Identification of serum biomarkers to predict pemetrexed/platinum chemotherapy efficacy for advanced lung adenocarcinoma patients by data-independent acquisition (DIA) mass spectrometry analysis with parallel reaction monitoring (PRM) verification

Jia, Bo; Zhao, Xinghui; Wu, Di; Dong, Zhi; Chi, Yujia; Zhao, Jun; Wu, Meina; An, Tongtong; Wang, Yuyan; Zhuo, Minglei; Li, Jianjie; Chen, Xiaoling; Tian, Guangming; Long, Jieran; Yang, Xue; Chen, Hanxiao; Wang, Jingjing; Zhai, Xiaoyu; Sheng, Li; Li, Junfeng; Ma, Menglei; He, Yuan; Kong, Ling-Dong; Brčić, Luka; Fang, Jian; Wang, Ziping

doi:10.21037/tlcr-21-153

Cited by 19 publications

(10 citation statements)

References 28 publications

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“…While, MS-based screening of serum protein biomarkers has unique challenges due to the interference of high abundance proteins [37] . In this study, we replaced traditional DDA-MS with DIA-MS, which eliminated the need for cumbersome and expensive abundance protein reseparation and fraction in individual serum samples [38,39] . Although the depth of our serum proteome could be improved, we detected hundreds of proteins that are not available in the human plasma proteome database, e.g.…”

Section: Discussionmentioning

confidence: 99%

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Xing

Cai

Ouyang

et al. 2023

Preprint

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Early diagnosis of hepatocellular carcinoma (HCC) lacks highly sensitive and specific protein biomarkers. Proteomics-driven discovery of tumor biomarkers is an important direction for omics study. Here, we described a staged mass spectrometry (MS)-based discovery-verification-validation proteomics workflow to explore serum proteomic biomarkers for HCC early diagnosis in 662 individuals (373 HCC patients and 289 non-HCC patients). Our workflow reproducibly quantified 451serum proteins using a data independent acquisition mass spectrometry (DIA-MS) strategy from discovery cohort, and proteins with significantly altered abundance in HCC were validated as candidates in an independent validation cohort using targeted proteomics based on parallel reaction monitoring (PRM). Machine learning models determined as P4 serum protein-panels (two serum proteomics biomarkers: HABP2, CD163 and two clinical used serum biomarkers: AFP, PIVKA-II) could clearly distinguish HCC patients from LC patients in an independent validation cohort (AUC 0.979, sensitivity 0.925, specificity 0.915), outperforming existing clinical prediction strategies (p < 0.05). Moreover, the P4 panels showed high sensitivity in AFP negative (0.857) HCC patients and PIVKA-II negative HCC patients (0.813). Most importantly, the P4 panels were validated to be perfectly accurate in predicting the conversion of LC to HCC (accuracy: 100.0%) with predicting HCC at a median of 12.6 months prior to imaging in a prospective external validation cohort, which was superior to existing clinical prediction strategies. These results suggested that proteomics-driven serum biomarker discovery provided a valuable reference for the liquid biopsy, and had great potential to improve early diagnosis of HCC.

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Section: Discussionmentioning

confidence: 99%

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Xing

Cai

Ouyang

et al. 2023

Preprint

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“…The parameters were set as previously described. 52 Protein identification for each sample was performed using Proteome Discoverer (version 2.2, Thermo Fisher) and Spectronaut software (version 14.0, Biognosys). 53,54 Peptide identification and quantitation were achieved by matching the ions and calculating the peak area.…”

Section: ■ Discussionmentioning

confidence: 99%

“…Fraction peptides (4 μg) labeled with 0.8 μL of iRT reagent (Biognosys, Schlieren, Switzerland) were analyzed using an Evosep One UHPLC system (Evosep, Odense, Denmark) coupled with a Q Exactive HF-X mass spectrometer (Thermo Fisher, MA). The parameters were set as previously described . Protein identification for each sample was performed using Proteome Discoverer (version 2.2, Thermo Fisher) and Spectronaut software (version 14.0, Biognosys). , Peptide identification and quantitation were achieved by matching the ions and calculating the peak area.…”

Section: Methodsmentioning

confidence: 99%

Proteomics-Guided Mining and Characterization of Epoxidase Involved in Camptothecin Biosynthesis from Camptotheca acuminata

Pu,

Wang,

Chen

et al. 2023

ACS Chem. Biol.

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The detailed metabolic map for camptothecin (CPT) biosynthesis in Camptotheca acuminata has been proposed according to our combined omics results. However, the CYP450-mediated epoxidation step in CPT biosynthesis remains unexplored. A proteomicsguided approach was used to identify and annotate the proteins enriched during the vigorous CPT metabolism period in mature C. acuminata and seedlings. Comparative analyses revealed that the CPT and flavonoid biosyntheses were vigorous in stems and all of the samples except the leaves, respectively. The CYP71BE genes were screened based on their enrichment patterns at the transcriptomic−proteomic level and biochemically characterized in Saccharomyces cerevisiae WAT11. Four CYP71BE proteins exhibited in vitro isoliquiritigenin epoxidase activity. Additionally, CYP71BE206 showed epoxidase activity toward strictosamide, the critical precursor for CPT biosynthesis, both in vitro and in Nicotiana benthamiana. In planta functional verification suggested that CYP71BE206 is involved in CPT biosynthesis. Their catalytic conditions were optimized, and the enzymatic parameters were determined. This study provides valuable insight into the CYP71BE-mediated epoxidation step for CPT biosynthesis and offers evidence to verify that the newly characterized epoxidase (CYP71BE206) is simultaneously responsible for the biosynthesis of CPT and the flavonoid in this plant. An evolution event probably happened on ancestral CYP71BE, resulting in the neofunctionalization of CYP71BE206.

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“…Therefore, identification of specific blood biomarkers remains an urgent need for increasing the diagnostic accuracy. In general, the prevailing diagnostic model after DIA is the verification of parallel reaction monitoring (PRM) [ 35 , 36 ]. However, PRM is expensive and it is not suitable for clinical testing.…”

Section: Discussionmentioning

confidence: 99%

The Plasma DIA-Based Quantitative Proteomics Reveals the Pathogenic Pathways and New Biomarkers in Cervical Cancer and High Grade Squamous Intraepithelial Lesion

Han

Zhang

Sun

et al. 2022

JCM

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Objective: The process of normal cervix changing into high grade squamous intraepithelial lesion (HSIL) and invasive cervical cancer is long and the mechanisms are still not completely clear. This study aimed to reveal the protein profiles related to HSIL and cervical cancer and find the diagnostic and prognostic molecular changes. Methods: Data-independent acquisition (DIA) analysis was performed to identify 20 healthy female volunteers, 20 HSIL and 20 cervical patients in a cohort to screen differentially expressed proteins (DEPs) for the HSIL and cervical cancer. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used for functional annotation of DEPs; the protein–protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) were performed for detection of key molecular modules and hub proteins. They were validated using the Enzyme-Linked Immunosorbent Assay (ELISA). Results: A total of 243 DEPs were identified in the study groups. GO and KEGG analysis showed that DEPs were mainly enriched in the complement and coagulation pathway, cholesterol metabolism pathway, the IL-17 signaling pathway as well as the viral protein interaction with cytokine and cytokine receptor pathway. Subsequently, the WGCNA analysis showed that the green module was highly correlated with the cervical cancer stage. Additionally, six interesting core DEPs were verified by ELISA, APOF and ORM1, showing nearly the same expression pattern with DIA. The area under the curve (AUC) of 0.978 was obtained by using ORM1 combined with APOF to predict CK and HSIL+CC, and in the diagnosis of HSIL and CC, the AUC can reach to 0.982. The high expression of ORM1 is related to lymph node metastasis and the clinical stage of cervical cancer patients as well as the poor prognosis. Conclusion: DIA-ELSIA combined analysis screened and validated two previously unexplored but potentially useful biomarkers for early diagnosis of HSIL and cervical cancer, as well as possible new pathogenic pathways and therapeutic targets.

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Identification of serum biomarkers to predict pemetrexed/platinum chemotherapy efficacy for advanced lung adenocarcinoma patients by data-independent acquisition (DIA) mass spectrometry analysis with parallel reaction monitoring (PRM) verification

Cited by 19 publications

References 28 publications

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Proteomics-driven noninvasive screening of circulating serum protein panels for the early diagnosis of hepatocellular carcinoma

Proteomics-Guided Mining and Characterization of Epoxidase Involved in Camptothecin Biosynthesis from Camptotheca acuminata

The Plasma DIA-Based Quantitative Proteomics Reveals the Pathogenic Pathways and New Biomarkers in Cervical Cancer and High Grade Squamous Intraepithelial Lesion

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