A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer

Shen, Qiujin; Połom, Karol; Williams, Coralie; Oliveira, Felipe Marques Souza de; Guergova-Kuras, Mariana; Lisacek, Frédérique; Karlsson, Niclas G.; Roviello, Franco; Kamali‐Moghaddam, Masood

doi:10.1016/j.ebiom.2019.05.044

Cited by 53 publications

(31 citation statements)

References 59 publications

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“…The small normal sample size may lead to some bias in the results. Other omics fields, such as genome, transcriptome, proteome, and metabolome, have shown respective advantages in GC diagnostic and prognostic models ( Li et al, 2010 ; Chan et al, 2016 ; Deng et al, 2018 ; Zhang et al, 2018 ; Shen et al, 2019 ); therefore, it is too early to assert that our model is optimal. The models should be validated in a real-world cohort.…”

Section: Discussionmentioning

confidence: 99%

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Liu

Wang

et al. 2021

Front. Genet.

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Background: The management of gastric cancer (GC) still lacks tumor markers with high specificity and sensitivity. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms.Methods: In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis. We constructed a diagnostic model of GC by logistic analysis and conducted verification and clinical correlation analysis. We screened credible prognostic DMSs through univariate Cox and LASSO analyses and verified a prognostic model of GC by multivariate Cox analysis. Independent prognostic and biological function analyses were performed for the prognostic risk score. We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes.Results: The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. The prediction accuracy of the eleven-DMS prognostic model was verified in both the training and validation datasets, indicating its certain potential for GC survival prediction. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The prognostic risk score was an independent risk factor for the prognosis of GC, which was significantly correlated with N stage and tumor location, positively correlated with the VIM gene, and negatively correlated with the CDH1 gene. The expression of CHRNB2 decreased significantly in the TP53 mutation group of gastric cancer patients, and there were significant differences in CCDC69, RASSF2, CHRNB2, ARMC9, and RPN1 between the TP53 mutation group and the TP53 non-mutation group of gastric cancer patients. In addition, CEP290, UBXN8, KDM4A, RPN1 had high frequency mutations and the function of eleven-DMG mutation related genes in GC patients is widely enriched in multiple pathways.Conclusion: Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment. The study provides direction for exploring potential markers of GC.

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Section: Discussionmentioning

confidence: 99%

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Liu

Wang

et al. 2021

Front. Genet.

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“…APBB1IP integrates signaling events that are critical for integrin-mediated immune function control and cancer progression (Patsoukis et al, 2019 ). Some studies have shown that APBB1IP is a new biological marker associated with gastric cancer and head and neck squamous cell carcinoma (Sanati et al, 2018 ; Shen et al, 2019 ). The Niemann–Pick type C2 (NPC2) protein is involved in the regulation of intracellular cholesterol homeostasis via direct binding with free cholesterol (Kamata et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis

Cao

Zhang

Lin

et al. 2020

Front. Mol. Biosci.

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Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, and FKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that high FKBP11 expression was correlated with high risk (a risk factor), and that high GJA5, APBB1IP, or NPC2 expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified.

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“…The rPSL SWATH allowed the identification of well-documented and clinically significant (supported by the literature) plasma proteins. Numerous studies have proposed CEA as a late-stage CRC biomarker (35), as well as for lung (36) and gastric cancers (37). Plasma IL6, CXCL8 and ILB1 have been also proposed as diagnostic markers in CRC, brain, breast, oral and prostate cancers (45–47, 55–57).…”

Section: Discussionmentioning

confidence: 99%

A Recombinant Protein Biomarker DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Ahn

Kamath

Mohamedali

et al. 2020

Preprint

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Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). Here, we employed a mixture of recombinant proteins in DDA libraries to subsequently detect cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 human recombinant proteins that had been previously implicated as possible cancer biomarkers in both our own and other studies. The rPSL was then used to identify proteins from non-depleted colorectal cancer (CRC) plasmas by SWATH-MS. Most (32/36) of the proteins in the rPSL were reliably identified in plasma samples, including 8 proteins (BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency MS in human plasmas according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and post-digestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 were identified using DDA-MS. The 20 additional proteins exclusively identified by using the rPSL approach with SWATH were mostly lower abundance (i.e., <10ng/ml) plasma proteins. To mitigate FDR concerns, and replicating a more typical approach, the DDA rPSL was also merged into a human plasma DDA library. When SWATH identification was repeated using this merged library, the majority (33/36) of low abundance plasma proteins from the rPSL could still be identified using high-stringency HPP Guidelines v3.0 protein inference criteria.

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A targeted proteomics approach reveals a serum protein signature as diagnostic biomarker for resectable gastric cancer

Cited by 53 publications

References 59 publications

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis

A Recombinant Protein Biomarker DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

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