and protein levels compared with those in paired nontumorous tissues. According to IHC C3 score, 65 (61.3%) and 41 (38.7%) patients had high and low C3 deposition, respectively. C3 deposition was negatively correlated with plasma levels of C3 and C3a (both P < 0.001) and positively correlated with pathological T and TNM stages (both P < 0.001). High C3 deposition was identified as an independent prognostic factor of poor 5-year overall survival (P = 0.045). In vitro C3 administration remarkably enhanced p-JAK2/p-STAT3 expression in GC cell lines but caused a reduction of such activation when pre-incubated with a C3 blocker. Importantly, C3 failed to activate such signaling in cells pre-treated with a JAK2 inhibitor.

Conclusions

Localized C3 deposition in the tumor microenvironment is a relevant immune signature for predicting prognosis of GC. It may aberrantly activate JAK2/STAT3 pathway allowing oncoprogression.

Trial registration

ClinicalTrials.gov, NCT02425930, Registered 1st August 2013.