Urine proteomic signatures predicting the progression from premalignancy to malignant gastric cancer

Hua, Fan; Li, Xue; Li, Zhongwu; Zheng, Nairen; Cao, Lihua; Liu, Zongchao; Liu, Mingwei; Li, Kai; Wu, Wenhui; Li, Zhexuan; Zhou, Tong; Zhang, Yang; Liu, Weidong; Zhang, Lan-Fu; You, Wei‐Cheng; Wang, Yi; Pan, Kai-Feng; Qin, Jun; Li, Wen-Qing

doi:10.1016/j.ebiom.2022.104340

Cited by 14 publications

(9 citation statements)

References 48 publications

(54 reference statements)

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“…For example, the higher mRNA and protein level of type I collagen, measured by polymerase chain reaction and immunohistochemistry respectively, are associated with a poorer prognosis in non-muscle invasive bladder cancers [29]. Additionally, urine proteome reveals that ANXA11, CDC42, NAPA and SLC25A4 were positively associated with the risk of gastric lesion progression into malignancy, with an AUC (95% confidence interval) of 0.92 (0.83-1.00), based on a case-control study of 255 cases from Linqu, China, a high-risk area for gastric cancer [30]. Urinary peptide signature represents a biomarker for the diagnosis of colorectal cancer and the development of liver metastases.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of machine learning models for diagnosis and prognosis of cancer by urinary proteomics, based on the FLEMENGHO cohort

Wang

2024

Am J Cancer Res

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The current study aims to develop and validate machine learning (ML) models for the prediction of cancer status by the non-invasive urinary proteomic in a population-based cohort. In this retrospective study, urinary proteome profiles in 804 cases from the FLEMENGHO cohort were measured by mass spectrometry. After feature selection by LASSO on both clinical variables and urinary proteome profile, benchmark models by clinical variables were built with six different ML algorithms. Proteome-based models and combined models were built and compared with the benchmark models. The models' performance, i.e. area under the curve (AUC) was compared by Delong method. The 95% confidence interval was estimated by the bootstrapping method. The best-performing model was explained by Shapley Additive Explanations (SHAP) method. The predictive role of proteome biomarkers in longitudinal cancer diagnosis was also explored. A clinical model, based on age, blood sugar and blood lipid profile, yielded the best AUC of 0.75 (0.68-0.82), with 0.80 (0.72-0.91) for the proteome model based on 13 selected biomarkers and 0.83 (0.77-0.90) for the combined model (P=0.01 for comparison with clinical model). SHAP on the support vector machine in the combined setting showed that except for age, proteome biomarkers contribute to the final prediction of the model. After adjusting with clinical factors, three proteome biomarkers are independent risk factors for longitudinal cancer development. Urinary proteome profiling, together with fine-tuned machine learning algorithms, demonstrates the predictive potential for cancer diagnosis transparently.

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Section: Discussionmentioning

confidence: 99%

Development and validation of machine learning models for diagnosis and prognosis of cancer by urinary proteomics, based on the FLEMENGHO cohort

Wang

2024

Am J Cancer Res

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“…A comprehensive study on the human urinary proteome reported 1823 proteins in normal human urine [129]. In recent years, an increasing number of studies have adopted different urinary proteomics workflows, e.g., LC-MS/MS, to discover GC diagnostic markers: in cohorts of GC patients differing in number and clinics, the increase in sortilin 1 (SORT1), vitronectin (VTN) [92], annexin A11 (ANXA11), cell division control protein 42 homolog (CDC42), NSF attachment protein α (NAPA), solute carrier family 25 member 4 (SLC25A4) [93], disintegrin and metalloproteinase domain-containing protein 12 (ADAM12), with either Trefoil Factor 1 (TFF1) and H. pylori [94] or matrix metallopeptidase 9/neutrophil gelatinase-associated lipolalin (MMP-9/NGAL) complex [95] and a decrease in endothelial lipase (EL) in GC urine were promising diagnostic markers of GC. The high heterogeneity reported for plasma/serum proteomics also emerges when considering the results obtained with other fluid biological matrices (e.g., ascitic fluid, gastric juice, saliva, and urine) (Table 2).…”

Section: Non-blood-based Circulating Biomarkersmentioning

confidence: 99%

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Repetto,

Vettori,

Steffan

et al. 2023

IJMS

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Gastric cancer (GC) is a highly malignant disease affecting humans worldwide and has a poor prognosis. Most GC cases are detected at advanced stages due to the cancer lacking early detectable symptoms. Therefore, there is great interest in improving early diagnosis by implementing targeted prevention strategies. Markers are necessary for early detection and to guide clinicians to the best personalized treatment. The current semi-invasive endoscopic methods to detect GC are invasive, costly, and time-consuming. Recent advances in proteomics technologies have enabled the screening of many samples and the detection of novel biomarkers and disease-related signature signaling networks. These biomarkers include circulating proteins from different fluids (e.g., plasma, serum, urine, and saliva) and extracellular vesicles. We review relevant published studies on circulating protein biomarkers in GC and detail their application as potential biomarkers for GC diagnosis. Identifying highly sensitive and highly specific diagnostic markers for GC may improve patient survival rates and contribute to advancing precision/personalized medicine.

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“…ANXA11 mediates RNA transportation by binding RNA to active lysosomes and performing critical cellular functions involved in the progression of systemic autoimmune disease [13,14] . Recent studies have shown that malfunction of ANXA11 was correlated with clinicopathologic characteristics and the initiation and progression of malignant tumours [15][16][17][18][19][20][21][22][23][24] .…”

Section: Introductionmentioning

confidence: 99%

Annexin A11(ANXA 11) facilitates oral squamous cell carcinoma progression via activating the PI3K/AKT signaling pathway

Xu,

Li,

Zhang

et al. 2024

Preprint

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Oral squamous cell carcinoma(OSCC) is one of the most frequent malignancies with a high incidence of recurrence and metastasis, accounting for about 90% of the oral and maxillofacial malignant neoplasms. The Annexin A11 (ANXA11) gene has been implicated in various human cancers, with highly expressed ANXA11 reported in the majority of cancers. In the current study, ANXA11 is a highly expressed gene in OSCC patients associated with the TNM stage, degrees of differentiation, and lymph node metastasis. Small interfering RNA verified that ANXA11 accelerated OSCC cell proliferation in vitro by involving the cell cycle and expedited neoplasm growth in vivo. Additionally, the knockdown of ANXA11 effectively suppressed metastasis capacity. Mechanistically, ANXA11 knockdown decreases the phosphorylation of PI3K and AKT, inferring that ANXA11 facilitates OSCC progression via activating the PI3K/AKT signalling pathway in OSCC. In summary, ANXA11, as a novel oncogene, is a potential therapeutic target in OSCC.

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Urine proteomic signatures predicting the progression from premalignancy to malignant gastric cancer

Cited by 14 publications

References 48 publications

Development and validation of machine learning models for diagnosis and prognosis of cancer by urinary proteomics, based on the FLEMENGHO cohort

Development and validation of machine learning models for diagnosis and prognosis of cancer by urinary proteomics, based on the FLEMENGHO cohort

Circulating Proteins as Diagnostic Markers in Gastric Cancer

Annexin A11(ANXA 11) facilitates oral squamous cell carcinoma progression via activating the PI3K/AKT signaling pathway

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