SEPROGADIC – serum protein-based gastric cancer prediction model for prognosis and selection of proper adjuvant therapy

Ahn, Hee-Sung; Sohn, Tae Sung; Kim, Mi Jeong; Cho, Byoung-Kyu; Kim, Su Mi; Kim, Seung Tae; Yi, Eugene C.; Lee, Cheolju

doi:10.1038/s41598-018-34858-x

Cited by 9 publications

(8 citation statements)

References 46 publications

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“…A recently published study suggested that the immunoScore signature could predict response to adjuvant chemotherapy in stage II and III GC patients in subgroup analysis, but the result was not validated [ 28 ]. Another two studies reported that serum biomarkers based model in GC patients from single-center could classify the patients who are suitable for chemoradiation or chemotherapy [ 29 , 30 ]. However, these models were conducted in a small sample size and were not independently validated.…”

Section: Discussionmentioning

confidence: 99%

A predictive signature for oxaliplatin and 5-fluorouracil based chemotherapy in locally advanced gastric cancer

Wang

Liu

Chen

et al. 2021

Translational Oncology

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Adjuvant chemotherapy(AC) plays a substantial role in the treatment of locally advanced gastric cancer (LAGC), but the response remains poor. We aims to improve its efficacy in LAGC. Therefore, we identified the expression of eight genes closely associated with platinum and fluorouracil metabolism ( RRM1 , RRM2 , RRM2B , POLH , DUT , TYMS , TYMP , MKI67 ) in the discovery cohort (N=291). And we further validated the findings in TCGA (N=279) and GEO. Overall survival (OS) was used as an endpoint. Univariate and multivariate Cox models were applied. A multivariate Cox regression model was simulated to predict the OS. In the discovery cohort, the univariate Cox model indicated that AC was beneficial to high-RRM1, high-DUT, low-RRM2, low-RRM2B, low-POLH, low-KI67, low-TYMS or low-TYMP patients, the results were validated in the TCGA cohort. The multivariate Cox model showed consistent results. Cumulative analysis indicated that patients with low C-Score respond poorly to the AC, whereas the high and medium C-Score patients significantly benefit from AC. A risk model based on the above variables successfully predicted the OS in both cohorts (AUC=0.75 and 0.67, respectively). Further validation in a panel of gastric cancer cell (GC) lines (N=37) indicated that C-Score is significantly associated with IC50 value to fluorouracil. Mutation profiling showed that C-Score was associated with the number and types of mutations. In conclusion, we successfully simulated a predictive signature for the efficacy of AC in LAGC patients and further explored the potential mechanisms. Our findings could promote precision medicine and improve the prognosis of LAGC patients.

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Section: Discussionmentioning

confidence: 99%

A predictive signature for oxaliplatin and 5-fluorouracil based chemotherapy in locally advanced gastric cancer

Wang

Liu

Chen

et al. 2021

Translational Oncology

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“…Except for the six normalization proteins in a sample, the normalized LFQ value of each protein was calculated by dividing its corrected LFQ value by NSF.

where

is the normalized LFQ of urinary protein j in sample s and

is the corrected LFQ of the corresponding protein [ 61 ].…”

Section: Methodsmentioning

confidence: 99%

Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction

Ahn

Kim

Jeong

et al. 2020

IJMS

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Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alkylated, digested, and analyzed qualitatively and quantitatively with a nano LC-MS platform. Each protein was identified, and its concentration normalized to that of creatinine. A prognostic model of DKD was formulated based on the adjusted quantities of each protein in the two groups. Of 1296 proteins identified in the 54 urine samples, 66 were differentially abundant in the two groups (area under the curve (AUC): p-value < 0.05), but none showed significantly better performance than albumin. To improve the predictive power by multivariate analysis, five proteins (ACP2, CTSA, GM2A, MUC1, and SPARCL1) were selected as significant by an AUC-based random forest method. The application of two classifiers—support vector machine and random forest—showed that the multivariate model performed better than univariate analysis of mucin-1 (AUC: 0.935 vs. 0.791) and albumin (AUC: 1.0 vs. 0.722). The urinary proteome can reflect kidney function directly and can predict the prognosis of patients with chronic kidney dysfunction. Classification based on five urinary proteins may better predict the prognosis of DKD patients than urinary albumin concentration or eGFR.

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“…The geometric mean of the six ratios of the sample was then used as the normalization scaling factor (NSF) for that sample. The details of this method are also provided in previous studies [ 83 , 84 ].…”

Section: Methodsmentioning

confidence: 99%

Plasma Protein Biomarkers Associated with Higher Ovarian Cancer Risk in BRCA1/2 Carriers

Ahn

et al. 2021

Cancers

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Ovarian cancer (OC) is the most lethal gynecologic malignancy and in-time diagnosis is limited because of the absence of effective biomarkers. Germline BRCA1/2 genetic alterations are risk factors for hereditary OC; risk-reducing salpingo-oophorectomy (RRSO) is pursued for disease prevention. However, not all healthy carriers develop the disease. Therefore, identifying predictive markers in the BRCA1/2 carrier population could help improve the identification of candidates for preventive RRSO. In this study, plasma samples from 20 OC patients (10 patients with BRCA1/2 wild type (wt) and 10 with the BRCA1/2 variant (var)) and 20 normal subjects (10 subjects with BRCA1/2wt and 10 with BRCA1/2var) were analyzed for potential biomarkers of hereditary OC. We applied a bottom-up proteomics approach, using nano-flow LC-MS to analyze depleted plasma proteome quantitatively, and potential plasma protein markers specific to the BRCA1/2 variant were identified from a comparative statistical analysis of the four groups. We obtained 1505 protein candidates from the 40 subjects, and SPARC and THBS1 were verified by enzyme-linked immunosorbent assay. Plasma SPARC and THBS1 concentrations in healthy BRCA1/2 carriers were found to be lower than in OC patients with BRCA1/2var. If plasma SPARC concentrations increase over 337.35 ng/ml or plasma THBS1 concentrations increase over 65.28 mg/ml in a healthy BRCA1/2 carrier, oophorectomy may be suggested.

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SEPROGADIC – serum protein-based gastric cancer prediction model for prognosis and selection of proper adjuvant therapy

Cited by 9 publications

References 46 publications

A predictive signature for oxaliplatin and 5-fluorouracil based chemotherapy in locally advanced gastric cancer

A predictive signature for oxaliplatin and 5-fluorouracil based chemotherapy in locally advanced gastric cancer

Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction

Plasma Protein Biomarkers Associated with Higher Ovarian Cancer Risk in BRCA1/2 Carriers

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