High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer

Enroth, Stefan; Berggrund, Malin; Lycke, Maria; Broberg, John; Lundberg, Martin; Assarsson, Erika; Olovsson, Matts; Stålberg, Karin; Sundfeldt, Karin; Gyllensten, Ulf

doi:10.1038/s42003-019-0464-9

Cited by 81 publications

(109 citation statements)

References 33 publications

(44 reference statements)

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“…The protein models demonstrated vital role for predicting prognosis, which including bladder cancer, esophageal squamous cell carcinoma and pancreatic ductal adenocarcinoma [25][26][27]. Abnormal expression of various protein biomarkers in cancer cells or inflammatory cells demonstrated prediction potential in cancers [21,28,29]. And a prediction model would demonstrate much better specificity and sensitivity than one biomarker.…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Fang¹,

Liu²,

Weng³

et al. 2020

Int. J. Med. Sci.

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Lung squamous cell carcinoma (LUSCC), as the major type of lung cancer, has high morbidity and mortality rates. The prognostic markers for LUSCC are much fewer than lung adenocarcinoma. Besides, protein biomarkers have advantages of economy, accuracy and stability. The aim of this study was to construct a protein prognostic model for LUSCC. The protein expression data of LUSCC were downloaded from The Cancer Protein Atlas (TCPA) database. Clinical data of LUSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 237 proteins were identified from 325 cases of LUSCC patients based on the TCPA and TCGA database. According to Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, a prognostic prediction model was established which was consisted of 6 proteins (CHK1_pS345, CHK2, IRS1, PAXILLIN, BRCA2 and BRAF_pS445). After calculating the risk values of each patient according to the coefficient of each protein in the risk model, the LUSCC patients were divided into high risk group and low risk group. The survival analysis demonstrated that there was significant difference between these two groups (p= 4.877e−05). The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was 0.699, which suggesting that the prognostic risk model could effectively predict the survival of LUSCC patients. Univariate and multivariate analysis indicated that this prognostic model could be used as independent prognosis factors for LUSCC patients. Proteins co-expression analysis showed that there were 21 proteins co-expressed with the proteins in the risk model. In conclusion, our study constructed a protein prognostic model, which could effectively predict the prognosis of LUSCC patients.

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Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Fang¹,

Liu²,

Weng³

et al. 2020

Int. J. Med. Sci.

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“…The three samples sets used were from the U-CAN collection [ 36 ] at Uppsala Biobank, Sweden, and from Sahlgrenska Gyncancer Biobank, Göteborg, Sweden and have been described earlier [ 18 , 37 ]. The EDTA plasma samples from the U-CAN biobank were from patients diagnosed with ovarian cancer stages I-IV and collected at time of admission to hospital from awake patients.…”

Section: Methodsmentioning

confidence: 99%

Preoperative Fasting and General Anaesthesia Alter the Plasma Proteome

Gyllensten

Enroth

Stålberg

et al. 2020

Cancers

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Background: Blood plasma collected at time of surgery is an excellent source of patient material for investigations into disease aetiology and for the discovery of novel biomarkers. Previous studies on limited sets of proteins and patients have indicated that pre-operative fasting and anaesthesia can affect protein levels, but this has not been investigated on a larger scale. These effects could produce erroneous results in case-control studies if samples are not carefully matched. Methods: The proximity extension assay (PEA) was used to characterize 983 unique proteins in a total of 327 patients diagnosed with ovarian cancer and 50 age-matched healthy women. The samples were collected either at time of initial diagnosis or before surgery under general anaesthesia. Results: 421 of the investigated proteins (42.8%) showed statistically significant differences in plasma abundance levels comparing samples collected at time of diagnosis or just before surgery under anaesthesia. Conclusions: The abundance levels of the plasma proteome in samples collected before incision, i.e., after short-time fasting and under general anaesthesia differs greatly from levels in samples from awake patients. This emphasizes the need for careful matching of the pre-analytical conditions of samples collected from controls to cases at time of surgery in the discovery as well as clinical use of protein biomarkers.

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“…In recent years, proximity extension assay (PEA) technology has been employed in the identification of novel protein biomarkers and biomarker combinations for early detection of ovarian cancer, with promising results [34,[48][49][50]. The addition of inflammatory and immunological biomarkers to CA125 and HE4 holds potential to increase sensitivity and specificity compared to the established algorithms.…”

Section: Biomarkers For Ovarian Cancer Detectionmentioning

confidence: 99%

A multiplex biomarker assay improves the diagnostic performance of HE4 and CA125 in ovarian tumor patients

et al. 2020

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High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer

Cited by 81 publications

References 33 publications

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

Preoperative Fasting and General Anaesthesia Alter the Plasma Proteome

A multiplex biomarker assay improves the diagnostic performance of HE4 and CA125 in ovarian tumor patients

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