Correlation analysis and exploration of potential biomarkers in patients with breast cancer combined with thyroid cancer

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The common differential genes of breast cancer and thyroid cancer were identified by Gene Express Omnibus database (GEO). The Cancer Genome Atlas (TCGA) database was used to determine the relationship between the screened differential genes and the clinicopathologic features. Gene set enrichment analysis (GSEA) was used to identify the enrichment pathways of the differential genes in breast and thyroid cancers.2. Retrospective studies were conducted to analyze the relationship between the general characteristics, clinicopathological features, and hormone expression levels in patients with dual cancers of breast cancer combined with thyroid cancer and patients with breast cancer and thyroid cancer alone, and to further analyze the KRT19 expression levels in their tissues. analyze the expression of KRT19 protein in their tissues. Method(s): 1. Download the datasets GSE70947 and GSE3467 from the GEO database, analyze the differential genes of breast cancer, thyroid cancer and normal tissues respectively by using R, and take the intersection of the differential genes of the two tissues to continue the next step of the study. 2. Download the mRNA-seq data of the above differential genes of BRCA and THCA from the TCGA database, and identify the differences in expression of differential genes between normal tissues and tumor tissues by using the above differential gene substitution in R. Gene set enrichment analysis (GSEA) identifies the differences in expression of differential genes enriched in breast and thyroid cancer. Differential gene substitution analysis, and identify the differences in differential gene expression between normal and tumor tissues, Gene set enrichment analysis (GSEA) to identify the pathways of enrichment in breast and thyroid cancers, and then screened out the representative differential gene KRT19 (human cytokeratin 19).3. Collect the mRNA-seq data of BRCA and THCA in the database of TCGA, and use the R language in the substitution analysis of the above differential genes, and identify the differences in differential gene expression between normal and tumor tissues. 3. July 2023 in Zhongshan People's Hospital and puncture or surgical treatment of breast cancer combined with thyroid cancer patient data, a total of 92 cases (experimental group), another randomly collected in the past two years in Zhongshan People's Hospital in the simple breast cancer patients 100 cases (control group 1), thyroid cancer patients, 100 cases (control group 2). 4. the experimental group and the control group 1, 2 of the patient pathology data for Pathological data of patients in experimental and control groups 1 and 2 were retrospectively analyzed, and additional surgical bulk or puncture specimens were subjected to immunohistochemical staining (IHC) to examine the expression of KRT19 protein in the tissues, and to explore whether there was a difference in its expression between experimental and control groups. Result(s): 1. KRT19 mRNA levels were significantly overexpressed in breast and thyroid cancer tissues as analyzed by the GEO database, and KRT19 was associated with clinicopathological features of breast and thyroid cancers as analyzed by the TCGA database, and the GSEA showed that both the breast and thyroid cancer-KRT19 overexpression groups were significantly enriched in the estrogen-responsive pathway.2. In the dual-primary cancer group, of which breast cancer preceded In the dual-primary cancer group, breast cancer preceded thyroid cancer in 86 cases and thyroid cancer preceded in 6 cases, i.e., breast cancer preceded thyroid cancer in the vast majority of patients (> 90%). Compared with the breast cancer group, the dual-primary cancer group was younger at the time of diagnosis, more often in the premenopausal state, with a larger tumor size, and more often positively expressed estrogen receptors (ER) and progesterone receptors (PR), with a statistically significant difference (all P < 0.050). Compared with the thyroid cancer group, the odds of tumors occurring bilaterally were increased in the double primary cancer group, and the levels of triiodothyronine (T3) and thyroxine (T4) (both P < 0.050) were significantly higher. KRT19 was more frequently positively expressed in breast cancer than in breast cancer alone in dual primary cancers ( P = 0.069), and in thyroid cancer than in thyroid cancer alone in dual primary cancers ( P < 0.050). Conclusion(s): Pathogenesis correlates between breast and thyroid cancers, estrogen receptor expression is associated with dual carcinogenesis, and KRT19 influences dual carcinogenesis through the estrogen response pathway. Breast Cancer Thyroid Cancer Correlation KRT19 Estrogen Receptor Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Breast cancer (BC) and thyroid cancer (TC) are among the most prevalent malignancies affecting women globally. Recent data indicate that BC is the second most common cancer worldwide, while TC ranks seventh in incidence [ 1 ] . BC is classified into molecular subtypes, with hormone receptor-positive (HR+) tumors (Luminal A and B) constituting approximately 75% of cases [ 2 , 3 ] . Similarly, papillary thyroid carcinoma (PTC) accounts for 80–85% of TC cases [ 4 ] . Epidemiological studies suggest a bidirectional increased risk of second primary cancers between BC and TC patients [ 5 ] , though the underlying mechanisms remain controversial [ 6 ] . This study aims to explore potential biomarkers linking BC and TC through bioinformatics analysis of public databases and retrospective clinical data. KRT19, a cytokeratin highly expressed in epithelial tumors, was identified as a candidate gene. Its overexpression in both cancers and association with estrogen response pathways suggest a shared molecular mechanism. 1. Materials and methods 1.1 Data acquisition The gene expression datasets were obtained from public databases including the GEO database (GSE70947 [ 7 ] with platform GPL13607 Agilent-028004 SurePrint G3 Human GE 8×60K Microarray, comprising 148 breast tumor samples and 148 normal samples, and GSE3467 [ 8 ] with platform GPL570 Affymetrix Human Genome U133 Plus 2.0 Array, containing 9 papillary thyroid carcinoma (PTC) samples and 9 normal samples), the TCGA database (providing BRCA and THCA KRT19 mRNA-seq data), and the UCSC Xena database (offering mRNA data and complete clinical information for 1,162 breast cancer patients and 558 thyroid cancer patients, with clinicopathological characteristics covering sex, age, TNM stage, tumor size, lymph node metastasis, distant metastasis, and ER/PR/HER-2 status). 1.2 Clinical cohort The study cohort consisted of 92 female patients with concurrent breast cancer and thyroid cancer who were treated at Zhongshan People's Hospital between January 2008 and July 2023 (age range: 30–68 years; mean age: 48.00 ± 8.35 years). Control group 1 included 100 randomly selected breast cancer-only patients (age range: 27–86 years; mean age: 53.02 ± 10.94 years) from our hospital's breast surgery department within the past two years, while control group 2 comprised 100 randomly selected thyroid cancer-only patients (age range: 24–75 years; mean age: 43.65 ± 11.34 years) from the thyroid surgery department during the same period. Breast cancer diagnoses followed the Breast Cancer Diagnosis and Treatment Guidelines (2022 edition) [ 9 ] , and thyroid cancer diagnoses adhered to the Thyroid Cancer Diagnosis and Treatment Guidelines (2022 edition) [ 10 ] . Inclusion criteria were: (1) female patients, (2) histopathological confirmation via surgery or biopsy, and (3) complete clinical records. Exclusion criteria included: (1) metastatic cancer or additional malignancies, and (2) severe comorbidities. The study protocol was approved by the Institutional Review Board of Zhongshan People's Hospital, and written informed consent was obtained from all participants or their legal representatives. 1.3 Statistical methods Data analysis was performed using R software (version 4.3.3) for processing GEO, TCGA, and UCSC Xena datasets. Visualization tools included ggplot2 for box plots and volcano plots, and gseaplot2 for gene set enrichment plots. Clinical data were analyzed using SPSS 26. Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean ± standard deviation (x̄ ± s) and compared using Student's t-test, while non-normally distributed data were expressed as median (25th, 75th percentiles) [M(P25, P75)] and analyzed via the Mann-Whitney U test. Categorical variables were reported as frequencies and compared using Pearson's chi-square test or continuity-corrected chi-square test for expected frequencies < 5. A two-tailed α level of 0.05 was applied, with P < 0.05 considered statistically significant. 1.4 Specimen acquisition Pathologists selected representative formalin-fixed paraffin-embedded tissue blocks based on diagnostic review and archival integrity. Given the rarity of dual-primary cancers and the extended collection period, specimens from dual-primary cases included either surgical resection or core needle biopsy samples, whereas control groups (single-primary breast or thyroid cancers) exclusively comprised surgical resection specimens to ensure consistency. 1.5 Main experimental reagents and instrumentsc 1.5.1 Experimental reagents Names of main experimental reagents Source KRT19 mAb SAB (48622-2) Horseradish peroxidase-conjugated Donkey Anti-Goat IgG IgG(H + L) Beyotime PBS Agilent technologies, Inc Xylene Guangdong Guanghua Sci-Tech Co., Ltd. DAB chromogenic substrate Agilent technologies, In Hematoxylin Agilent technologies, Inc 1.5.2 Instrumentsc Laboratory instruments and equipment Source Paraffin microtome Kelatai Slide Warming Cabinet Shanghai Pudong Rongfeng Scientific Instrument Co., Ltd. Slide Drying Cabinet Shanghai Boxun Industrial Co., Ltd. - Medical Equipment Factory Microscope OLYMPUS BX51 1.6 Experimental methods KRT19 protein expression in tumor tissues was detected using immunohistochemistry (IHC), strictly following the antibody manufacturer's instructions. Preliminary antibody optimization was performed using gastric cancer tissue as a positive control. Breast or thyroid cancer tissues served as internal controls, and PBS buffer replaced the primary antibody for negative controls. The working concentration for the KRT19 antibody was 1:100. 1.6.1 Staining procedure ①Sectioning;②Baking;③Deparaffinization and Hydration;④Antigen Retrieval;⑤Endogenous Peroxidase Blocking;⑥Primary Antibody Incubation;⑦Secondary Antibody Incubation;⑧DAB Development; ⑨Counterstaining and Mounting. 1.6.2 Result interpretation ER, PR, and HER-2 IHC results were interpreted according to relevant guidelines [ 11 , 12 ] . ER or PR positivity was defined as ≥ 1% tumor cell nuclear staining. HER-2 positivity was defined as IHC 3 + and/or IHC 2 + with confirmed gene amplification by fluorescence in situ hybridization (FISH). Hormone receptor (HR) positivity was defined as ER and/or PR positive. KRT19 results were assessed using the Immunoreactive Score (IRS) [ 13 ] : Positive staining was located on the cell membrane and/or cytoplasm. ① Staining intensity scores: No staining = 0, Light yellow = 1, Brownish-yellow = 2, Tan = 3 (Fig. 1.1 , 1.2). ② Percentage of positive cells scores: 0%=0; 1–25%=1; 26–50%=2; 51–75%=3; >75%=4. The final IRS was the product of intensity and percentage scores: 0 = Negative (-); 1–4 = Weakly Positive (+); 5–8 = Moderately Positive (++); 9–12 = Strongly Positive (+++). 2. Results 2.1 Identification of Representative Differentially Expressed Genes Common to Breast and Thyroid Cancers Differential expression analysis was performed to explore common expression mechanisms between breast and thyroid cancers. Using GSE70947, 399 DEGs were identified in breast cancer (201 up-regulated, 198 down-regulated; Fig. 2.1 A). In the GSE3467 dataset, 440 DEGs were identified in thyroid cancer (227 up-regulated, 213 down-regulated; Fig. 2.1 B). Common DEGs between breast and thyroid cancers are depicted in a Venn diagram (Fig. 2.1 C), yielding 20 common DEGs. Among these, 7 were up-regulated in both cancers (TACSTD2, KRT19, TYMS, COL1A1, CFB, TNFRSF12A, MDK), 10 were down-regulated in both (AVPR1A, CACNA2D1, AOX1, NCAM1, ANGPTL1, ALDH1A1, LIFR, ADH1B, GHR, SDPR), one was up-regulated in breast cancer but down-regulated in thyroid cancer (TFF3), and two were down-regulated in breast cancer but up-regulated in thyroid cancer (S100A1, TESC). A heatmap was generated to visualize the expression levels of these DEGs (Fig. 2.1 D). We focused further research on DEGs commonly high or low expressed in both cancers. Notably, KRT19 was significantly overexpressed in both breast and thyroid cancers (Fig. 2.1 E, F). ROC curve analysis evaluated the diagnostic value of KRT19, with AUCs of 0.861 for breast cancer and 0.821 for thyroid cancer (Fig. 2.1 G, H). 2.2 Relationship Between KRT19 Expression and Clinico-pathological Characteristics Analysis of BRCA and THCA data from UCSC Xena revealed that among 1162 breast cancer patients, KRT19 expression was significantly associated with age, distant metastasis, stage, and ER, PR, and HER-2 status (Table 2.1 ). Among 558 thyroid cancer patients, KRT19 expression was significantly associated with tumor size, lymph node metastasis, and clinical stage (Table 2.2 ). Table 2.1 Relationship between KRT19 expression and clinical phenotype of breast cancer patients Categorization High Expression Low Expression χ 2 value p-value Gender Female 673(98.39) 476(99.58) 3.600 0.058 Male 11(1.61) 2(0.42) Age <45 91(13.3) 88(18.41) 5.629 0.018 ≥ 45 593(86.7) 390(81.59) Tumor T1 177(25.88) 120(25.1) 1.273 0.866 T2 391(57.16) 286(59.83) T3 90(13.16) 54(11.3) T4 24(3.51) 17(3.56) TX 2(0.29) 1(0.21) Node N0 307(44.88) 237(49.58) 9.149 0.057 N1 228(33.33) 165(34.52) N2 77(11.26) 48(10.04) N3 56(8.19) 23(4.81) NX 16(2.34) 5(1.05) Metastasis M0 551(80.56) 431(90.17) 20.061 <0.001 M1 16(2.34) 7(1.46) MX 117(17.11) 40(8.37) Stage Stage I 115(16.81) 78(16.32) 11.534 0.021 Stage II 372(54.39) 296(61.92) Stage III 170(24.85) 97(20.29) Stage IV 16(2.34) 5(1.05) Stage X 11(1.61) 2(0.42) ER Indeterminate 1(0.15) 2(0.42) 46.919 <0.001 Missing 29(4.24) 30(6.28) Negative 100(14.62) 144(30.13) Positive 554(80.99) 302(63.18) PR Indeterminate 2(0.29) 2(0.42) 31.353 <0.001 Missing 29(4.24) 31(6.49) Negative 171(25) 185(38.7) Positive 482(70.47) 260(54.39) HER-2 Indeterminate 139(20.3) 56(11.71) 19.632 <0.001 Missing 122(17.84) 72(15.06) Negative 319(46.64) 271(56.69) Positive 104(15.2) 79(16.53) (Note: ER denotes estrogen receptor, PR denotes progesterone receptor, HER-2 denotes human epidermal growth factor receptor 2, and the designation 'X' in the TNM staging system represents 'cannot be assessed'). Table 2.2 Relationship between KRT19 expression and clinical phenotype of thyroid cancer patients Categorization High Expression Low Expression χ 2 value p-value Gender Female 265(73.41) 141(71.57) 0.216 0.642 Male 96(26.59) 56(28.43) Age <45 168(46.54) 89(45.18) 0.095 0.758 ≥ 45 193(53.46) 108(54.82) Tumor T1 93(25.76) 59(29.95) 11.651 0.020 T2 108(29.92) 78(39.59) T3 137(37.95) 54(27.41) T4 21(5.82) 6(3.05) TX 2(0.55) 0(0) Node N0 134(37.12) 123(62.44) 54.302 <0.001 N1 201(55.68) 46(23.35) NX 26(7.2) 28(14.21) Metastasis M0 216(59.83) 106(53.81) 3.188 0.203 M1 5(1.39) 6(3.05) MX 140 (38.78) 85(43.15) Stage Stage I 202(55.96) 114(57.87) 29.376 <0.001 Stage II 22(6.09) 37(18.78) Stage III 89(24.65) 35(17.77) Stage IV 48(13.3) 11(5.58) (Note: In the TNM staging system, 'X' designates that the category cannot be assessed). 2.3 Gene Set Enrichment Analysis (GSEA) GSEA was performed to identify pathways associated with KRT19 upregulation in breast and thyroid cancers (Fig. 2.2 ). The results indicated significant enrichment of both the early and late estrogen response pathways in both breast cancer (Fig. 2.2 A, B) and thyroid cancer (Fig. 2.2 C, D) with high KRT19 expression. (Note: A significant enrichment was defined as Nominal p-value < 0.05, FDR q-value < 0.25, and |NES| ≥ 1). 2.4 Comparison of General and Pathological Characteristics Between the Dual-Primary Cancer Group and the Breast Cancer-Alone Group Compared to the breast cancer-alone group, the dual-primary cancer group had a significantly higher proportion of premenopausal patients (P < 0.001), larger tumor size (P = 0.005), and higher rates of positive ER (P = 0.049) and PR (P = 0.041) expression. No statistically significant differences were found between the two groups regarding pathological type of breast cancer (P = 0.070), lymph node metastasis status (P = 0.725), HER-2 expression level (P = 0.773), molecular subtype (P = 0.081), or KRT19 expression level (P = 0.069). See Table 2.3 . Table 2.3 Comparison of general and pathological characteristics between the double primary cancer group and the breast cancer group [n(%)] Categorization Double Primary Cancers Group (n = 92) Breast Cancer Group (n = 100) χ 2 value p-value Age at Diagnosis <50 62 (67) 38 (38) 16.586 <0.001 ≥ 50 30 (33) 62 (62) Menopausal Status Premenopausal 14 (15) 57 (57) 35.895 <0.001 Postmenopausal 78 (85) 43 (43) Pathological Type Invasive Carcinoma 72 (80) 88 (88) 3.272 0.070 Non-invasive Carcinoma 20 (20) 12 (12) Tumor Diameter <2cm 21 (23) 42 (42) 7.991 0.005 ≥ 2cm 71 (77) 58 (58) Lymph Node Metastasis Yes 26 (28) 26 (26) 0.124 0.725 No 66 (72) 74 (74) ER Positive 70 (76) 63 (63) 3.855 0.049 Negative 22 (24) 37 (37) PR Positive 68 (74) 60 (60) 4.174 0.041 Negative 24 (26) 40 (40) HER-2 Positive 16 (17) 19 (19) 0.083 0.773 Negative 76 (83) 81 (81) Molecular Subtype Luminal 72 (78) 67 (67) 3.040 0.081 HER-2 + NAC 20 (22) 33 (33) KRT19 Strongly Positive 41 (45) 27 (27) 7.101 0.069 Moderately Positive 33 (35) 45 (45) Weakly Positive 7 (8) 14 (14) Negative 11 (12) 14 (14) (Note: ER denotes estrogen receptor, PR denotes progesterone receptor, and HER-2 denotes human epidermal growth factor receptor 2). 2.5 Comparison of General and Pathological Characteristics Between the Dual-Primary Cancer Group and the Thyroid Cancer-Alone Group Compared to the thyroid cancer-alone group, thyroid cancers in the dual-primary cancer group were more likely to be bilateral (P = 0.035) and exhibited higher KRT19 expression levels (P = 0.008). No statistically significant differences were observed in age at diagnosis (P = 0.516), menopausal status (P = 0.138), pathological type (P = 0.933), proportion of microcarcinomas (≤ 1cm) (P = 0.577), or lymph node metastasis status (P = 0.102). See Table 2.4 . Compared to the thyroid cancer-alone group, the dual-primary cancer group had significantly higher serum levels of triiodothyronine (T3) (P = 0.010) and thyroxine (T4) (P = 0.040). No significant differences were found in serum levels of free T3 (FT3) (P = 0.125), free T4 (FT4) (P = 0.242), thyroid-stimulating hormone (TSH) (P = 0.068), thyroglobulin antibody (TGAb) (P = 0.606), or thyroid peroxidase antibody (TPOAb) (P = 0.704). See Table 2.5 . Table 2.4 Comparison of general and pathological characteristics between the double primary cancer group and the thyroid cancer group[n(%)] Thyroid Cancer Double Primary Cancers Group (n = 92) Single Cancer Group (n = 100) χ 2 value p-value Age at Diagnosis <55 72 (78) 82 (82) 0.422 0.516 ≥ 55 20 (22) 18 (18) Menopausal Status Premenopausal 36 (39) 29 (29) 2.196 0.138 Postmenopausal 56 (61) 71 (71) Pathological Type Papillary Carcinoma 90 (98) 98 (98) 0.070 0.933 Non-Papillary Carcinoma 2 (2) 2 (2) Microcarcinoma (≤ 1cm) Yes 60 (65) 69 (69) 0.311 0.577 No 32 (35) 31 (31) Lymph Node Metastasis Yes 38 (41) 30 (30) 2.677 0.102 No 54 (59) 70 (70) Unilateral or Bilateral Unilateral 74 (80) 91 (91) 4.426 0.035 Bilateral 18 (20) 9 (9) KRT19 Strongly Positive 27 (29) 11 (11) 11.948 0.008 Moderately Positive 50 (54) 66 (66) Weakly Positive 8 (9) 17 (17) Negative 7 (8) 6 (6) Table 2.5 Comparison of thyroid hormone and antigen antibody between the double primary cancer group and the thyroid cancer group M ( P 25 , P 75 ) Thyroid Hormones and Thyroid Antibodies Double Primary Cancers Group (n = 92) Thyroid Cancer Group (n = 100) z-value p-value FT3(pmol/L) 4.73 (4.43–5.02) 4.91 (4.63–5.17) -1.533 0.125 FT4(pmol/L) 16.16 (14.42–17.52) 16.27 (15.00-18.08) -1.171 0.242 T3(nmol/L) 1.71 (1.48–1.87) 1.52 (1.33–1.68) -2.581 0.010 T4(nmol/L) 107.80(94.55-127.38) 102.30 (85.30-115.70) -2.052 0.040 TSH(uIU/ml) 1.24 (0.82–2.20) 1.57 (1.08–2.26) -1.823 0.068 TGAb(U/ml) 20.60(15.00-160.30) 15.60 (15.00-78.10) -0.516 0.606 TPOAb(U/ml) 47.35 (28.00-496.90) 39.50 (28.00-226.20) -0.380 0.704 (Note: FT3 denotes free triiodothyronine, FT4 denotes free thyroxine, T3 denotes triiodothyronine, T4 denotes thyroxine, TSH denotes thyroid-stimulating hormone, TGAb denotes thyroglobulin antibody, and TPOAb denotes thyroid peroxidase antibody). 3. Discussion The relationship between breast cancer and thyroid cancer is complex. Previous studies have indicated that ER and PR expression is higher in patients with co-occurring breast and thyroid cancers compared to those with breast cancer alone [ 14 , 15 ] , suggesting a potential common molecular pathogenesis for these dual primary cancers. Our study aimed to explore the clinicopathological correlations in the development of dual primary cancers and identify potential biomarkers linking breast and thyroid cancer. In our initial analysis, we identified 20 common DEGs. Abdelaziz LA et al. [ 16 ] detected CK19 (KRT19) levels in peripheral blood of breast cancer patients by flow cytometry and assessed OCT4 expression in tissues by IHC, finding that positive expression of both was negatively correlated with patient survival. Gao et al. [ 17 ] revealed that the disease-free survival rate was lower in thyroid cancer patients with high KRT19 mRNA expression compared to those with low expression. Our study further demonstrates that KRT19 is highly expressed in both breast and thyroid cancers, suggesting its potential role as a common pathogenic gene, prompting further investigation. Keratin 19 (KRT19), a gene located on chromosome 17q21.2, encodes the Cytokeratin-19 (CK19) protein, which is abundantly expressed in a wide range of epithelial tumors, including those of the pancreas, colorectum, esophagus, stomach, and breast [ 18 , 19 ] . Notably, KRT19/CK19 expression is reported to be higher in ER-positive (ER+) than in ER-negative (ER-) breast cancers [ 20 ] . The mechanistic link between estrogen receptor signaling and KRT19 is underscored by our current findings, which demonstrate upregulated KRT19 expression in ER + breast cancer. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed that KRT19 overexpression significantly enriches both early and late estrogen response pathways in breast and thyroid cancers. This aligns with established literature wherein ERα enhances tumor cell migration and metastasis by upregulating MMP-9 and downregulating E-cadherin [ 21 , 22 ] , and estrogen activates key signaling pathways like MAPK and PI3K, as well as Matrix Metalloproteinases (MMPs), via membrane-associated estrogen receptors [ 23 , 24 ] . Supporting this axis, studies have shown that estrogen promotes proliferation, migration, and invasion in papillary thyroid carcinoma through the ERα/KRT19 signaling pathway [ 25 ] . Collectively, these findings suggest that KRT19, potentiated by estrogenic signaling, may drive the progression of both breast and thyroid cancers, thereby potentially elevating the risk of developing second primary tumors in patients with either malignancy. As two of the most common malignant diseases threatening women's health globally, breast cancer and thyroid cancer are increasingly affecting younger populations, posing serious risks. Both the breast and thyroid are target organs regulated by the hypothalamic-pituitary axis. Research on the relationship between these two cancers dates back to 1896. Current domestic and international studies suggest a connection. Sadetzki S et al. [ 26 ] reported in 2003 that the likelihood of breast cancer patients developing metachronous thyroid cancer was 1.34%, and vice versa was 1.07%. In our cohort, 86 patients developed thyroid cancer after breast cancer, while 6 developed breast cancer after thyroid cancer. The higher number of breast cancer preceding thyroid cancer cases aligns with findings by Ji Wenzhong et al. [ 27 ] , possibly because breast cancer often progresses faster with more apparent early symptoms, facilitating detection. This highlights the importance of routine thyroid screening in breast cancer patients for early detection of thyroid abnormalities. Pathological classification of breast cancer into invasive and non-invasive types based on the extent of invasion and metastatic risk correlates with prognosis, with non-invasive carcinoma having the best prognosis [ 28 ] . Most breast cancers are invasive. In our study, invasive breast cancer accounted for 80% and 88% in the dual-primary and breast cancer-alone groups, respectively, consistent with previous reports [ 29 ] . Yang Haibo et al. [ 30 ] found a higher proportion of premenopausal patients among those with breast cancer combined with thyroid cancer compared to those with breast cancer alone, which aligns with our results. Furthermore, patients in the dual-primary group were diagnosed at a younger age and had larger breast tumors (> 2cm), possibly due to the more aggressive nature of breast cancer compared to thyroid cancer, underscoring the need for thyroid screening in young breast cancer patients. Our study showed that ER and PR were more frequently positive in breast cancer patients with concurrent thyroid cancer compared to those with breast cancer alone, consistent with previous research [ 31 ] . ER is a protein molecule located on the cell membrane, in the cytoplasm, and nucleus that specifically binds estrogen and regulates growth and differentiation in the reproductive system. PR is a product induced by estrogen action and can enhance the response of sex hormones via ER. Both play synergistic roles in cell growth and development. ER and PR status are important factors for evaluating breast cancer prognosis [ 32 ] ; positive expression is associated with lower proliferation and invasion indices, relatively slower clinical progression, and sensitivity to endocrine therapy. Earlier studies support the expression of ER and PR in thyroid tissue [ 33 , 34 ] . As an endocrine gland, thyroid malignancies also exhibit hormone dependence. Han et al. [ 35 ] found that ER and PR can promote the proliferation of thyroid tumor cells by upregulating the cell cycle. Other studies suggest estrogen can regulate breast carcinogenesis by modulating DNA methylation, with enhancers binding ERα, FOXA1, and GATA3 showing demethylation in ER + breast cancer [ 36 ] . DNA methylation is also implicated in thyroid carcinogenesis [ 37 ] . It is plausible that estrogen acts via ER to promote the development of second primary thyroid cancer in breast cancer patients through DNA demethylation, though the precise mechanisms require further investigation. Zhao Yuanyuan et al. [ 38 ] found that patients with thyroid cancer combined with breast cancer were more often postmenopausal, possibly due to declining gonadal function and deficient hormone levels. In our study, compared to the thyroid cancer-alone group, a higher proportion of patients in the dual-primary group were postmenopausal ( P = 0.138). Although not statistically significant, the P-value close to 0.05 suggests that breast examination should not be neglected in older thyroid cancer patients. Additionally, patients with concurrent breast cancer were more likely to have bilateral thyroid cancer, possibly related to the malignant progression of breast cancer promoting thyroid tumor development through endocrine mechanisms. Among thyroid hormones, thyroxine (T4) is entirely secreted by the thyroid gland, while most triiodothyronine (T3) is produced peripherally from T4 deiodination. A study by Moretto FC [ 39 ] found that when T3 binds to its receptor in breast cancer cells, it activates the PI3K pathway, increasing the expression of important proteins like TGFα, a transforming growth factor that promotes cancer cell growth. Other studies indicate that thyroid hormones can mimic estrogen effects to promote breast cell growth in vitro [ 40 , 41 ] . Our results showed significantly higher T3 and T4 levels in the dual-primary group compared to the thyroid cancer-alone group, suggesting that high T3/T4 levels increase the risk of breast cancer following thyroid cancer. T3 and T4 exert mitogenic effects in thyroid cancer cells by binding to the membrane receptor αvβ3 integrin [ 42 ] , which is expressed in various cancers, including breast and thyroid cancer. It is hypothesized that thyroid hormones might influence breast cancer via this receptor connection, potentially supporting the use of TSH suppression therapy in thyroid cancer patients to mitigate breast cancer progression in dual-primary cases. In our study, KRT19 expression was higher in the dual-primary group compared to the breast cancer-alone group, though the difference was not statistically significant (P = 0.069). However, KRT19 expression was significantly higher in the thyroid cancer components of the dual-primary group compared to thyroid cancer alone (P = 0.008). The lack of statistical significance in the breast cancer comparison, despite a trend (P = 0.069), might be attributed to the relatively small sample size of dual cancer cases. This study has limitations. The sample size was small, and the long interval between primary cancer diagnoses and limited tissue preservation may have affected immunohistochemistry quality. Additionally, follow-up data and manual verification of results were lacking. 4. Conclusion 1. KRT19 is a potential biomarker influencing the development of thyroid cancer following breast cancer, or breast cancer following thyroid cancer. It likely affects the pathogenesis of dual primary cancers through the estrogen response pathway. 2. Breast cancer patients with positive ER and PR expression are more susceptible to developing thyroid cancer. KRT19 expression is associated with positive hormone receptor status, particularly ER positivity. 3. High T3 and T4 levels in thyroid cancer patients may indicate an increased risk of concurrent breast cancer. 5. Summary In conclusion, a pathogenic correlation exists between breast cancer and thyroid cancer. KRT19 expression likely promotes tumor development in patients with either cancer through the early and late estrogen response pathways. Clinically, breast cancer patients who are ER-positive, PR-positive, younger, premenopausal, or have larger tumors should prioritize routine thyroid examination. Conversely, thyroid cancer patients with high T3 and T4 levels should not overlook the potential for breast malignancy. KRT19 may serve as a potential biomarker for predicting the risk of thyroid cancer in breast cancer patients, or breast cancer in thyroid cancer patients. Declarations 1. Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki. The research protocol and related procedures involving human participants were reviewed and approved by the Clinical Research and Experimental Animal Ethics Committee of Zhongshan People’s Hospital (Project No.: 2025-128). Written informed consent was obtained from all individual participants included in the study. 2. Consent for publication. The paper titled “Correlation analysis and exploration of potential biomarkers in patients with breast cancer combined with thyroid cancer” is authored by Xinran Cai, Juan Wang, Shihui Ma, Yingzhi Chen, and Shijun Sun from Zhongshan People’s Hospital. The project associated with this manuscript has undergone ethical review and has been approved by the Clinical Research and Experimental Animal Ethics Committee of Zhongshan People’s Hospital. The Committee hereby grants approval for its publication. 2. Consent for publication Not applicable. 3. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 4. Competing interests The authors declare that they have no competing interests. 5. Funding None declared. 6. Authors' contributions X.C. and J.W. contributed equally to this work. X.C. and J.W. contributed to conceptualization, methodology, formal analysis, investigation, data curation, and writing—original draft preparation. S.S. and Y.C. contributed to resources, specimen acquisition, pathological evaluation, and methodology. S.M. contributed to conceptualization, resources, writing—review and editing, supervision, project administration, and funding acquisition. All authors read and approved the final manuscript. 7. Acknowledgements I extend my sincere gratitude to all those who have assisted me. References Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263. Chinese Anti-Cancer Association Committee of Breast Cancer Society, Chinese Society of Oncology Breast Cancer Group, Shao Zhimin. Guidelines and Standards for Breast Cancer Diagnosis and Treatment by Chinese Anti-Cancer Association (2024 Edition)[J]. 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Proc Natl Acad Sci U S A. 2005 Dec 27;102(52):19075-80. Guidelines for Breast Cancer Diagnosis and Treatment (2022 Edition)[J]. Chinese Journal of Rational Drug Use, 2022, 19(10): 1-26. Guidelines for Thyroid Cancer Diagnosis and Treatment (2022 Edition)[J]. Chinese Journal of Practical Surgery, 2022, 42(12): 1343-1357+1363. Hammond M E, Hayes D F, Dowsett M, et al. American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer [J]. J Clin Oncol, 2010, 28(16): 2784-2795. Wolff A C, Hammond M E H, Allison K H, et al. HER2 Testing in Breast Cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline Focused Update Summary [J]. J Oncol Pract, 2018, 14(7): 437-441. Remmele W, Stegner HE. 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Eur J Endocrinol. 2015 Jul;173(1):29-36. Haque MM, Desai KV. Pathways to Endocrine Therapy Resistance in Breast Cancer. Front Endocrinol (Lausanne). 2019 Aug 21;10:573. Song ZM, Wang YD, Chai F, et al. Estrogen enhances the proliferation, migration, and invasion of papillary thyroid carcinoma via the ERα/KRT19 signaling axis. J Endocrinol Invest. 2024 Oct 25. Sadetzki S, Calderon-Margalit R, Peretz C, Novikov I, Barchana M, Papa MZ. Second primary breast and thyroid cancers (Israel). Cancer Causes Control. 2003 May;14(4):367-75. Ji Wenzhong, Chen Jianzhong. Clinical analysis of mammary-thyroid multiple primary carcinoma[J]. Henan Journal of Surgery, 2017, 23(03): 11-14. Li Shuang, Fan Hongmin, Xiao Feifei, et al. Relationship between different molecular subtypes, clinicopathological characteristics and prognosis of postoperative breast cancer patients[J]. Chinese Journal of Clinical and Experimental Pathology, 2016, 32(01): 39-44. Zheng Xixi, Jia Yongsheng, Shi Yehui, et al. Clinicopathological characteristics and prognosis analysis of multiple primary cancers in breast cancer[J]. Chinese Journal of Clinical Oncology, 2017, 44(05): 219-223. Yang Haibo, Gao Jinnan, Song Wanzhi. Analysis of clinicopathological characteristics in patients with breast cancer alone versus breast cancer combined with thyroid cancer[J]. Chinese Journal of Oncology, 2019, 41(8): 4. Shang Jiangfeng, Wu Danping, Wang Bo, et al. Analysis of clinicopathological characteristics in patients with breast cancer combined with thyroid cancer[J]. Journal of Guangxi Medical University, 2019, 36(06): 919-923. Chen Qing, Meng Gang, Huang Wen. Relationship between ERα and PR expression and prognosis in breast cancer, and factors affecting positive rates[J]. Chinese Journal of Clinical and Experimental Pathology, 2016, 32(01): 13-18. Duan Duwen, Zhou Jing, Hu Ping. Expression and clinical significance of ER, PR, C-erbB-2, and p53 in 87 cases of thyroid carcinoma[J]. Journal of Modern Oncology, 2008, 16(12): 2075-2077. Cheng Kelun. Expression and significance of p33ING1, ER, PR, and c-erbB-2 in thyroid carcinoma[J]. Tianjin Medical Journal, 2009, 37(7): 541-543. Han Yuping, Wang Tongtong, Wang Ting, et al. Research progress on the correlation between ER, PR, their respective antagonists, and thyroid cancer[J]. Labeled Immunoassays and Clinical Medicine, 2016, 23(5): 571-574. Fleischer T, Tekpli X, Mathelier A, et al. DNA methylation at enhancers identifies distinct breast cancer lineages. Nat Commun. 2017 Nov 9;8(1):1379. Cao Yiming, Zhu Yongxue. Research progress on DNA methylation in thyroid cancer[J]. China Oncology, 2017(4): 304-311. Zhao Yuanyuan, He Qi, Ji Feihong, et al. Clinicopathological characteristics of patients with papillary thyroid carcinoma and breast cancer[J]. Journal of Jiangsu University (Medicine Edition), 2022, 32(06): 461-466. Moretto FC, De Sibio MT, Luvizon AC, et al. Triiodothyronine (T3) induces HIF1A and TGFA expression in MCF7 cells by activating PI3K. Life Sci. 2016 Jun 1;154:52-7. Ortega-Olvera C, Ulloa-Aguirre A, Ángeles-Llerenas A, et al. Thyroid hormones and breast cancer association according to menopausal status and body mass index. Breast Cancer Res. 2018 Aug 9;20(1):94. Liang Jiazheng, Feng Ziyu, Song Xudong, et al. Research progress on the relationship between thyroid diseases and breast cancer[J]. Anhui Medical and Pharmaceutical Journal, 2022, 26(06): 1068-1073.. Tobi D, Krashin E, Davis PJ, et al. Three-Dimensional Modeling of Thyroid Hormone Metabolites Binding to the Cancer-Relevant αvβ3 Integrin: In-Silico Based Study. Front Endocrinol (Lausanne). 2022 May 27; 13:895240. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8240986","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589484076,"identity":"b0d24dbc-3ccf-43b1-9200-2fb20dd794d5","order_by":0,"name":"Xinran Cai","email":"","orcid":"","institution":"Tumor Branch Court of Zhongshan City People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinran","middleName":"","lastName":"Cai","suffix":""},{"id":589484077,"identity":"2bd0d4ac-1229-468d-be8a-a4faf387ed82","order_by":1,"name":"Juan Wang","email":"","orcid":"","institution":"Tumor Branch Court of Zhongshan City People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Wang","suffix":""},{"id":589484078,"identity":"7ca53c95-e151-4c93-967d-2d495c639144","order_by":2,"name":"Shihui Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDACCcY2BgYDGx5+9sbGhx9I0JImI9lzuNlYgjgtDGxA8pCNwY30NgEeYnTIz25ue8xTcICH4ebDNqB+OzndBgJaDO4cbDfmMbjDwzg7se1BAUOysdkBQlokEtukeQye8TBLJ7YbSDAcSNxGSIv8DLCWwzxskgfbJHiI0cJwA6qFBxh0xGkxAGqRnGOQxiPBkwgMZAMi/CI/I/2ZxJs/Nvb2x48/fPihwk6OoBZ0S0lTPgpGwSgYBaMABwAABT4+6Px9/NkAAAAASUVORK5CYII=","orcid":"","institution":"Tumor Branch Court of Zhongshan City People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shihui","middleName":"","lastName":"Ma","suffix":""},{"id":589484079,"identity":"370ec0bf-7591-474e-a0bb-acc7cf4c63c5","order_by":3,"name":"Shijun Sun","email":"","orcid":"","institution":"Zhongshan City People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shijun","middleName":"","lastName":"Sun","suffix":""},{"id":589484080,"identity":"ad579e6f-a3c1-4e19-be99-4fe644d900ae","order_by":4,"name":"Yingzhi Chen","email":"","orcid":"","institution":"Zhongshan City People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingzhi","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-11-30 09:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8240986/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8240986/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102962596,"identity":"76691b1a-9260-43e6-b0c9-eb051bc5b22c","added_by":"auto","created_at":"2026-02-19 04:10:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1376083,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1.1 \u0026nbsp;Staining intensity of KRT19 in breast cancer result A: light brown; B: brownish-yellow; C: Tan (IHC Envision two-step method)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8240986/v1/eaf2b9b7f2fdee3c25f8b1fd.png"},{"id":102759263,"identity":"6ea3ba54-b864-455c-b07a-6d88d8e89fe9","added_by":"auto","created_at":"2026-02-16 10:13:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1492306,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1.2 \u0026nbsp;Staining intensity of KRT19 in breast cancer result A: light brown; B: brownish-yellow; C: Tan (IHC Envision two-step method)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8240986/v1/483b8e5b950bb7cf74ad69ee.png"},{"id":102759264,"identity":"33af5317-4d7b-487b-bf6c-2591fea3b331","added_by":"auto","created_at":"2026-02-16 10:13:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185749,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2.1 \u0026nbsp;(A) Volcanogram of DEGs between breast cancer and normal tissue (GSE70947). (B) Volcanogram of DEGs between thyroid cancer and normal tissue (GSE3467). (C) Venn diagram of all DEGs for both datasets. (D) Expression levels of DEGs in both datasets. (E) Differences in the expression of KRT19 in breast cancer and normal tissues. (F) Differences in the expression of KRT19 in thyroid cancer and normal tissues. (G) ROC curve of KRT19 upregulation in breast cancer. (H) ROC curve of KRT19 upregulation in thyroid cancer.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8240986/v1/8b652874a10367ce72f59da0.png"},{"id":102759266,"identity":"688a0dcc-bdeb-4387-bda8-9c35ec60d15d","added_by":"auto","created_at":"2026-02-16 10:13:17","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":890685,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2.2 \u0026nbsp;In breast cancer, KRT19 overexpression was significantly associated with \"early estrogen response\" (A) and \"late estrogen response\" (B). In thyroid cancer, KRT19 overexpression was significantly associated with \"early estrogen response\" (C) and \"late estrogen response\" (D).\u003c/p\u003e\n\u003cp\u003e(Note: A significant enrichment was defined as Nominal p-value \u0026lt; 0.05, FDR q-value \u0026lt; 0.25, and |NES| ≥ 1).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8240986/v1/8a80d368b3f9d90381eb6345.jpeg"},{"id":106414519,"identity":"f3318d72-f0ac-436c-be91-ada3ddc6cf5b","added_by":"auto","created_at":"2026-04-08 10:09:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4806979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8240986/v1/2bcf1143-6247-4df3-adc1-485da81cc4ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation analysis and exploration of potential biomarkers in patients with breast cancer combined with thyroid cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BC) and thyroid cancer (TC) are among the most prevalent malignancies affecting women globally. Recent data indicate that BC is the second most common cancer worldwide, while TC ranks seventh in incidence\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. BC is classified into molecular subtypes, with hormone receptor-positive (HR+) tumors (Luminal A and B) constituting approximately 75% of cases\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Similarly, papillary thyroid carcinoma (PTC) accounts for 80\u0026ndash;85% of TC cases\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Epidemiological studies suggest a bidirectional increased risk of second primary cancers between BC and TC patients\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, though the underlying mechanisms remain controversial\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aims to explore potential biomarkers linking BC and TC through bioinformatics analysis of public databases and retrospective clinical data. KRT19, a cytokeratin highly expressed in epithelial tumors, was identified as a candidate gene. Its overexpression in both cancers and association with estrogen response pathways suggest a shared molecular mechanism.\u003c/p\u003e"},{"header":"1. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Data acquisition\u003c/h2\u003e \u003cp\u003eThe gene expression datasets were obtained from public databases including the GEO database (GSE70947\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e with platform GPL13607 Agilent-028004 SurePrint G3 Human GE 8\u0026times;60K Microarray, comprising 148 breast tumor samples and 148 normal samples, and GSE3467 \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e with platform GPL570 Affymetrix Human Genome U133 Plus 2.0 Array, containing 9 papillary thyroid carcinoma (PTC) samples and 9 normal samples), the TCGA database (providing BRCA and THCA KRT19 mRNA-seq data), and the UCSC Xena database (offering mRNA data and complete clinical information for 1,162 breast cancer patients and 558 thyroid cancer patients, with clinicopathological characteristics covering sex, age, TNM stage, tumor size, lymph node metastasis, distant metastasis, and ER/PR/HER-2 status).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Clinical cohort\u003c/h2\u003e \u003cp\u003eThe study cohort consisted of 92 female patients with concurrent breast cancer and thyroid cancer who were treated at Zhongshan People's Hospital between January 2008 and July 2023 (age range: 30\u0026ndash;68 years; mean age: 48.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.35 years). Control group 1 included 100 randomly selected breast cancer-only patients (age range: 27\u0026ndash;86 years; mean age: 53.02\u0026thinsp;\u0026plusmn;\u0026thinsp;10.94 years) from our hospital's breast surgery department within the past two years, while control group 2 comprised 100 randomly selected thyroid cancer-only patients (age range: 24\u0026ndash;75 years; mean age: 43.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.34 years) from the thyroid surgery department during the same period. Breast cancer diagnoses followed the Breast Cancer Diagnosis and Treatment Guidelines (2022 edition)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, and thyroid cancer diagnoses adhered to the Thyroid Cancer Diagnosis and Treatment Guidelines (2022 edition)\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Inclusion criteria were: (1) female patients, (2) histopathological confirmation via surgery or biopsy, and (3) complete clinical records. Exclusion criteria included: (1) metastatic cancer or additional malignancies, and (2) severe comorbidities. The study protocol was approved by the Institutional Review Board of Zhongshan People's Hospital, and written informed consent was obtained from all participants or their legal representatives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Statistical methods\u003c/h2\u003e \u003cp\u003eData analysis was performed using R software (version 4.3.3) for processing GEO, TCGA, and UCSC Xena datasets. Visualization tools included ggplot2 for box plots and volcano plots, and gseaplot2 for gene set enrichment plots. Clinical data were analyzed using SPSS 26. Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s) and compared using Student's t-test, while non-normally distributed data were expressed as median (25th, 75th percentiles) [M(P25, P75)] and analyzed via the Mann-Whitney U test. Categorical variables were reported as frequencies and compared using Pearson's chi-square test or continuity-corrected chi-square test for expected frequencies\u0026thinsp;\u0026lt;\u0026thinsp;5. A two-tailed α level of 0.05 was applied, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Specimen acquisition\u003c/h2\u003e \u003cp\u003ePathologists selected representative formalin-fixed paraffin-embedded tissue blocks based on diagnostic review and archival integrity. Given the rarity of dual-primary cancers and the extended collection period, specimens from dual-primary cases included either surgical resection or core needle biopsy samples, whereas control groups (single-primary breast or thyroid cancers) exclusively comprised surgical resection specimens to ensure consistency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Main experimental reagents and instrumentsc\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e1.5.1 Experimental reagents\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNames of main experimental reagents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRT19 mAb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSAB (48622-2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorseradish peroxidase-conjugated Donkey Anti-Goat IgG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgG(H\u0026thinsp;+\u0026thinsp;L) Beyotime\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgilent technologies, Inc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXylene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuangdong Guanghua Sci-Tech Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAB chromogenic substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgilent technologies, In\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematoxylin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgilent technologies, Inc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e1.5.2 Instrumentsc\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory instruments and equipment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaffin microtome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKelatai\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlide Warming Cabinet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShanghai Pudong Rongfeng Scientific Instrument Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlide Drying Cabinet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShanghai Boxun Industrial Co., Ltd. - Medical Equipment Factory\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOLYMPUS BX51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1.6 Experimental methods\u003c/h2\u003e \u003cp\u003eKRT19 protein expression in tumor tissues was detected using immunohistochemistry (IHC), strictly following the antibody manufacturer's instructions. Preliminary antibody optimization was performed using gastric cancer tissue as a positive control. Breast or thyroid cancer tissues served as internal controls, and PBS buffer replaced the primary antibody for negative controls. The working concentration for the KRT19 antibody was 1:100.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e1.6.1 Staining procedure\u003c/h2\u003e \u003cp\u003e①Sectioning;②Baking;③Deparaffinization and Hydration;④Antigen Retrieval;⑤Endogenous Peroxidase Blocking;⑥Primary Antibody Incubation;⑦Secondary Antibody Incubation;⑧DAB Development; ⑨Counterstaining and Mounting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e1.6.2 Result interpretation\u003c/h2\u003e \u003cp\u003eER, PR, and HER-2 IHC results were interpreted according to relevant guidelines\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. ER or PR positivity was defined as \u0026ge;\u0026thinsp;1% tumor cell nuclear staining. HER-2 positivity was defined as IHC 3\u0026thinsp;+\u0026thinsp;and/or IHC 2\u0026thinsp;+\u0026thinsp;with confirmed gene amplification by fluorescence in situ hybridization (FISH). Hormone receptor (HR) positivity was defined as ER and/or PR positive.\u003c/p\u003e \u003cp\u003eKRT19 results were assessed using the Immunoreactive Score (IRS)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e: Positive staining was located on the cell membrane and/or cytoplasm. ① Staining intensity scores: No staining\u0026thinsp;=\u0026thinsp;0, Light yellow\u0026thinsp;=\u0026thinsp;1, Brownish-yellow\u0026thinsp;=\u0026thinsp;2, Tan\u0026thinsp;=\u0026thinsp;3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1.1\u003c/span\u003e, 1.2). ② Percentage of positive cells scores: 0%=0; 1\u0026ndash;25%=1; 26\u0026ndash;50%=2; 51\u0026ndash;75%=3; \u0026gt;75%=4. The final IRS was the product of intensity and percentage scores: 0\u0026thinsp;=\u0026thinsp;Negative (-); 1\u0026ndash;4\u0026thinsp;=\u0026thinsp;Weakly Positive (+); 5\u0026ndash;8\u0026thinsp;=\u0026thinsp;Moderately Positive (++); 9\u0026ndash;12\u0026thinsp;=\u0026thinsp;Strongly Positive (+++).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Identification of Representative Differentially Expressed Genes Common to Breast and Thyroid Cancers\u003c/h2\u003e \u003cp\u003eDifferential expression analysis was performed to explore common expression mechanisms between breast and thyroid cancers. Using GSE70947, 399 DEGs were identified in breast cancer (201 up-regulated, 198 down-regulated; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003eA). In the GSE3467 dataset, 440 DEGs were identified in thyroid cancer (227 up-regulated, 213 down-regulated; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003eB). Common DEGs between breast and thyroid cancers are depicted in a Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003eC), yielding 20 common DEGs. Among these, 7 were up-regulated in both cancers (TACSTD2, KRT19, TYMS, COL1A1, CFB, TNFRSF12A, MDK), 10 were down-regulated in both (AVPR1A, CACNA2D1, AOX1, NCAM1, ANGPTL1, ALDH1A1, LIFR, ADH1B, GHR, SDPR), one was up-regulated in breast cancer but down-regulated in thyroid cancer (TFF3), and two were down-regulated in breast cancer but up-regulated in thyroid cancer (S100A1, TESC). A heatmap was generated to visualize the expression levels of these DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003eD). We focused further research on DEGs commonly high or low expressed in both cancers. Notably, KRT19 was significantly overexpressed in both breast and thyroid cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003eE, F). ROC curve analysis evaluated the diagnostic value of KRT19, with AUCs of 0.861 for breast cancer and 0.821 for thyroid cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003eG, H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Relationship Between KRT19 Expression and Clinico-pathological Characteristics\u003c/h2\u003e \u003cp\u003eAnalysis of BRCA and THCA data from UCSC Xena revealed that among 1162 breast cancer patients, KRT19 expression was significantly associated with age, distant metastasis, stage, and ER, PR, and HER-2 status (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e). Among 558 thyroid cancer patients, KRT19 expression was significantly associated with tumor size, lymph node metastasis, and clinical stage (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between KRT19 expression and clinical phenotype of breast cancer patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategorization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow Expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e673(98.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e476(99.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(0.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88(18.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e593(86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e390(81.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177(25.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120(25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e391(57.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e286(59.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90(13.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54(11.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17(3.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1(0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307(44.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e237(49.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e9.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165(34.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(11.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48(10.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(8.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23(4.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e551(80.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e431(90.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e20.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117(17.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40(8.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115(16.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78(16.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e11.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372(54.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e296(61.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170(24.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97(20.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(0.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndeterminate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e46.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30(6.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(14.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144(30.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e554(80.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e302(63.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndeterminate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e31.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31(6.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e185(38.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e482(70.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e260(54.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHER-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndeterminate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139(20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56(11.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e19.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122(17.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72(15.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319(46.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e271(56.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79(16.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Note: ER denotes estrogen receptor, PR denotes progesterone receptor, HER-2 denotes human epidermal growth factor receptor 2, and the designation 'X' in the TNM staging system represents 'cannot be assessed').\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between KRT19 expression and clinical phenotype of thyroid cancer patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategorization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow Expression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e265(73.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141(71.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96(26.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(28.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168(46.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89(45.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193(53.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108(54.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93(25.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(29.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e11.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108(29.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(39.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137(37.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54(27.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21(5.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(3.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(37.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123(62.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e54.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201(55.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(23.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(14.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216(59.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106(53.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(3.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140 (38.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85(43.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202(55.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114(57.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e29.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22(6.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(18.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89(24.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(17.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(5.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Note: In the TNM staging system, 'X' designates that the category cannot be assessed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eGSEA was performed to identify pathways associated with KRT19 upregulation in breast and thyroid cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e). The results indicated significant enrichment of both the early and late estrogen response pathways in both breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003eA, B) and thyroid cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003eC, D) with high KRT19 expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Note: A significant enrichment was defined as Nominal p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25, and |NES| \u0026ge; 1).\u003c/p\u003e \u003cp\u003e2.4 Comparison of General and Pathological Characteristics Between the Dual-Primary Cancer Group and the Breast Cancer-Alone Group\u003c/p\u003e \u003cp\u003eCompared to the breast cancer-alone group, the dual-primary cancer group had a significantly higher proportion of premenopausal patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), larger tumor size (P\u0026thinsp;=\u0026thinsp;0.005), and higher rates of positive ER (P\u0026thinsp;=\u0026thinsp;0.049) and PR (P\u0026thinsp;=\u0026thinsp;0.041) expression. No statistically significant differences were found between the two groups regarding pathological type of breast cancer (P\u0026thinsp;=\u0026thinsp;0.070), lymph node metastasis status (P\u0026thinsp;=\u0026thinsp;0.725), HER-2 expression level (P\u0026thinsp;=\u0026thinsp;0.773), molecular subtype (P\u0026thinsp;=\u0026thinsp;0.081), or KRT19 expression level (P\u0026thinsp;=\u0026thinsp;0.069). See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of general and pathological characteristics between the double primary cancer group and the breast cancer group [n(%)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategorization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDouble Primary Cancers Group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBreast Cancer Group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge at Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMenopausal Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePathological Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvasive Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-invasive Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTumor Diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLymph Node Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular Subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLuminal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHER-2\u0026thinsp;+\u0026thinsp;NAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRT19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeakly Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Note: ER denotes estrogen receptor, PR denotes progesterone receptor, and HER-2 denotes human epidermal growth factor receptor 2).\u003c/p\u003e \u003cp\u003e2.5 Comparison of General and Pathological Characteristics Between the Dual-Primary Cancer Group and the Thyroid Cancer-Alone Group\u003c/p\u003e \u003cp\u003eCompared to the thyroid cancer-alone group, thyroid cancers in the dual-primary cancer group were more likely to be bilateral (P\u0026thinsp;=\u0026thinsp;0.035) and exhibited higher KRT19 expression levels (P\u0026thinsp;=\u0026thinsp;0.008). No statistically significant differences were observed in age at diagnosis (P\u0026thinsp;=\u0026thinsp;0.516), menopausal status (P\u0026thinsp;=\u0026thinsp;0.138), pathological type (P\u0026thinsp;=\u0026thinsp;0.933), proportion of microcarcinomas (\u0026le;\u0026thinsp;1cm) (P\u0026thinsp;=\u0026thinsp;0.577), or lymph node metastasis status (P\u0026thinsp;=\u0026thinsp;0.102). See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eCompared to the thyroid cancer-alone group, the dual-primary cancer group had significantly higher serum levels of triiodothyronine (T3) (P\u0026thinsp;=\u0026thinsp;0.010) and thyroxine (T4) (P\u0026thinsp;=\u0026thinsp;0.040). No significant differences were found in serum levels of free T3 (FT3) (P\u0026thinsp;=\u0026thinsp;0.125), free T4 (FT4) (P\u0026thinsp;=\u0026thinsp;0.242), thyroid-stimulating hormone (TSH) (P\u0026thinsp;=\u0026thinsp;0.068), thyroglobulin antibody (TGAb) (P\u0026thinsp;=\u0026thinsp;0.606), or thyroid peroxidase antibody (TPOAb) (P\u0026thinsp;=\u0026thinsp;0.704). See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of general and pathological characteristics between the double primary cancer group and the thyroid cancer group[n(%)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid Cancer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDouble Primary Cancers Group (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingle Cancer Group (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge at Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMenopausal Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostmenopausal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePathological Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePapillary Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98 (98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Papillary Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMicrocarcinoma (\u0026le;\u0026thinsp;1cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLymph Node Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnilateral or Bilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eKRT19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerately Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeakly Positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2.5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of thyroid hormone and antigen antibody between the double primary cancer group and the thyroid cancer group \u003cem\u003eM\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid Hormones and Thyroid Antibodies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDouble Primary Cancers Group (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThyroid Cancer Group (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ez-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3(pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.73 (4.43\u0026ndash;5.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.91 (4.63\u0026ndash;5.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4(pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16.16 (14.42\u0026ndash;17.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.27 (15.00-18.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3(nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.71 (1.48\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52 (1.33\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4(nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e107.80(94.55-127.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.30 (85.30-115.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH(uIU/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.24 (0.82\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57 (1.08\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGAb(U/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e20.60(15.00-160.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.60 (15.00-78.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTPOAb(U/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.35 (28.00-496.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.50 (28.00-226.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Note: FT3 denotes free triiodothyronine, FT4 denotes free thyroxine, T3 denotes triiodothyronine, T4 denotes thyroxine, TSH denotes thyroid-stimulating hormone, TGAb denotes thyroglobulin antibody, and TPOAb denotes thyroid peroxidase antibody).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe relationship between breast cancer and thyroid cancer is complex. Previous studies have indicated that ER and PR expression is higher in patients with co-occurring breast and thyroid cancers compared to those with breast cancer alone\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, suggesting a potential common molecular pathogenesis for these dual primary cancers. Our study aimed to explore the clinicopathological correlations in the development of dual primary cancers and identify potential biomarkers linking breast and thyroid cancer. In our initial analysis, we identified 20 common DEGs. Abdelaziz LA et al.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e detected CK19 (KRT19) levels in peripheral blood of breast cancer patients by flow cytometry and assessed OCT4 expression in tissues by IHC, finding that positive expression of both was negatively correlated with patient survival. Gao et al.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e revealed that the disease-free survival rate was lower in thyroid cancer patients with high KRT19 mRNA expression compared to those with low expression. Our study further demonstrates that KRT19 is highly expressed in both breast and thyroid cancers, suggesting its potential role as a common pathogenic gene, prompting further investigation.\u003c/p\u003e \u003cp\u003eKeratin 19 (KRT19), a gene located on chromosome 17q21.2, encodes the Cytokeratin-19 (CK19) protein, which is abundantly expressed in a wide range of epithelial tumors, including those of the pancreas, colorectum, esophagus, stomach, and breast \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Notably, KRT19/CK19 expression is reported to be higher in ER-positive (ER+) than in ER-negative (ER-) breast cancers \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The mechanistic link between estrogen receptor signaling and KRT19 is underscored by our current findings, which demonstrate upregulated KRT19 expression in ER\u0026thinsp;+\u0026thinsp;breast cancer. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed that KRT19 overexpression significantly enriches both early and late estrogen response pathways in breast and thyroid cancers. This aligns with established literature wherein ERα enhances tumor cell migration and metastasis by upregulating MMP-9 and downregulating E-cadherin \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, and estrogen activates key signaling pathways like MAPK and PI3K, as well as Matrix Metalloproteinases (MMPs), via membrane-associated estrogen receptors \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Supporting this axis, studies have shown that estrogen promotes proliferation, migration, and invasion in papillary thyroid carcinoma through the ERα/KRT19 signaling pathway \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Collectively, these findings suggest that KRT19, potentiated by estrogenic signaling, may drive the progression of both breast and thyroid cancers, thereby potentially elevating the risk of developing second primary tumors in patients with either malignancy.\u003c/p\u003e \u003cp\u003eAs two of the most common malignant diseases threatening women's health globally, breast cancer and thyroid cancer are increasingly affecting younger populations, posing serious risks. Both the breast and thyroid are target organs regulated by the hypothalamic-pituitary axis. Research on the relationship between these two cancers dates back to 1896. Current domestic and international studies suggest a connection. Sadetzki S et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e reported in 2003 that the likelihood of breast cancer patients developing metachronous thyroid cancer was 1.34%, and vice versa was 1.07%. In our cohort, 86 patients developed thyroid cancer after breast cancer, while 6 developed breast cancer after thyroid cancer. The higher number of breast cancer preceding thyroid cancer cases aligns with findings by Ji Wenzhong et al.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, possibly because breast cancer often progresses faster with more apparent early symptoms, facilitating detection. This highlights the importance of routine thyroid screening in breast cancer patients for early detection of thyroid abnormalities.\u003c/p\u003e \u003cp\u003ePathological classification of breast cancer into invasive and non-invasive types based on the extent of invasion and metastatic risk correlates with prognosis, with non-invasive carcinoma having the best prognosis\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Most breast cancers are invasive. In our study, invasive breast cancer accounted for 80% and 88% in the dual-primary and breast cancer-alone groups, respectively, consistent with previous reports\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Yang Haibo et al.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e found a higher proportion of premenopausal patients among those with breast cancer combined with thyroid cancer compared to those with breast cancer alone, which aligns with our results. Furthermore, patients in the dual-primary group were diagnosed at a younger age and had larger breast tumors (\u0026gt;\u0026thinsp;2cm), possibly due to the more aggressive nature of breast cancer compared to thyroid cancer, underscoring the need for thyroid screening in young breast cancer patients.\u003c/p\u003e \u003cp\u003eOur study showed that ER and PR were more frequently positive in breast cancer patients with concurrent thyroid cancer compared to those with breast cancer alone, consistent with previous research\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. ER is a protein molecule located on the cell membrane, in the cytoplasm, and nucleus that specifically binds estrogen and regulates growth and differentiation in the reproductive system. PR is a product induced by estrogen action and can enhance the response of sex hormones via ER. Both play synergistic roles in cell growth and development. ER and PR status are important factors for evaluating breast cancer prognosis\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e; positive expression is associated with lower proliferation and invasion indices, relatively slower clinical progression, and sensitivity to endocrine therapy. Earlier studies support the expression of ER and PR in thyroid tissue\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. As an endocrine gland, thyroid malignancies also exhibit hormone dependence. Han et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e found that ER and PR can promote the proliferation of thyroid tumor cells by upregulating the cell cycle. Other studies suggest estrogen can regulate breast carcinogenesis by modulating DNA methylation, with enhancers binding ERα, FOXA1, and GATA3 showing demethylation in ER\u0026thinsp;+\u0026thinsp;breast cancer\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. DNA methylation is also implicated in thyroid carcinogenesis\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. It is plausible that estrogen acts via ER to promote the development of second primary thyroid cancer in breast cancer patients through DNA demethylation, though the precise mechanisms require further investigation.\u003c/p\u003e \u003cp\u003eZhao Yuanyuan et al.\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e found that patients with thyroid cancer combined with breast cancer were more often postmenopausal, possibly due to declining gonadal function and deficient hormone levels. In our study, compared to the thyroid cancer-alone group, a higher proportion of patients in the dual-primary group were postmenopausal (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.138). Although not statistically significant, the P-value close to 0.05 suggests that breast examination should not be neglected in older thyroid cancer patients. Additionally, patients with concurrent breast cancer were more likely to have bilateral thyroid cancer, possibly related to the malignant progression of breast cancer promoting thyroid tumor development through endocrine mechanisms.\u003c/p\u003e \u003cp\u003eAmong thyroid hormones, thyroxine (T4) is entirely secreted by the thyroid gland, while most triiodothyronine (T3) is produced peripherally from T4 deiodination. A study by Moretto FC\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e found that when T3 binds to its receptor in breast cancer cells, it activates the PI3K pathway, increasing the expression of important proteins like TGFα, a transforming growth factor that promotes cancer cell growth. Other studies indicate that thyroid hormones can mimic estrogen effects to promote breast cell growth in vitro\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Our results showed significantly higher T3 and T4 levels in the dual-primary group compared to the thyroid cancer-alone group, suggesting that high T3/T4 levels increase the risk of breast cancer following thyroid cancer. T3 and T4 exert mitogenic effects in thyroid cancer cells by binding to the membrane receptor αvβ3 integrin\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, which is expressed in various cancers, including breast and thyroid cancer. It is hypothesized that thyroid hormones might influence breast cancer via this receptor connection, potentially supporting the use of TSH suppression therapy in thyroid cancer patients to mitigate breast cancer progression in dual-primary cases.\u003c/p\u003e \u003cp\u003eIn our study, KRT19 expression was higher in the dual-primary group compared to the breast cancer-alone group, though the difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.069). However, KRT19 expression was significantly higher in the thyroid cancer components of the dual-primary group compared to thyroid cancer alone (P\u0026thinsp;=\u0026thinsp;0.008). The lack of statistical significance in the breast cancer comparison, despite a trend (P\u0026thinsp;=\u0026thinsp;0.069), might be attributed to the relatively small sample size of dual cancer cases. This study has limitations. The sample size was small, and the long interval between primary cancer diagnoses and limited tissue preservation may have affected immunohistochemistry quality. Additionally, follow-up data and manual verification of results were lacking.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003e1. KRT19 is a potential biomarker influencing the development of thyroid cancer following breast cancer, or breast cancer following thyroid cancer. It likely affects the pathogenesis of dual primary cancers through the estrogen response pathway.\u003c/p\u003e\n\u003cp\u003e2. Breast cancer patients with positive ER and PR expression are more susceptible to developing thyroid cancer. KRT19 expression is associated with positive hormone receptor status, particularly ER positivity.\u003c/p\u003e\n\u003cp\u003e3. High T3 and T4 levels in thyroid cancer patients may indicate an increased risk of concurrent breast cancer.\u003c/p\u003e"},{"header":"5. Summary","content":"\u003cp\u003eIn conclusion, a pathogenic correlation exists between breast cancer and thyroid cancer. KRT19 expression likely promotes tumor development in patients with either cancer through the early and late estrogen response pathways. Clinically, breast cancer patients who are ER-positive, PR-positive, younger, premenopausal, or have larger tumors should prioritize routine thyroid examination. Conversely, thyroid cancer patients with high T3 and T4 levels should not overlook the potential for breast malignancy. KRT19 may serve as a potential biomarker for predicting the risk of thyroid cancer in breast cancer patients, or breast cancer in thyroid cancer patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e1. Ethics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. The research protocol and related procedures involving human participants were reviewed and approved by the Clinical Research and Experimental Animal Ethics Committee of Zhongshan People’s Hospital (Project No.: 2025-128). Written informed consent was obtained from all individual participants included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. Consent for publication.\u003c/p\u003e\n\u003cp\u003eThe paper titled “Correlation analysis and exploration of potential biomarkers in patients with breast cancer combined with thyroid cancer” is authored by Xinran Cai, Juan Wang, Shihui Ma, Yingzhi Chen, and Shijun Sun from Zhongshan People’s Hospital. The project associated with this manuscript has undergone ethical review and has been approved by the Clinical Research and Experimental Animal Ethics Committee of Zhongshan People’s Hospital. The Committee hereby grants approval for its publication.\u003c/p\u003e\n\u003cp\u003e2. Consent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e3. Availability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e4. Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e5. Funding\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e6. Authors' contributions\u003c/p\u003e\n\u003cp\u003eX.C. and J.W. contributed equally to this work. X.C. and J.W. contributed to conceptualization, methodology, formal analysis, investigation, data curation, and writing—original draft preparation. S.S. and Y.C. contributed to resources, specimen acquisition, pathological evaluation, and methodology. S.M. contributed to conceptualization, resources, writing—review and editing, supervision, project administration, and funding acquisition. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e7. Acknowledgements\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;I extend my sincere gratitude to all those who have assisted me.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024 May-Jun;74(3):229-263.\u003c/li\u003e\n\u003cli\u003eChinese Anti-Cancer Association Committee of Breast Cancer Society, Chinese Society of Oncology Breast Cancer Group, Shao Zhimin. Guidelines and Standards for Breast Cancer Diagnosis and Treatment by Chinese Anti-Cancer Association (2024 Edition)[J]. 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Clinicopathological characteristics of patients with papillary thyroid carcinoma and breast cancer[J]. Journal of Jiangsu University (Medicine Edition), 2022, 32(06): 461-466.\u003c/li\u003e\n\u003cli\u003eMoretto FC, De Sibio MT, Luvizon AC, et al. Triiodothyronine (T3) induces HIF1A and TGFA expression in MCF7 cells by activating PI3K. Life Sci. 2016 Jun 1;154:52-7. \u003c/li\u003e\n\u003cli\u003eOrtega-Olvera C, Ulloa-Aguirre A, \u0026Aacute;ngeles-Llerenas A, et al. Thyroid hormones and breast cancer association according to menopausal status and body mass index. Breast Cancer Res. 2018 Aug 9;20(1):94.\u003c/li\u003e\n\u003cli\u003eLiang Jiazheng, Feng Ziyu, Song Xudong, et al. Research progress on the relationship between thyroid diseases and breast cancer[J]. Anhui Medical and Pharmaceutical Journal, 2022, 26(06): 1068-1073..\u003c/li\u003e\n\u003cli\u003eTobi D, Krashin E, Davis PJ, et al. 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Front Endocrinol (Lausanne). 2022 May 27; 13:895240.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Thyroid Cancer, Correlation, KRT19, Estrogen Receptor","lastPublishedDoi":"10.21203/rs.3.rs-8240986/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8240986/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective(s):\u003c/h2\u003e \u003cp\u003e1. The common differential genes of breast cancer and thyroid cancer were identified by Gene Express Omnibus database (GEO). The Cancer Genome Atlas (TCGA) database was used to determine the relationship between the screened differential genes and the clinicopathologic features. Gene set enrichment analysis (GSEA) was used to identify the enrichment pathways of the differential genes in breast and thyroid cancers.2. Retrospective studies were conducted to analyze the relationship between the general characteristics, clinicopathological features, and hormone expression levels in patients with dual cancers of breast cancer combined with thyroid cancer and patients with breast cancer and thyroid cancer alone, and to further analyze the KRT19 expression levels in their tissues. analyze the expression of KRT19 protein in their tissues.\u003c/p\u003e\u003ch2\u003eMethod(s):\u003c/h2\u003e \u003cp\u003e1. Download the datasets GSE70947 and GSE3467 from the GEO database, analyze the differential genes of breast cancer, thyroid cancer and normal tissues respectively by using R, and take the intersection of the differential genes of the two tissues to continue the next step of the study. 2. Download the mRNA-seq data of the above differential genes of BRCA and THCA from the TCGA database, and identify the differences in expression of differential genes between normal tissues and tumor tissues by using the above differential gene substitution in R. Gene set enrichment analysis (GSEA) identifies the differences in expression of differential genes enriched in breast and thyroid cancer. Differential gene substitution analysis, and identify the differences in differential gene expression between normal and tumor tissues, Gene set enrichment analysis (GSEA) to identify the pathways of enrichment in breast and thyroid cancers, and then screened out the representative differential gene KRT19 (human cytokeratin 19).3. Collect the mRNA-seq data of BRCA and THCA in the database of TCGA, and use the R language in the substitution analysis of the above differential genes, and identify the differences in differential gene expression between normal and tumor tissues. 3. July 2023 in Zhongshan People's Hospital and puncture or surgical treatment of breast cancer combined with thyroid cancer patient data, a total of 92 cases (experimental group), another randomly collected in the past two years in Zhongshan People's Hospital in the simple breast cancer patients 100 cases (control group 1), thyroid cancer patients, 100 cases (control group 2). 4. the experimental group and the control group 1, 2 of the patient pathology data for Pathological data of patients in experimental and control groups 1 and 2 were retrospectively analyzed, and additional surgical bulk or puncture specimens were subjected to immunohistochemical staining (IHC) to examine the expression of KRT19 protein in the tissues, and to explore whether there was a difference in its expression between experimental and control groups.\u003c/p\u003e\u003ch2\u003eResult(s):\u003c/h2\u003e \u003cp\u003e1. KRT19 mRNA levels were significantly overexpressed in breast and thyroid cancer tissues as analyzed by the GEO database, and KRT19 was associated with clinicopathological features of breast and thyroid cancers as analyzed by the TCGA database, and the GSEA showed that both the breast and thyroid cancer-KRT19 overexpression groups were significantly enriched in the estrogen-responsive pathway.2. In the dual-primary cancer group, of which breast cancer preceded In the dual-primary cancer group, breast cancer preceded thyroid cancer in 86 cases and thyroid cancer preceded in 6 cases, i.e., breast cancer preceded thyroid cancer in the vast majority of patients (\u0026gt;\u0026thinsp;90%). Compared with the breast cancer group, the dual-primary cancer group was younger at the time of diagnosis, more often in the premenopausal state, with a larger tumor size, and more often positively expressed estrogen receptors (ER) and progesterone receptors (PR), with a statistically significant difference (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050). Compared with the thyroid cancer group, the odds of tumors occurring bilaterally were increased in the double primary cancer group, and the levels of triiodothyronine (T3) and thyroxine (T4) (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050) were significantly higher. KRT19 was more frequently positively expressed in breast cancer than in breast cancer alone in dual primary cancers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.069), and in thyroid cancer than in thyroid cancer alone in dual primary cancers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.050).\u003c/p\u003e\u003ch2\u003eConclusion(s):\u003c/h2\u003e \u003cp\u003ePathogenesis correlates between breast and thyroid cancers, estrogen receptor expression is associated with dual carcinogenesis, and KRT19 influences dual carcinogenesis through the estrogen response pathway.\u003c/p\u003e","manuscriptTitle":"Correlation analysis and exploration of potential biomarkers in patients with breast cancer combined with thyroid cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 10:13:12","doi":"10.21203/rs.3.rs-8240986/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-10T05:52:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210328813875657720924434310229483741929","date":"2026-04-10T05:32:52+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T14:34:35+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-11T09:00:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-11T06:18:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-10T18:01:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-12-10T17:56:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"660de473-8f59-4801-99a2-cc64793b0593","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T10:13:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 10:13:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8240986","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8240986","identity":"rs-8240986","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}