Correlation of multiple peripheral blood parameters with metastasis and invasion of papillary thyroid cancer: a retrospective cohort study

doi:10.21203/rs.3.rs-5330530/v1

Download PDF

Research Article

Correlation of multiple peripheral blood parameters with metastasis and invasion of papillary thyroid cancer: a retrospective cohort study

https://doi.org/10.21203/rs.3.rs-5330530/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objective

The primary progression mechanisms of papillary thyroid cancer (PTC) involve the development of lymph node metastasis and thyroid capsular invasion. This study aimed to identify high-risk populations for these conditions in PTC based on various peripheral blood tests, and to investigate the factors significantly associated with such populations.

Methods

A total of 4,557 patients with papillary thyroid cancer were retrospectively analyzed in this study. Principal Component Analysis (PCA) and cluster analysis were conducted using the results from 45 different peripheral blood tests. High-risk and low-risk clusters were defined by comparing the prevalence of metastasis and invasion across different clusters. Key indicators that significantly differed between clusters were identified to examine the correlation between peripheral blood parameters and tumor progression.

Results

Preoperative examination measures categorized patients into two distinct clusters. Patients in Cluster 0 exhibited a significantly higher rate of tumor metastasis and invasion compared to those in Cluster 1, and were thus categorized as the high-risk group. Following PCA, four principal components showing the most significant differences between the clusters were identified. These components were further analyzed to determine the most crucial peripheral blood parameters. Subsequent multiple logistic regression analysis revealed that parameters such as transaminase levels, white blood cell and red blood cell counts, coagulation time, and thyroid hormones were significantly associated with membership in Cluster 0.

Conclusion

Several peripheral blood parameters, including transaminase levels, white and red blood cell counts, coagulation time, and thyroid hormones, are linked to the metastasis and invasion of papillary thyroid cancer. These findings suggest that peripheral blood parameters hold potential predictive value for disease progression in PTC, offering a basis for more targeted and effective clinical management.

Papillary thyroid cancer (PTC) is a type of neoplastic disease known for its favorable prognosis, it seldom develops into distant or systemic metastasis¹. The most significant progression pathways in PTC involve the development of regional lymph node metastasis (LNM) and thyroid capsular invasion (CI). These two pathological features are directly linked to the pathological stage of PTC and significantly influence the timing and approach of surgical treatment². Due to the absence of effective pre-surgical prediction methods for these pathological features, many PTC patients undergo lymphadenectomy even without evidence of lymph node metastasis, leading to unnecessary trauma³.

Currently, the primary method for preoperative examination of metastasis and invasion in PTC is ultrasound. However, there are no standardized ultrasound diagnostic criteria for identifying LNM or capsular invasion CI, as these pathological features are often difficult to discern directly from ultrasound images^4,5. To overcome the limitations of manual recognition of image features, an increasing number of researchers are employing machine learning algorithms to extract image-omics features from ultrasound images, thereby enhancing the accuracy and stability of ultrasound predictions⁶. Furthermore, clinical prediction models that integrate ultrasound features with certain clinical characteristics can also relatively effectively predict PTC in specific populations^7,8. While these models or novel diagnostic tools have improved the predictive power for LNM and CI, the localization of ultrasound images still heavily relies on the operator’s experience. In particular, the application of such models may be limited and challenging to interpret when the tumor characteristics under ultrasound are very indistinct.

Compared to ultrasound, peripheral blood detection indices are relatively easier to obtain, and the methods of detection are more standardized, allowing for more objective use in diagnosis. Unfortunately, unlike other tumors, there are few direct tests available for predicting LNM and CI)in PTC preoperatively, such as tumor markers⁹. Nevertheless, different studies have shown that there is a certain correlation between some conventional test indicators in peripheral blood and PTC, such as the proportion of blood cells and thyroid peroxidase antibody, have been shown to have some correlation with tumor metastasis and invasion^{10, 11}. In addition, thyroglobulin and thyroglobulin antibodies are the most prominent markers thought to be directly associated with differentiated thyroid cancer, and their levels in blood are often used as criteria to assess whether PTC recurs after surgery¹². At the same time, they can also be used to assist in the prediction of pathological conditions of PTC before surgery^13,14. Considering that thyroid cancer has been reported to be associated with liver and kidney diseases in previous studies, liver and kidney disease-related indicators such as transaminases and creatinine in peripheral blood may also be associated with pathological conditions that can reflect thyroid cancer^15,16. And in previous studies, the inclusion of these indicators is relatively single, which may be biased due to the complexity of peripheral blood components. However, this still suggests that multiple peripheral blood tests are associated with the pathological status of PTC and have the potential to be used as diagnostic indicators.

Therefore, in this study, we aimed to identify which peripheral blood parameters could serve as predictors for metastasis and invasion in PTC. Our goal is to provide surgeons with insights into specific clinical features that warrant closer attention for more scientifically informed treatment approaches. To achieve this, we utilized machine learning with unsupervised clustering to explore a broader range of predictors, based on multidimensional peripheral blood tests, that are highly associated with PTC metastasis and invasion.

The study included 4557 patients who had been hospitalized from 2018 to 2022. These patients had undergone thyroidectomy for the first time and cervical lymphadenectomy at a general medical center in central China, all of whom had definitive confirmation of PTC by postoperative pathological examination and clearly reported the presence or absence of LNM or CI. The most recent preoperative examination results were used for unsupervised cluster analysis, with a total of 45 peripheral blood examination indicators, including 15 biochemical indicators, 21 blood cell count indicators, 5 coagulation indicators and 7 thyroid-related indicators (Table 1). These indicators were detected 3 to 5 days before surgery, which were initially used to assess whether patients had other major diseases other than PTC that were not suitable for surgery, and patients with complications or other systemic diseases had been excluded in our study to avoid bias on the study results. This study was approved by the institutional review board of the Wuhan Union Hospital, and written consent has been obtained from each patient or subject after full explanation of the purpose and nature of all procedures used.

We performed dimensionality reduction based on principal component analysis of preoperative peripheral blood parameters in all patients according to four different test categories: biochemistry, blood cell count, coagulation function, and thyroid-related parameters. The data were normalized before principal component transformation, and the principal component score categories consistent with the number of test indicators were obtained after transformation. Key principal components for subsequent unsupervised clustering were screened based on the elbow and screen plots of principal component analysis. The principal components that best represent the features of the original data were clustered using the k-means algorithm, and the number of target clusters was set to 2 classes to obtain different classes that were distinguished according to the heterogeneity of the multidimensional indicators of peripheral blood. Completion of this data analysis requires Python-based sklearn libraries, and specific codes we provide in Supplementary Material in the form of jupyter notebook (PCA + k-means.ipynb)

Chi-square test was used to compare the frequency of metastasis and invasion of PTC in patient categories after unsupervised clustering, and p-values less than 0.05 were considered statistically significant. After identifying the high-risk group and low-risk group, wilcox test is used to compare the degree of difference of principal component score of peripheral blood test indicators between the two groups, and one principal component with the most significant p-value is selected as the principal component that can most distinguish the two types of population. Correlation analysis was used to compare the correlation between principal component scores and the original peripheral blood test indicators, and the original test indicators with the most significant spearman coefficient were selected as the characteristics that best represented the principal components. Multiple logistics regression with two clusters as dependent variables was performed on these original test indicators, and p-values less than 0.05 were considered statistically significant.

The statistical analysis process were completed by python 3.8, R 4.3.1, SPSS 26.0.

Principal Component Analysis and Dimensionality Reduction of Data

We successively reduced the dimension of principal component analysis data for biochemical parameters, blood cell count parameters, coagulation function, and thyroid-related peripheral blood test parameters in all patients. After the raw data were transformed into the main component score, the first few principal components that best represented the raw data characteristics were selected for subsequent cluster analysis based on the results of the elbow plot, with the explanation retained above 0.8 as the threshold (Figure 1). Among them, 15 principal components of biochemical test indicators retained the first 6 items (bio0-bio5), 21 principal components of blood cell count indicators retained the first 6 items (BCC0-BCC5), 5 principal components of coagulation function indicators retained the first 2 items (cog1, cog2), and 7 principal components of thyroid-related test indicators retained the first 5 items (thy0-thy4).

Cluster analysis

The above retained 19 principal components were clustered as characteristics of patients, and all patients were divided into 2 categories (cluster0, cluster1). We performed statistical analysis and comparison of the proportions of LNM and CI in the two clusters of patients (Table 2). Among them, patients with LNM were 56.2% in cluster0 and 44.5% in cluster1 (p < 0.001). Patients with CI were 64.7% in cluster0 and 60.0% in cluster1 (p = 0.007). We analyzed differences in the general characteristics of the population between cluster0 and cluster1, and neither age nor gender showed significant statistical differences, ruling out their influence on pathological characteristics.(Table S1). Obviously, cluster0 PTC patients had a significantly higher proportion of combined metastasis and invasion than cluster1, so cluster0 patients were defined as a high-risk group for cancer metastasis and invasion, while cluster1 was relatively defined as a low-risk group. It also showed that the differences in peripheral blood parameters could comprehensively reflect the degree of progression of PTC.

Correlation analysis and regression analysis

In order to further clarify the role of each principal component in distinguishing the two types of population during cluster analysis, we analyzed the difference between the principal component scores of each type of test index retained for the two types of population. Among the principal components of biochemical test indicators, bio0 was the most significantly different between the two groups, BCC0 was the most significant among the principal components of blood cell count, thy1 was the most significant among the thyroid-related principal components, and cog1 was the most significant among the principal components of coagulation function. Their wilcox tests had p-values less than 1^-50 (Figure 2).

The principal components with the most significant differences were further assessed for correlation with the original test measures, giving specific clinical significance to these principal components. The results of correlation test showed that the principal component score of bio0 had the strongest correlation with alanine aminotransferase (ALT) and aspartate aminotransferase (AST) (r > 0.6, p < 0.001), the principal component score of BCC0 had the strongest correlation with leucocyte, neutrophils, red blood cell hematocrit, and hemoglobin (r < -0.6, p < 0.001), the principal component score of thy1 had the strongest correlation with free triiodothyronine (FT3) and free tetraiodothyronine (FT4) (r > 0.4, p < 0.001), and the principal component score of cog1 had the strongest correlation with thrombin time (r = 0.814, p < 0.001) (Figure 3). These results suggest that these original test indicators of peripheral blood have a certain degree of correlation with the metastasis and invasion of PTC.

To confirm the above results, we performed multiple logistics regression (Figure 4) between the above 9 original test indicators with the most correlation with key principal component scores and the clustering results for PTC patients, and all peripheral blood indicators had a significant risk effect or protective effect on the high-risk population (cluster0) except free tetraiodothyronine, which was not statistically significant (p < 0.01). It shows that combining multiple kinds of peripheral blood parameters can comprehensively reflect the metastasis and invasion status of PTC, and may combine these indicators to develop more intuitive diagnostic models or novel biomarkers based on a larger population.

Because most tumors involve changes in various systems during their development and progression. Therefore, the detection indicators in peripheral blood that can reflect the general condition have very important diagnostic and prognostic value for neoplastic diseases, such as circulating tumor DNA and circulating tumor cells ^17,18. However, PTC is mainly concentrated in the local thyroid gland due to its low malignancy, and indicators currently considered to be directly related to the lesion, such as thyroglobulin, which is highly heterogeneous in different population studies and can only be used to indicate the condition of postoperative recurrence, but not for preoperative prediction^19,20. Therefore, the preoperative severity of PTC is rarely effectively assessed by biomarkers or other parameters in peripheral blood.

Our study included common peripheral blood tests preoperatively, which have not been clearly demonstrated to have a clear association with the degree of progression of PTC, such as metastasis and invasion. Some studies have reached some preliminary conclusions by retrospective analysis, especially by constructing clinical prediction models together with ultrasound imaging indicators, or supervised learning models based on machine learning algorithms. After combining the results of preoperative thyroid antibodies or a serological marker, the predictive efficacy of ultrasound for LNM can be further improved^21,22. However, the limitation of these studies lies in the relatively limited sample size on the one hand, and the very complex composition of peripheral blood on the other hand, and the combination of single or several indicators may not be able to measure the potential relationship between peripheral blood and thyroid tumors more comprehensively. In addition, researchers tend to pay more attention to the overall predictive power of diagnostic models in diagnostic tests, especially when performing supervised machine learning. The contribution of each predictor to the model is not particularly interpretable²³. For example, image-omics studies rely more on the power of the improved algorithm itself for disease prediction than on the clinical significance of each independent variant⁶.

In this study, we included a large number of multidimensional peripheral blood tests. PTC patients were mainly classified according to these peripheral blood parameters by means of unsupervised clustering. Because pathologic conditions of whether PTC developed metastasis and invasion were not included in the classification process, and unsupervised algorithms only emphasized differences between data rather than convergencing²⁴. Therefore, when our results indicate that patients after clustering do have differences in PTC invasion and metastasis between different clusters, it is even more likely to illustrate the potential association of these indicators with the progression of PTC when combined. Among them, laboratory tests in peripheral blood such as blood counts, biochemical parameters, thyroid function, and coagulation function all contribute significantly to the clustering results of high-risk populations for PTC metastasis and invasion.

Among the biochemical parameters, the most relevant ones were ALT and AST, which mainly reflected the patient 's liver function. Present results suggest that nonalcoholic fatty liver disease is associated with a risk of thyroid cancer and that this risk increases with increasing ALT levels, in line with our findings ²⁵. However, in another study, the increased ratio of AST and ALT was found to be elevated in patients with thyroid cancer²⁶.Different studies have shown the complexity of the two transaminases for the development and progression of PTC. Overall, the primary etiology of nonalcoholic fatty liver disease is closely related to metabolism, and previous studies have shown that the metabolomics of thyroid cancer also has a unique mechanism of change²⁷, reflecting the complexity of the process of tumor development, therefore, changes in AST and ALT may also indirectly reflect the potential burden of thyroid cancer development on liver metabolism. Blood count parameters are also very complex for metastasis and invasion of PTC, and usually progression of cancer leads to the development of anemia, so for most cancers, low hemoglobin is a risk factor for progression²⁸.This is contrary to our findings. We suggest that this phenomenon may occur in association with angiogenesis during tumor metastasis and invasion, as well as causing a compensatory increase in systemic red blood cells by a hypoxic state in the local microcirculation²⁹. Neutrophils are considered protective against invasion and metastasis of PTC in our study, which could reflect that elevated levels of neutrophils are more beneficial in inhibiting cancer progression in the absence of co-infection, as also confirmed by related studies^{30, 31}. However, the increase in total leukocyte count still predicts a poor prognosis in PTC, indicating that inflammatory cells in peripheral blood may still reflect a pro-tumor trend as a whole. In addition, thrombin time and FT3 levels were both significant adverse factors for PTC progression. What is known is that adverse effects on coagulation function emerge as a chronic inflammatory state that adversely impacts tumor prognosis, which has been demonstrated in other studies³². Although hyperthyroidism itself is a protective factor for the development of thyroid cancer, low TSH is not conducive to the growth of thyroid cancer cells. However, regardless of TSH, a cohort study has similarly shown that high levels of thyroid hormones are also associated with thyroid cancer incidence³³. We suggest that increased thyroid hormone synthesis may also reflect active physiological function of the thyroid gland and also predict active cell division and proliferation, so it exists as a risk factor for adverse pathological features of PTC in our study.

To explore biomarkers that are more biologically meaningful and intuitive for PTC. Methods for high-throughput sequencing have also been used in different studies. Although the samples examined in these studies were mainly postoperative pathological tissues of the thyroid gland, or thyroid tissue obtained by fine needle aspiration cytology before surgery, rather than peripheral blood. However, sequencing results can also be reflected in indicators in peripheral blood by the association of the gene co-expression level behind them, and novel sequencing-based biomarkers, such as non-coding RNAs ^34,35, have also been developed for peripheral blood of PTC patients. In line with the conclusions of our study, these similar markers, such as MiR-221 and MiR-146, were also detected to be specific in patients with chronic liver injury³⁶, inflammatory disease³⁷, and thrombus-related disease³⁸. Thus, our study also provides a rationale for exploring potential common pathogenic mechanisms between PTC and other diseases. Certainly, because this study was a retrospective study, bias in cohort selection remains. To further demonstrate the reliability of our findings, more rigorous prospective studies are still needed to validate our results in the future.

After unsupervised machine learning and cluster analysis, a number of preoperative peripheral blood tests, including transaminase, white blood cell and red blood cell counts, coagulation time, and thyroid hormones, were associated with the risk of metastasis and invasion of PTC, suggesting that the biological progression of thyroid tumors can be predicted by peripheral blood and the potential pathogenic mechanisms of PTC can be deeply explored.

Declaration of interest

The authors declare no conflict of interest.

Funding

This work was funded by the Hubei Province Key Laboratory of Molecular Imagine, Grant(No.2021fzyx016), the Principal Investigator is Han-yu Wang, the Wuhan Knowledge Innovation Project,Grant(No.2023020201010162), the Principal Investigator is Hui Sun, and the Technology Innovation Project of Hubei Province, Grant(No.2023BCB131), the Principal Investigator is Hui Sun.

Author Contribution

All authors made substantial contributions to the conception and design of this study. XC performed the data analyses and wrote the manuscript; HYW performed the data collection and prepared the manuscript. HS contributed to the conception of the study and provided professional comments on the content. LY and JQL helped with data collection.

Acknowledgements

The authors are grateful to all the organizations and people who participated in the study, Huazhong University of Science and Technology.

Data Availability

De-identified datasets analyzed in this study are available from the corresponding author upon reasonable request.

Cabanillas ME, McFadden DG, Durante C. Thyroid cancer. Lancet (London, England). 2016;388(10061):2783–95.
Schlumberger M, Leboulleux S. Current practice in patients with differentiated thyroid cancer. Nature reviews Endocrinology. 2021;17(3):176–88.
Chou R, Dana T, Haymart M, Leung AM, Tufano RP, Sosa JA, Ringel MD. Active Surveillance Versus Thyroid Surgery for Differentiated Thyroid Cancer: A Systematic Review. Thyroid: official journal of the American Thyroid Association. 2022;32(4):351–67.
Chen YH, Zhang YQ. Diagnostic potential of ultrasonography and computed tomography in differentiating cervical lymph node metastasis of thyroid cancer: a systematic review and meta-analysis. Arch Med Sci. 2023;19(4):965–75.
Rago T, Vitti P. Risk Stratification of Thyroid Nodules: From Ultrasound Features to TIRADS. Cancers. 2022;14(3).
Cao Y, Zhong X, Diao W, Mu J, Cheng Y, Jia Z. Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations. Cancers. 2021;13(10).
Lu J, Liao J, Chen Y, Li J, Huang X, Zhang H, Zhang B. Risk factor analysis and prediction model for papillary thyroid carcinoma with lymph node metastasis. Frontiers in endocrinology. 2023;14:1287593.
Li J, Xia F, Wang X, Jin Y, Yan J, Wei X, Zhao Q. Multiclassifier Radiomics Analysis of Ultrasound for Prediction of Extrathyroidal Extension in Papillary Thyroid Carcinoma in Children. Int J Med Sci. 2023;20(2):278–86.
Belousov PV. The Autoantibodies against Tumor-Associated Antigens as Potential Blood-Based Biomarkers in Thyroid Neoplasia: Rationales, Opportunities and Challenges. Biomedicines. 2022;10(2).
Riguetto CM, Barreto IS, Maia FFR, Assumpção L, Zantut-Wittmann DE. Usefulness of pre-thyroidectomy neutrophil-lymphocyte, platelet-lymphocyte, and monocyte-lymphocyte ratios for discriminating lymph node and distant metastases in differentiated thyroid cancer. Clinics (Sao Paulo). 2021;76:e3022.
Noel JE, Thatipamala P, Hung KS, Chen J, Shi RZ, Orloff LA. Pre-Operative Antithyroid Antibodies in Differentiated Thyroid Cancer. Endocr Pract. 2021;27(11):1114–8.
Boucai L, Zafereo M, Cabanillas ME. Thyroid Cancer: A Review. Jama. 2024;331(5):425–35.
Chen B, Yan Z, Bao Y, Li J, Luo C, Yang G, Li T, Cheng X, Lv J. Detection of thyroglobulin for diagnosis of metastatic lateral cervical lymph nodes in papillary thyroid carcinoma: accuracy and application in clinical practice. Transl Cancer Res. 2024;13(2):1043–51.
Li L, Shan T, Sun X, Lv B, Chen B, Liu N, Zhang B, Hu S, Zeng Q, Turner AG, Sheng L. Positive Thyroid Peroxidase Antibody and Thyroglobulin Antibody are Associated With Better Clinicopathologic Features of Papillary Thyroid Cancer. Endocr Pract. 2021;27(4):306–11.
Kwon H, Han KD, Moon SJ, Park SE, Rhee EJ, Lee WY. Nonalcoholic Fatty Liver Disease and the Risk of Thyroid Cancer Among Young Adults in South Korea. J Clin Endocrinol Metab. 2024;109(3):e1095-e104.
Kirnap NG, Peker H, Kirnap M, Akdur A, Akcay EY, Moray G. Thyroid cancer incidence and clinicopathological differences in patients with end-stage renal failure. Ann Ital Chir. 2020;91.
Deng Z, Wu S, Wang Y, Shi D. Circulating tumor cell isolation for cancer diagnosis and prognosis. EBioMedicine. 2022;83:104237.
Stadler JC, Belloum Y, Deitert B, Sementsov M, Heidrich I, Gebhardt C, Keller L, Pantel K. Current and Future Clinical Applications of ctDNA in Immuno-Oncology. Cancer Res. 2022;82(3):349–58.
Nixon AM, Provatopoulou X, Kalogera E, Zografos GN, Gounaris A. Circulating thyroid cancer biomarkers: Current limitations and future prospects. Clinical endocrinology. 2017;87(2):117–26.
Algeciras-Schimnich A. Thyroglobulin measurement in the management of patients with differentiated thyroid cancer. Critical reviews in clinical laboratory sciences. 2018;55(3):205–18.
Luo S, Lai F, Liang R, Li B, He Y, Chen W, Zhang J, Li X, Xu T, Hou Y, Liu Y, Long J, Yang Z, Chen X. Clinical prediction models for cervical lymph node metastasis of papillary thyroid carcinoma. Endocrine. 2024.
Huang Y, Huang Z, Cai H, Zhuge L, Wang S, Yan D, Zhang X, An C, Niu L, Li Z. Evaluation of serum B7-H3 expression, ultrasound and clinical characteristics to predict the risk of cervical lymph node metastases in papillary thyroid carcinoma by nomogram. J Clin Lab Anal. 2023;37(1):e24811.
Kierner S, Kucharski J, Kierner Z. Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review. J Biomed Inform. 2023;144:104428.
Jafari M, Wang Y, Amiryousefi A, Tang J. Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine. Front Pharmacol. 2020;11:1319.
Wang Z, Zhao X, Chen S, Wang Y, Cao L, Liao W, Sun Y, Wang X, Zheng Y, Wu S, Wang L. Associations Between Nonalcoholic Fatty Liver Disease and Cancers in a Large Cohort in China. Clin Gastroenterol Hepatol. 2021;19(4):788 – 96.e4.
Crudele L, Novielli F, De Matteis C, Petruzzelli S, Suppressa P, Berardi E, Antonica G, Piazzolla G, Sabbà C, Graziano G, Moschetta A. Thyroid nodule malignancy is associated with increased non-invasive hepatic fibrosis scores in metabolic subjects. Frontiers in oncology. 2023;13:1233083.
Qu N, Chen D, Ma B, Zhang L, Wang Q, Wang Y, Wang H, Ni Z, Wang W, Liao T, Xiang J, Wang Y, Jin S, Xue D, Wu W, Wang Y, Ji Q, He H, Piao HL, Shi R. Integrated proteogenomic and metabolomic characterization of papillary thyroid cancer with different recurrence risks. Nat Commun. 2024;15(1):3175.
Chi G, Lee JJ, Montazerin SM, Marszalek J. Prognostic value of hemoglobin-to-red cell distribution width ratio in cancer: a systematic review and meta-analysis. Biomark Med. 2022;16(6):473–82.
Chen X, Zhou H, Lv J. The Importance of Hypoxia-Related to Hemoglobin Concentration in Breast Cancer. Cell Biochem Biophys. 2024.
He J, Zhou M, Yin J, Wan J, Chu J, Jia J, Sheng J, Wang C, Yin H, He F. METTL3 restrains papillary thyroid cancer progression via m(6)A/c-Rel/IL-8-mediated neutrophil infiltration. Mol Ther. 2021;29(5):1821–37.
Zheng D, Yang J, Qian J, Jin L, Huang G. Fibrinogen-to-Neutrophil Ratio as a New Predictor of Central Lymph Node Metastasis in Patients with Papillary Thyroid Cancer and Type 2 Diabetes Mellitus. Cancer Manag Res. 2022;14:3493–505.
Cantrell R, Palumbo JS. The thrombin-inflammation axis in cancer progression. Thromb Res. 2020;191 Suppl 1:S117-s22.
Kim TH, Lee MY, Jin SM, Lee SH. The association between serum concentration of thyroid hormones and thyroid cancer: a cohort study. Endocrine-related cancer. 2022;29(12):635–44.
Rogucki M, Buczyńska A, Krętowski AJ, Popławska-Kita A. The Importance of miRNA in the Diagnosis and Prognosis of Papillary Thyroid Cancer. J Clin Med. 2021;10(20).
Geropoulos G, Psarras K, Papaioannou M, Giannis D, Meitanidou M, Kapriniotis K, Symeonidis N, Pavlidis ET, Pavlidis TE, Sapalidis K, Ahmed NM, Abdel-Aziz TE, Eddama MMR. Circulating microRNAs and Clinicopathological Findings of Papillary Thyroid Cancer: A Systematic Review. In Vivo. 2022;36(4):1551–69.
Markovic J, Sharma AD, Balakrishnan A. MicroRNA-221: A Fine Tuner and Potential Biomarker of Chronic Liver Injury. Cells. 2020;9(8).
Jiang C, Lin Y, Shan H, Xia W, Pan C, Wang N, Zhou L, Gao Y, Zhou Z, Yu X. miR-146a Protects against Staphylococcus aureus-Induced Osteomyelitis by Regulating Inflammation and Osteogenesis. ACS Infect Dis. 2022;8(5):918–27.
Kotlyar MJ, Krebs M, Solimando AG, Marquardt A, Burger M, Kübler H, Bargou R, Kneitz S, Otto W, Breyer J, Vergho DC, Kneitz B, Kalogirou C. Critical Evaluation of a microRNA-Based Risk Classifier Predicting Cancer-Specific Survival in Renal Cell Carcinoma with Tumor Thrombus of the Inferior Vena Cava. Cancers. 2023;15(7).

Table 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

Table12.docx

Download PDF

Editor assigned by journal
28 Oct, 2024
Submission checks completed at journal
28 Oct, 2024
First submitted to journal
25 Oct, 2024

You are reading this latest preprint version

Correlation of multiple peripheral blood parameters with metastasis and invasion of papillary thyroid cancer: a retrospective cohort study

Status:

Version 1

Abstract

Objective

Methods

Results

Conclusion

Figures

Introduction

Methods

Results

Discussion

Conclusion

Declarations

Declaration of interest

Funding

Author Contribution

Acknowledgements

Data Availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1