Diagnostic approach to thyroid cancer based on amino acid metabolomics in saliva by ultra-performance liquid chromatography with high resolution mass spectrometry

Zhang, Jing; Wen, Xinxin; Li, Yuting; Li, Xian; Cheng, Qian; Tian, Yonghui; Ling, Rui; Duan, Yixiang

doi:10.1016/j.talanta.2021.122729

Cited by 24 publications

(26 citation statements)

References 47 publications

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“…Currently, fine-needle aspiration is the most reliable diagnostic tool for PTC; however, it cannot be used to cytologically distinguish between benign and malignant PTC and thus cannot be used to evaluate the risk of PTC metastasis ( 10 , 11 ). Previous studies have found characteristic genetic changes in PTC, such as RET - PTC , NTRK rearrangements, RAS and BRAF mutations, and PAX8 / PPAR translocation ( 12 , 13 ), which have been used for the diagnosis of PTC; however, the genetic changes currently found in PTC cannot be used to characterize tumors with different clinical behaviors, such as LN metastasis ( 13 , 14 ). Identifying genetic changes that can help diagnose PTC and assess the risk of metastasis is crucial for early clinical intervention in high-risk PTC patients.…”

Section: Introductionmentioning

confidence: 99%

Screening and validation of lymph node metastasis risk-factor genes in papillary thyroid carcinoma

Zhang

Shen

et al. 2022

Front. Endocrinol.

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BackgroundAlthough most papillary thyroid carcinoma (PTC) cases have a good prognosis, some PTCs are more aggressive and are often accompanied by lymph node (LN) metastasis, a high recurrence rate, and poor prognosis. Distinguishing highly invasive metastatic PTC is an urgent problem that needs to be addressed clinically. We analyzed a microarray of metastasized PTC and validated it using quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry to identify biomarkers that can be used to assess the risk of PTC metastasis.MethodsThe microarray of metastasized PTC was screened using the Gene Expression Omnibus (GEO) database. The differences between cancer and normal tissues were analyzed using the official GEO tool: GEO2R. Gene expression profile data (GEPIA) were used to verify the expression of differential genes in large samples and to analyze their correlation. The Kaplan–Meier plotter (KM-plotter) database was used for the analysis of genes potentially related to survival. RT-qPCR was used to check the expression of risk factor genes in pathological sections from PTC patients with clinical LN metastasis. Immunohistochemistry was used to verify the expression of core risk-associated genes.ResultsFourteen PTC metastasis-associated genes were identified. In metastasized PTC, CLDN1, LRP4, LRRK2, and TENM1 were highly expressed, whereas DIO1, DPP6, HGD, IPCEF1, MT1F, SLC26A4, SLC26A7, SPX, TFF3, and TPO were expressed at low levels, compared to expression in normal tissues. DIO1, HGD, SLC26A4, and TPO were found to be the core risk genes in the PTC metastatic risk set. Results based on clinical samples showed that the expression differences for metastasis risk-associated genes were consistent with the bioinformatics analysis results.ConclusionsFourteen differentially expressed genes (CLDN1, LRP4, LRRK2, TENM1, DIO1, DPP6, HGD, IPCEF1, MT1F, SLC26A4, SLC26A7, SPX, TFF3, TPO) are associated with an increased risk of PTC metastasis, and DIO1, HGD, SLC26A4, and TPO are the key risk-associated genes in this set that might affect the occurrence and development of PTC through iodine metabolism. These genes could provide a reference for clinical metastatic PTC risk evaluation and treatment.

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Section: Introductionmentioning

confidence: 99%

Screening and validation of lymph node metastasis risk-factor genes in papillary thyroid carcinoma

Zhang

Shen

et al. 2022

Front. Endocrinol.

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“… Quality assessment, including the main potential risk of bias (risk level: green—low, yellow—unspecified, red—high; quality score: green—good, yellow—intermediate, red—poor) [ 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. …”

Section: Methodsmentioning

confidence: 99%

Salivary Metabolomics for Systemic Cancer Diagnosis: A Systematic Review

et al. 2022

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Cancers are the leading cause of death worldwide. The most common cancers include breast, lung, and colorectum. Salivary metabolome profiling is a novel non-invasive method in oncological diagnosis. This systematic review was designed to answer the question “Are salivary metabolites reliable for the diagnosis of systemic cancers?”. Following the inclusion and exclusion criteria, nineteen studies were included (according to PRISMA statement guidelines). Changes in salivary metabolome were most commonly determined in patients with breast cancer, gastrointestinal cancers, and lung cancer. Most studies involved unstimulated whole saliva as the diagnostic material, evaluated by different spectroscopic methods. Among the found saliva metabolites, the alterations in the metabolic pathways of amino acids and polyamines were most frequently observed, which showed significant predictive values in oncological diagnostics. The most frequently encountered risks of bias were the absence of data regarding blinding, sample size justification, and randomisation. In conclusion, salivary metabolites seem to be potentially reliable for detecting the most common systemic cancers. However, further research is desirable to confirm these outcomes and to detect new potential metabolic biomarkers in saliva.

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“…Bone suture osteogenesis plays an important role in the process of bone formation, and the development of maxillofacial bones, especially the face and skull, is very important [18]. Animal studies have shown that skull fusion in mice is caused by overexposure to thyroxine in utero [19]. Premature closure of mouse skull sutures may be due to enhancement of differentiation of osteoblasts at bone sutures due to excessive thyroxine [20].…”

Section: Effects Of Erk and P38mapk Signaling Pathways On Mrna And Pr...mentioning

confidence: 99%

Triiodothyronine participates in odontoblast differentiation of apical papilla stem cells through regulation of ERK and p38MAPK signaling pathways

Xu¹,

Li²,

Yang³

2022

Trop. J. Pharm Res

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Purpose: To investigate the effect of triiodothyronine (T3) in odontoblast differentiation of apical papilla stem cells, and the mechanism of action involved. Methods: Apical unclosed permanent molars extracted from patients due to orthodontics and impaction were selected. The extracted teeth were cultured in the isolation stage of SCAP cells. The cells were exposed to different concentrations of T3. The effects of ERK and p38 MAPK signaling pathways on activity of alkaline phosphatase (ALP) were determined. Calcium deposition was measured using a calcium determination kit, while the expression of BMP - 2 protein by T3 was determined by Western blot assay. Fluorescence quantitative polymerase chain reaction (FqPCR) method was used to determine the mRNA expression of BMP. Results: The ALP activities were significantly higher in T3 groups than in control group. Relative to control, there were marked differences in ALP activity and calcium deposition in T3 group, T3 + PD group and T3 + SB group (p < 0.05). Relative to control, the mRNA and protein expressions of BMP-2 in T3 group were increased significantly (p < 0.05). Conclusion: Triiodothyronine regulates the differentiation of apical papilla stem cells into dentin through ERK and p38MAPK signaling pathways. This provides the mechanism underlying odontoblast differentiation of apical papilla stem cells.

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Diagnostic approach to thyroid cancer based on amino acid metabolomics in saliva by ultra-performance liquid chromatography with high resolution mass spectrometry

Cited by 24 publications

References 47 publications

Screening and validation of lymph node metastasis risk-factor genes in papillary thyroid carcinoma

Screening and validation of lymph node metastasis risk-factor genes in papillary thyroid carcinoma

Salivary Metabolomics for Systemic Cancer Diagnosis: A Systematic Review

Triiodothyronine participates in odontoblast differentiation of apical papilla stem cells through regulation of ERK and p38MAPK signaling pathways

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