Proteomics provides individualized options of precision medicine for patients with gastric cancer

Huang, Wenwen; Zhan, Dongdong; Li, Yazhuo; Zheng, Nairen; Wei, Xin; Bai, Bin; Zhang, Kecheng; Liu, Mingwei; Zhao, Xuefei; Ni, Xiaohua; Xia, Xia; Shi, Jinwen; Zhang, Cheng; Lü, Zhihao; Ji, Jiafu; Wang, Juan; Wang, Shiqi; Ji, Gang; Li, Jipeng; Nie, Yongzhan; Liang, Wenquan; Wu, Xiaosong; Cui, Jianxin; Meng, Yongsheng; Cao, Feng; Shi, Tieliu; Zhu, Weimin; Wang, Yi; Chen, Lin; Zhao, Qingchuan; Wang, Hongwei; Shen, Lin; Qin, Jun

doi:10.1007/s11427-021-1966-4

Cited by 11 publications

(7 citation statements)

References 46 publications

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“…In this study, we used FFPE surgical resection samples from 833 GC patients with TNM stage II/III from previous work ( 31 ). These samples were collected between 2004 and 2016 and came from five hospitals, namely, the Peking University Cancer Hospital & Institute (PKUCH, N = 387), the Fourth Medical Center of PLA General Hospital/304 Hospital (304H, N = 210), the Xijing Hospital of Digestive Diseases (XJH, N = 112), the Medical School of Chinese PLA/301 Hospital (301H, N = 71), and the Shanxi Cancer Hospital (SXCH, N = 53).…”

Section: Methodsmentioning

confidence: 99%

“…However, due to the limited amount of data, this result needs to be further verified. In a subsequent proteomic subtyping of GC, a workflow based on non-negative matrix factorization (NMF) consensus clustering was applied on 1,020 formalin-fixed, paraffin-embedded (FFPE) GC samples ( 31 ). While this workflow could identify chemotherapy benefit for patients, there was no significant difference in prognosis between the two molecular subtypes.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer

et al. 2022

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Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate worldwide and lacks effective methods for prognosis prediction. Postoperative adjuvant chemotherapy is the first-line treatment for advanced gastric cancer, but only a subgroup of patients benefits from it. Here, we used 833 formalin-fixed, paraffin-embedded resected tumor samples from patients with TNM stage II/III GC and established a proteomic subtyping workflow using 100 deep-learned features. Two proteomic subtypes (S-I and S-II) with overall survival differences were identified. S-I has a better survival rate and is sensitive to chemotherapy. Patients in the S-I who received adjuvant chemotherapy had a significant improvement in the 5-year overall survival rate compared with patients who received surgery alone (65.3% vs 52.6%; log-rank P = 0.014), but no improvement was observed in the S-II (54% vs 51%; log-rank P = 0.96). These results were verified in an independent validation set. Furthermore, we also evaluated the superiority and scalability of the deep learning-based workflow in cancer molecular subtyping, exhibiting its great utility and potential in prognosis prediction and therapeutic decision-making.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer

et al. 2022

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“…With the advents of bioinformatics and modern multi-analytes “omics” technologies (Supplementary Fig. 1 ), proteomics holds a great promise for uncovering the molecular mechanisms that underlies diseases towards the discovery of novel biomarkers 8 and can be used as specific diagnostic assays, prognostic predictors, and therapeutic targets to enhance personalized medicine further 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

High-throughput proteomics: a methodological mini-review

Cui

Cheng

Zhang

2022

Laboratory Investigation

148

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Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other ‘omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery.

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“…Liver biopsy is an invasive test to assess liver cell enzyme activity. Gene sequencing and genetic analysis, a technology that can provide precise diagnosis and procreation guidance and are more acceptable to patients, is increasingly used in the diagnosis of genetic diseases, including GSD ( Shen and Shi, 2019 ; Fang et al, 2021 ; Huang et al, 2021 ; Li et al, 2021 ; Szymanska et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Genotypic and phenotypic characteristics of 12 chinese children with glycogen storage diseases

et al. 2022

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Background: Glycogen storage diseases (GSDs) are known as a group of disorders characterized by genetic errors leading to accumulation of glycogen in various tissues. Since different types of GSD can sometimes be clinically indistinguishable, next generation sequencing is becoming a powerful tool for clinical diagnosis.Methods: 12 patients with suspected GSDs and their parents were enrolled in this study. The clinical and laboratory data of the patients were reviewed. Causative gene variants were identified in the patients using whole exome sequencing (WES) and verified by Sanger sequencing.Results: Genetic testing and analysis showed that 7 patients were diagnosed with GSD II (Pompe disease), 2 patients with GSD III, 1 patient with GSD VI, and 2 patients with GSD IXα. A total number of 18 variants were identified in 12 patients including 11 variants in GAA gene, 3 variants in AGL gene, 2 variants in PYGL gene and 2 variants in PHKA2 gene, of which 9 variants were reported and 9 variants were novel. SIFT, Polyphen-2, Mutation Taster, and REVEL predicted the novel variants (except GAA c.1052_1075 + 47del) to be disease-causing. The 3D structures of wild/mutant type GAA protein were predicted indicating that variants p. Trp621Gly, p. Pro541Leu, p. Ser800Ile and p. Gly293Trp might affect the proteins function via destroying hydrogen bonds or conformational constraints. Neither liver size nor laboratory findings allow for a differentiation among GSD III, GSD VI and GSD IXα.Conclusion: Our study expanded the variation spectrum of genes associated with GSDs. WES, in combination with clinical, biochemical, and pathological hallmarks, could provide accurate results for diagnosing and sub-typing GSD and related diseases in clinical setting.

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Proteomics provides individualized options of precision medicine for patients with gastric cancer

Cited by 11 publications

References 46 publications

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer

Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer

High-throughput proteomics: a methodological mini-review

Genotypic and phenotypic characteristics of 12 chinese children with glycogen storage diseases

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