Xiang Wang, Xuan Wang, Mengsheng Zhao, Lijuan Lin, Yi Li, Ning Xie, Yanru Wang, Aoxuan Wang, Xiaowen Xu, Can Ju, Qiuyuan Chen, Jiajin Chen, Ruili Hou, Zhongwen Zhang, David C. Christiani, Feng Chen, Yongyue Wei, Ruyang Zhang. Bidirectional Mendelian randomization and mediation analysis of million-scale data reveal causal relationships between thyroid-related phenotypes, smoking, and lung cancer[J]. The Journal of Biomedical Research. DOI: 10.7555/JBR.38.20240421
Citation:
Xiang Wang, Xuan Wang, Mengsheng Zhao, Lijuan Lin, Yi Li, Ning Xie, Yanru Wang, Aoxuan Wang, Xiaowen Xu, Can Ju, Qiuyuan Chen, Jiajin Chen, Ruili Hou, Zhongwen Zhang, David C. Christiani, Feng Chen, Yongyue Wei, Ruyang Zhang. Bidirectional Mendelian randomization and mediation analysis of million-scale data reveal causal relationships between thyroid-related phenotypes, smoking, and lung cancer[J]. The Journal of Biomedical Research. DOI: 10.7555/JBR.38.20240421
Xiang Wang, Xuan Wang, Mengsheng Zhao, Lijuan Lin, Yi Li, Ning Xie, Yanru Wang, Aoxuan Wang, Xiaowen Xu, Can Ju, Qiuyuan Chen, Jiajin Chen, Ruili Hou, Zhongwen Zhang, David C. Christiani, Feng Chen, Yongyue Wei, Ruyang Zhang. Bidirectional Mendelian randomization and mediation analysis of million-scale data reveal causal relationships between thyroid-related phenotypes, smoking, and lung cancer[J]. The Journal of Biomedical Research. DOI: 10.7555/JBR.38.20240421
Citation:
Xiang Wang, Xuan Wang, Mengsheng Zhao, Lijuan Lin, Yi Li, Ning Xie, Yanru Wang, Aoxuan Wang, Xiaowen Xu, Can Ju, Qiuyuan Chen, Jiajin Chen, Ruili Hou, Zhongwen Zhang, David C. Christiani, Feng Chen, Yongyue Wei, Ruyang Zhang. Bidirectional Mendelian randomization and mediation analysis of million-scale data reveal causal relationships between thyroid-related phenotypes, smoking, and lung cancer[J]. The Journal of Biomedical Research. DOI: 10.7555/JBR.38.20240421
Unproofed Manuscript: The manuscript has been professionally copyedited and typeset to confirm the JBR’s formatting, but still needs proofreading by the corresponding author to ensure accuracy and correct any potential errors introduced during the editing process. It will be replaced by the online publication version.
Bidirectional Mendelian randomization and mediation analysis of million-scale data reveal causal relationships between thyroid-related phenotypes, smoking, and lung cancer
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
2.
China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
3.
Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
4.
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
5.
Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
6.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
7.
Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing 100191, China
8.
Changzhou Medical Center, Nanjing Medical University, Changzhou, Jiangsu 213164, China
Yongyue Wei, Center for Public Health and Epidemic Preparedness & Response, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China. E-mail: ywei@pku.edu.cn; Feng Chen and Ruyang Zhang, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China. E-mails: fengchen@njmu.edu.cn (Chen) and zhangruyang@njmu.edu.cn (Zhang)
Emerging evidence highlights the role of thyroid hormones in cancer, though findings are controversial. Research on thyroid-related traits in lung carcinogenesis is limited. Using UK Biobank data, we conducted bidirectional Mendelian randomization (MR) to assess causal links between lung cancer risk and thyroid dysfunction (hypothyroidism/hyperthyroidism) or function traits (free thyroxine [FT4], normal-range TSH). Furthermore, in the smoking-behavior stratified MR analysis, we evaluated the mediating effect of thyroid-related phenotypes on the association between smoking phenotype and lung cancer. We confirmed significant associations between lung cancer risk and hypothyroidism (hazards ratio [HR] = 1.14, 95% confidence interval [CI] = 1.03–1.26, P = 0.009) as well as hyperthyroidism (HR = 1.55, 95% CI = 1.29–1.87, P = 1.90×10−6) in the UKB. Moreover, the MR analysis indicated a causal effect of thyroid dysfunction on lung cancer risk (ORinverse variance weighted [IVW] = 1.09, 95% CI = 1.05–1.13, P = 3.12×10−6 for hypothyroidism; ORIVW = 1.08, 95% CI = 1.04–1.12, P = 8.14×10−5 for hyperthyroidism). We found that FT4 levels were protective against lung cancer risk (ORIVW = 0.93, 95% CI = 0.87–0.99, P = 0.030). Additionally, the stratified MR analysis demonstrated the distinct causal effect of thyroid dysfunction on lung cancer risk among smokers. Hyperthyroidism mediated the effect of smoking behavior, especially the age of smoking initiation (17.66% mediated), on lung cancer risk. Thus, thyroid dysfunction phenotypes play causal roles in lung cancer development exclusively among smokers and act as mediators in the causal pathway from smoking to lung cancer.
Nowadays, colorectal cancer (CRC) is one of the most deadly cancers and almost 900 000 CRC-related deaths were reported each year in the world[1]. With the understanding of pathophysiology of the disease, different treatment options to improve the survival rates of CRC patients have been developed in the world. The 5-year survival rate of CRC patients was >90% when the patients were diagnosed at early stages [2]. However, due to lacking early detection methods, many CRC patients were diagnosed at an advanced stage or in the metastasis status. And the 5-year survival rate for those diagnosed with metastasis was at approximately 12%[3]. Recently, a new kind of analysis method has been used to identify the differential expression genes between CRC and normal tissues based on the high-throughput sequencing platforms, such as microarrays. This is a promising tool with extensive clinical applications, including molecular diagnosis, prognosis prediction, new drug targets discovery, etc[4–6]. Furthermore, microarray assay combining bioinformatics analysis made it possible to analyze the gene expression on mRNA level in CRC progression. For example, several studies have used this method to identify key genes in CRC development through comparing with normal samples, and showed that the key genes were involved in different signal pathways, biological processes, and molecular functions[7–12].
However, with a relatively limited degree of overlap, we still can not find reliable biomarkers or drug targets. Therefore, the discovery of novel biomarkers for early detection and prognosis prediction of CRC is urgently required.
In the present study, we targeted to find key genes to develop novel biomarkers or drug targets for CRC. Therefore, we chose four Gene Expression Omnibus (GEO) datasets, GSE113513, GSE21510, GSE44076, and GSE32323, and used bioinformatics methods to screen the significant differentially expressed genes (DEGs) between CRC tissues and normal tissues. Gene ontology (GO) and KEGG Pathway analyses were used to find the biological roles of these DEGs through DAVID database. Furthermore, the PPI network of DEGs was constructed and key modules or hub genes were selected with Molecular Complex Detection (MCODE) plugin of Cytoscape software. And the clinical significance was validated by GEPIA database. Finally, small active candidate molecules were identified to develop new drugs through Connectivity Map (CMap) database. In brief, we found four hub genes involved in CRC, which may provide novel potential biomarkers for CRC prognosis, and two potential candidate drugs for CRC.
Materials and methods
Data resources
To explore the differential gene expression profiles between CRC and normal tissues, we searched the NCBI-GEO database to collect enough and adequate tissues. A total of 4 GEO datasets were selected, including GSE113513, GSE21510, GSE44076, and GSE32323. These mRNA profiles were based on platform GPL15207 (GSE113513), GPL570 (GSE21510 and GSE32323), and GPL13667 (GSE44076). A total of 253 CRC samples and 203 normal samples were chosen for this study, including 14 pairs of cancer and normal samples in GSE113513, 124 CRC samples and 24 normal samples in GSE21510, 98 pairs of cancer and normal samples plus 50 healthy donor tissues in GSE44076, and 17 pairs of cancer and normal samples in GSE32323.
Identification of DEGs and data preprocessing
To identify the DEGs, we used the NCBI-GEO2R online tool to analyze these datasets. Subsequently, adjusted P-value <0.05 and |log 2(fold change)| >1 were set as the cutoff criteria to screen the significant DEGs of each dataset. Finally, Venn diagrams were performed to get the overlap significant DEGs of the 4 datasets.
GO and KEGG pathway analyses of DEGs
To find the biological functional roles of DEGs, GO and KEGG pathway analyses were performed through DAVID database. Significant results of molecular function (MF), biological process (BP), cellular component (CC), and biological pathways were selected with P-value <0.05.
PPI network construction and module analysis
The DEGs profiles were submitted to STRING database for exploring their potential interactions. The interactions with a combined score >0.4 were considered significant. Subsequently, the interaction files were downloaded and imported into Cytoscape software to construct the PPI network. The MCODE plugin was used to find key modules of the whole PPI network with a degree cutoff=2, node score cutoff=0.2, K-core=2, and max depth=100. The hub genes were then selected with connectivity degree >10. Furthermore, KEGG pathway analyses of the significant modules were performed with P-value <0.05.
Analysis and validation of hub genes
To verify the hub genes we found, we used GEPIA database to analyze their expression and clinical prognostic information in 270 CRC patients. And the survival curve, stage analysis and box plot were performed to show the clinical implications of hub genes.
Identification of small molecules
To find potential small active molecules to develop new drugs for treating CRC, we uploaded DEGs probe profiles into the CMap database. This database can help to predict small molecules that induce or reverse gene expression signature with a score from −1 to 1. And the molecules which value from 0 closer to −1 were functioned as reversing the cancer cell status.
Results
Identification of DEGs in CRC
Analyzed with the GEO2R online tool, a total of 1763, 4411, 2428, and 2276 DEGs were extracted from GSE113513, GSE21510, GSE44076, and GSE32323, respectively, using adjusted P-value <0.05 and |log 2(fold change)| >1 as cutoff criteria. The volcano plots of DEGs in each dataset were shown in Fig. 1A. And the Venn diagrams showed that 865 overlap DEGs were identified from these four datasets, including 374 significantly upregulated genes and 491 downregulated genes (Fig. 1B and Supplementary Table 1, available online).
Figure
1.
Identification of the DEGs in CRC.
A: Volcano plots of gene expression profiles between CRC and normal samples from GSE113513, GSE21510, GSE44076 and GSE32323. Red dots: significantly upregulated genes in CRC; Green dots: remarkably downregulated genes in CRC. Adjusted P value <0.05 and |log2(fold change)| >1 were considered as significant criteria. B: Venn diagrams show that 865 overlap DEGs were found through GEO2R in the four datasets, including 374 upregulated DEGs and 491 downregulated DEGs. DEGs: differentially expressed genes; CRC: colorectal cancer.
To explore the biological functional roles of the overlap DEGs, GO and KEGG analyses were performed on DAVID database. And the top 20 terms were listed in the charts (Fig. 2A–D and Supplementary Table 2, available online). The GO analysis results consist of three functional categories, including BP, CC, and MF. In the BP group, DEGs were mainly enriched in cell proliferation (Fig. 2A). In the CC group, DEGs were enriched in cytoplasm (Fig. 2B). And in the MF group, DEGs were enriched in protein binding (Fig. 2C). KEGG pathway analysis showed that DEGs were enriched in metabolic pathways, pathways in cancer and cell cycle (Fig. 2D). The details of the top 20 terms were listed in Supplementary Table 2.
Figure
2.
GO and KEGG analysis of the overlap differentially expressed genes in colorectal cancer through DAVID online-tools.
Top 20 terms of biological processes (A), cellular components (B), molecular functions (C), and KEGG signaling pathways (D) were shown in the charts, and P-value <0.05 was considered as selection criteria.
Using the STRING online database and Cytoscape software, a total of 865 DEGs were filtered into the PPI network complex, containing 863 nodes and 5817 edges (Fig. 3). Based on degree scores using the MCODE plugin, two key modules were detected from the whole PPI network complex. Module 1 contained 61 nodes and 1648 edges, and DEGs were enriched in cell cycle, oocyte meiosis, progesterone-mediated oocyte maturation, DNA replication and p53 signaling pathway (Fig. 4A and B). Module 2 had 55 nodes and 625 edges, and these DEGs were enriched in chemokine signaling pathway, ribosome biogenesis in eukaryotes, cytokine-cytokine receptor interaction, pathways in cancer, purine metabolism, RNA polymerase, retrograde endocannabinoid signaling, TNF signaling pathway, legionellosis, regulation of lipolysis in adipocytes, NOD-like receptor signaling pathway, cytosolic DNA-sensing pathway and gastric acid secretion (Fig. 4C and D). Additionally, the top 20 hub genes, CDK1, CCNB1, MYC, CCNA2, MAD2L1, AURKA, TOP2A, CDC6, UBE2C, CHEK1, RRM2, BUB1B, TTK, TRIP13, TPX2, BUB1, NCAPG, KIF2C, KIF23, and MCM4 were identified with higher degrees of connectivity. These hub genes were enriched in cell cycle, progesterone-mediated oocyte maturation, oocyte meiosis, p53 signaling pathway, and HTLV-I infection (Fig. 4E and F).
Figure
3.
PPI interaction network of the overlap differentially expressed genes by STRING database.
Figure
4.
Module and KEGG analyses of the hub genes.
A and C: Two top modules screened from the whole PPI network of DEGs analyzed by MCODE plugin in Cytoscape. E: Top 20 hub genes with a higher degree of connectivity of DEGs were selected with MCODE plugin. B, D, and F: KEGG pathway analysis of the two top modules and top 20 hub genes. DEGs: differentially expressed genes.
To validate the hub genes we got from this study, we uploaded the hub genes list into GEPIA database and explored the correlation between hub genes expression and the clinical characteristics of CRC. It was found that HMMR, PAICS, ETFDH, and SCG2 were significant DEGs in 270 CRC samples from GEPIA (Fig. 5A). And these four genes could represent the important prognostic biomarkers for predicting the survival of CRC patients (Fig. 5B). Meanwhile, PAICS and SCG2 were related to the stages of CRC progression (Fig. 5C). The summaries of four hub genes were shown in Table 1.
Table
1.
Gene summaries of the four hub genes
Gene
Summary
Microarray datasets [P-value, log2(fold change)]
GSE113513
GSE21510
GSE44076
GSE32323
HMMR
The protein encoded by this gene is involved in cell motility. It is expressed in breast tissue and together with other proteins, it forms a complex with BRCA1 and BRCA2, thus is potentially associated with higher risk of breast cancer. Alternatively spliced transcript variants encoding different isoforms have been noted for this gene.
6.82e−04, 1.11
3.84e−35, 3.22
5.32e−22, 1.20
2.63e−04, 1.63
PAICS
This gene encodes a bifunctional enzyme containing phosphoribosylaminoimidazole carboxylase activity in its N-terminal region and phosphoribosylaminoimidazole succinocarboxamide synthetase in its C-terminal region. It catalyzes steps 6 and 7 of purine biosynthesis. The gene is closely linked and divergently transcribed with a locus that encodes an enzyme in the same pathway, and transcription of the two genes is coordinately regulated. The human genome contains several pseudogenes of this gene. Multiple transcript variants encoding different isoforms have been found for this gene.
4.77e−08, 1.31
5.71e−47, 2.48
2.14e−55, 1.49
1.38e−06, 1.73
ETFDH
This gene encodes a component of the electron-transfer system in mitochondria and is essential for electron transfer from a number of mitochondrial flavin-containing dehydrogenases to the main respiratory chain. Mutations in this gene are associated with glutaric acidemia. Alternatively spliced transcript variants that encode distinct isoforms have been observed.
1.78e−08, −1.47
1.67e−27, −1.92
1.80e−71, −1.85
3.14e−08, −1.78
SCG2
The protein encoded by this gene is a member of the chromogranin/secretogranin family of neuroendocrine secretory proteins. Studies in rodents suggest that the full-length protein, secretogranin II, is involved in the packaging or sorting of peptide hormones and neuropeptides into secretory vesicles. The full-length protein is cleaved to produce the active peptide secretoneurin, which exerts chemotaxic effects on specific cell types, and EM66, whose function is unknown.
Figure
5.
Analysis and validation of hub genes and identification of related active small molecules.
A and B: The expression level and prognostic value of four hub genes based on the GEPIA database. C: PAICS and SCG2 were related with the stages of CRC progression through GEPIA database. D: Top 20 potential small active molecules reverse DEG of CRC predicted by CMap database. TPM: transcripts per million.
To search candidate small molecules for developing potential drugs to treat CRC, we uploaded DEGs probe profiles into the CMap database. And the predicted results were download and filtered with enrichment score <0 and P-value <0.05. The results were shown in Table 2. And Fig. 5D listed the top 20 small molecules with their enrichment scores and P-values. Therefore, these small molecules may be the targets to develop new drugs or therapies of CRC. Among these molecules, Blebbistatin and Sulconazole may be selected for new clinical trials.
Table
2.
The significant small active molecules that may reverse the DEGs of CRC predicted by CMap
In summary, we chose GSE113513, GSE21510, GSE44076, and GSE32323 GEO datasets and found 865 significant DEGs between CRC tissues and normal tissues. Subsequently, the biological roles of these DEGs were confirmed with enrichment pathway analysis. Furthermore, the four hub genes, HMMR, PAICS, ETFDH, and SCG2 were identified as important prognostic biomarkers for predicting the survival of CRC patients based on the GEPIA database. Finally, blebbistatin and sulconazole were picked out to develop new drugs through CMap database (Fig. 6).
In our study, we chose four GEO datasets and used bioinformatics methods to get 865 DEGs (374 upregulated and 491 downregulated). KEGG pathway analysis showed that the key modules were mainly metabolic pathways, pathways in cancer, cell cycle, purine metabolism, pancreatic secretion, thyroid hormone signaling pathway and Wnt signaling pathway. The PPI network was constructed including 863 nodes and 5817 edges. The four hub genes, HMMR, PAICS, ETFDH, and SCG2 were remarkably related to the prognosis of patients. Furthermore, two small molecules, blebbistatin and sulconazole, also have been identified as potential candidates to develop new drugs.
Recently, findings about DEGs or molecular biomarkers of CRC have been increasingly reported. Based on integrated analysis of GSE32323, GSE74602, and GSE113513 datasets, and TCGA databases, CCL19, CXCL1, CXCL5, CXCL11, CXCL12, GNG4, INSL5, NMU, PYY, and SST were identified as hub genes. And 9 genes including SLC4A4, NFE2L3, GLDN, PCOLCE2, TIMP1, CCL28, SCGB2A1, AXIN2, and MMP1 was related to predicting overall survivals of CRC patients[8]. Moreover, TOP2A, MAD2L1, CCNB1, CHEK1, CDC6, and UBE2C were indicated as hub genes, and TOP2A, MAD2L1, CDC6, and CHEK1 may serve as prognostic biomarkers in CRC[10]. In addition, CEACAM7, SLC4A4, GCG, and CLCA1 genes were associated with unfavorable prognosis in CRC[11]. According to analysis of GEO datasets and survival analysis by GEPIA database, AURKA, CCNB1, CCNF, and EXO1 were significantly associated with longer overall survival. Moreover, CMap predicted that DL-thiorphan, repaglinide, MS-275, and quinostatin have the potential to treat CRC[9]. In this study, we have identified four hub genes as new potential biomarkers to predict the prognosis of CRC patients and two new small molecules. Further studies are needed to develop new drugs to treat CRC.
Several studies have reported that these hub genes play important roles in cancer development. For instance, HMMR expression level was remarkably correlated with the progression and prognosis of breast cancer[13], bladder cancer[14], prostate cancer[15–16], lung cancer[17–19], hepatocellular carcinoma (HCC)[20–22], and gastric cancer[23]. Furthermore, HMMR was confirmed to maintain its oncogenic properties and resistance to chemotherapy through activating TGF-β/Smad-2 signaling pathway[24]. And HMMR was highly expressed in glioblastoma and related to support the self-renewal and tumorigenic potential of glioblastoma stem cells[25]. PAICS was also upregulated in several kinds of cancer tissues and it promotes cancer cells proliferation, migration, and invasion[26–31]. The expression level of ETFDH was found significantly decreased in HCC tissues, and this low expression was related to poor overall survival in patients[32]. However, the role of SCG2 in cancer remains unclear.
In the present study, HMMR, PAICS, ETFDH, and SCG2 were significantly up or down regulated in CRC tissues compared with those in normal samples, and the survival rate of CRC patients was positively correlated with the expression of these genes. Besides, several small molecules with potential therapeutic efficacy were identified through bioinformatics analyses, including blebbistatin and sulconazole. Blebbistatin has been reported to inhibit cell migration and invasiveness of pancreatic adenocarcinoma[31], and decrease spreading and migration of breast cancer cells[33]. Moreover, blebbistatin has shown its antitumorigenic properties in HCC cells[34]. Another small molecule, sulconazole, also inhibited the proliferation and formation of breast cancer stem cells through blocking the NF-κB/IL-8 signaling pathway[35]. Although these two molecules have significant antitumor activity, their specific roles in CRC development need to be further clarified.
Acknowledgments
This work was supported by grants from the National Natural Science Foundation of China (No. 81672748 and No. 81871936).
Fundings
The study was funded by the National Natural Science Foundation of China (Grant Nos. 82220108002 to F.C., 82273737 to R.Z., and 82473728 to Y.W.), US National Institutes of Health (Grant Nos. CA209414, HL060710, and ES000002 to D.C.C.; CA209414 and CA249096 to Y.L.), and Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). R.Z. was partially supported by the Qing Lan Project of the Higher Education Institutions of Jiangsu Province and the Outstanding Young Level Academic Leadership Training Program of Nanjing Medical University.
Acknowledgments
We thank all the GWAS consortia involved in this work for making the summary statistics publicly available. We are grateful to all the investigators and participants who contributed to these studies.
Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J]. CA Cancer J Clin, 2023, 73(1): 17–48. doi: 10.3322/caac.21763
[2]
Postmus PE, Kerr KM, Oudkerk M, et al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2017, 28(S4): iv1–iv21. https://pubmed.ncbi.nlm.nih.gov/28881918/
[3]
Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: current therapies and new targeted treatments[J]. Lancet, 2017, 389(10066): 299–311. doi: 10.1016/S0140-6736(16)30958-8
[4]
Garon EB, Hellmann MD, Rizvi NA, et al. Five-year overall survival for patients with advanced non-small-cell lung cancer treated with pembrolizumab: results from the phase I KEYNOTE-001 study[J]. J Clin Oncol, 2019, 37(28): 2518–2527. doi: 10.1200/JCO.19.00934
[5]
Krashin E, Piekiełko-Witkowska A, Ellis M, et al. Thyroid hormones and cancer: a comprehensive review of preclinical and clinical studies[J]. Front Endocrinol (Lausanne), 2019, 10: 59. doi: 10.3389/fendo.2019.00059
[6]
Chi HC, Chen C, Tsai MM, et al. Molecular functions of thyroid hormones and their clinical significance in liver-related diseases[J]. Biomed Res Int, 2013, 2013: 601361. https://pubmed.ncbi.nlm.nih.gov/23878812/
[7]
Liu YC, Yeh CT, Lin KH. Molecular functions of thyroid hormone signaling in regulation of cancer progression and anti-apoptosis[J]. Int J Mol Sci, 2019, 20(20): 4986. doi: 10.3390/ijms20204986
[8]
Journy NMY, Bernier MO, Doody MM, et al. Hyperthyroidism, hypothyroidism, and cause-specific mortality in a large cohort of women[J]. Thyroid, 2017, 27(8): 1001–1010. doi: 10.1089/thy.2017.0063
[9]
Rinaldi S, Plummer M, Biessy C, et al. Thyroid-stimulating hormone, thyroglobulin, and thyroid hormones and risk of differentiated thyroid carcinoma: the EPIC study[J]. J Natl Cancer Inst, 2014, 106(6): dju097. doi: 10.1093/jnci/dju097
[10]
Khan SR, Chaker L, Ruiter R, et al. Thyroid function and cancer risk: the rotterdam study[J]. J Clin Endocrinol Metab, 2016, 101(12): 5030–5036. doi: 10.1210/jc.2016-2104
[11]
Tran TVT, Kitahara CM, de Vathaire F, et al. Thyroid dysfunction and cancer incidence: a systematic review and meta-analysis[J]. Endocr Relat Cancer, 2020, 27(4): 245–259. doi: 10.1530/ERC-19-0417
[12]
Chan YX, Knuiman MW, Divitini ML, et al. Lower TSH and higher free thyroxine predict incidence of prostate but not breast, colorectal or lung cancer[J]. Eur J Endocrinol, 2017, 177(4): 297–308. doi: 10.1530/EJE-17-0197
[13]
Jorde R, Sundsfjord J. Serum TSH levels in smokers and non-smokers. The 5th Tromsø study[J]. Exp Clin Endocrinol Diabetes, 2006, 114(7): 343–347. doi: 10.1055/s-2006-924264
[14]
Fisher CL, Mannino DM, Herman WH, et al. Cigarette smoking and thyroid hormone levels in males[J]. Int J Epidemiol, 1997, 26(5): 972–977. doi: 10.1093/ije/26.5.972
[15]
Prummel MF, Wiersinga WM. Smoking and risk of Graves' disease[J]. JAMA, 1993, 269(4): 479–482. doi: 10.1001/jama.1993.03500040045034
[16]
Gruppen EG, Kootstra-Ros J, Kobold AM, et al. Cigarette smoking is associated with higher thyroid hormone and lower TSH levels: the PREVEND study[J]. Endocrine, 2020, 67(3): 613–622. doi: 10.1007/s12020-019-02125-2
[17]
Sekula P, Del Greco MF, Pattaro C, et al. Mendelian randomization as an approach to assess causality using observational data[J]. J Am Soc Nephrol, 2016, 27(11): 3253–3265. doi: 10.1681/ASN.2016010098
[18]
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians[J]. BMJ, 2018, 362: k601. https://pubmed.ncbi.nlm.nih.gov/30002074/
[19]
Swanson SA, Tiemeier H, Ikram MA, et al. Nature as a trialist?: deconstructing the analogy between Mendelian randomization and randomized trials[J]. Epidemiology, 2017, 28(5): 653–659. doi: 10.1097/EDE.0000000000000699
[20]
Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age[J]. PLoS Med, 2015, 12(3): e1001779. doi: 10.1371/journal.pmed.1001779
[21]
Psaty BM, O'donnell CJ, Gudnason V, et al. Cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium: design of prospective meta-analyses of genome-wide association studies from 5 cohorts[J]. Circ: Cardiovasc Genet, 2009, 2(1): 73–80. doi: 10.1161/CIRCGENETICS.108.829747
[22]
Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population[J]. Nature, 2023, 613(7944): 508–518. doi: 10.1038/s41586-022-05473-8
[23]
Teumer A, Chaker L, Groeneweg S, et al. Genome-wide analyses identify a role for SLC17A4 and AADAT in thyroid hormone regulation[J]. Nat Commun, 2018, 9(1): 4455. doi: 10.1038/s41467-018-06356-1
[24]
Zhou W, Brumpton B, Kabil O, et al. GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer[J]. Nat Commun, 2020, 11(1): 3981. doi: 10.1038/s41467-020-17718-z
[25]
Saunders GRB, Wang X, Chen F, et al. Genetic diversity fuels gene discovery for tobacco and alcohol use[J]. Nature, 2022, 612(7941): 720–724. doi: 10.1038/s41586-022-05477-4
[26]
Wootton RE, Richmond RC, Stuijfzand BG, et al. Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study[J]. Psychol Med, 2020, 50(14): 2435–2443. doi: 10.1017/S0033291719002678
[27]
McKay JD, Hung RJ, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes[J]. Nat Genet, 2017, 49(7): 1126–1132. doi: 10.1038/ng.3892
[28]
Chang CC, Chow CC, Tellier LC, et al. Second-generation PLINK: rising to the challenge of larger and richer datasets[J]. GigaScience, 2015, 4: 7. doi: 10.1186/s13742-015-0047-8
[29]
Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol, 2013, 37(7): 658–665. doi: 10.1002/gepi.21758
[30]
Waterworth DM, Ricketts SL, Song K, et al. Genetic variants influencing circulating lipid levels and risk of coronary artery disease[J]. Arterioscler Thromb Vasc Biol, 2010, 30(11): 2264–2276. doi: 10.1161/ATVBAHA.109.201020
[31]
Bowden J, Spiller W, Del Greco MF, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression[J]. Int J Epidemiol, 2018, 47(6): 2100. doi: 10.1093/ije/dyy265
[32]
Zhu Z, Zheng Z, Zhang F, et al. Causal associations between risk factors and common diseases inferred from GWAS summary data[J]. Nat Commun, 2018, 9(1): 224. doi: 10.1038/s41467-017-02317-2
[33]
Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693–698. doi: 10.1038/s41588-018-0099-7
[34]
Mounier N, Kutalik Z. Bias correction for inverse variance weighting Mendelian randomization[J]. Genet Epidemiol, 2023, 47(4): 314–331. doi: 10.1002/gepi.22522
[35]
Carter AR, Sanderson E, Hammerton G, et al. Mendelian randomisation for mediation analysis: current methods and challenges for implementation[J]. Eur J Epidemiol, 2021, 36(5): 465–478. doi: 10.1007/s10654-021-00757-1
[36]
Xin J, Jiang X, Ben S, et al. Association between circulating vitamin E and ten common cancers: evidence from large-scale Mendelian randomization analysis and a longitudinal cohort study[J]. BMC Med, 2022, 20(1): 168. doi: 10.1186/s12916-022-02366-5
[37]
Gómez-Izquierdo J, Filion KB, Boivin JF, et al. Subclinical hypothyroidism and the risk of cancer incidence and cancer mortality: a systematic review[J]. BMC Endocr Disord, 2020, 20(1): 83. doi: 10.1186/s12902-020-00566-9
[38]
Hellevik AI, Asvold BO, Bjøro T, et al. Thyroid function and cancer risk: a prospective population study[J]. Cancer Epidemiol Biomarkers Prev, 2009, 18(2): 570–574. doi: 10.1158/1055-9965.EPI-08-0911
[39]
Angelousi AG, Anagnostou VK, Stamatakos MK, et al. Mechanisms in endocrinology: primary HT and risk for breast cancer: a systematic review and meta-analysis[J]. Eur J Endocrinol, 2012, 166(3): 373–381. doi: 10.1530/EJE-11-0838
[40]
L'Heureux A, Wieland DR, Weng CH, et al. Association between thyroid disorders and colorectal cancer risk in adult patients in Taiwan[J]. JAMA Netw Open, 2019, 2(5): e193755. doi: 10.1001/jamanetworkopen.2019.3755
Knight BA, Shields BM, He X, et al. Effect of perchlorate and thiocyanate exposure on thyroid function of pregnant women from South-West England: a cohort study[J]. Thyroid Res, 2018, 11: 9. doi: 10.1186/s13044-018-0053-x
Sawicka-Gutaj N, Gutaj P, Sowinski J, et al. Influence of cigarette smoking on thyroid gland-an update[J]. Endokrynol Pol, 2014, 65(1): 54–62. https://pubmed.ncbi.nlm.nih.gov/24549603/
[45]
Wang X, Liu X, Li Y, et al. The causal relationship between thyroid function, autoimune thyroid dysfunction and lung cancer: a mendelian randomization study[J]. BMC Pulm Med, 2023, 23(1): 338. doi: 10.1186/s12890-023-02588-0
[46]
Luo J, Martucci VL, Quandt Z, et al. Immunotherapy-mediated thyroid dysfunction: genetic risk and impact on outcomes with PD-1 blockade in non-small cell lung cancer[J]. Clin Cancer Res, 2021, 27(18): 5131–5140. doi: 10.1158/1078-0432.CCR-21-0921
[47]
Wang L, He W, Xu X, et al. Pathological changes and oxidative stress of the HPG axis in hypothyroid rat[J]. J Mol Endocrinol, 2021, 67(3): 107–119. doi: 10.1530/JME-21-0095
[48]
Nanda N, Bobby Z, Hamide A. Oxidative stress and protein glycation in primary hypothyroidism. Male/female difference[J]. Clin Exp Med, 2008, 8(2): 101–108. doi: 10.1007/s10238-008-0164-0
[49]
Peixoto MS, de Vasconcelos ESA, Andrade IS, et al. Hypothyroidism induces oxidative stress and DNA damage in breast[J]. Endocr Relat Cancer, 2021, 28(7): 505–519. doi: 10.1530/ERC-21-0010
[50]
Azad N, Rojanasakul Y, Vallyathan V. Inflammation and lung cancer: roles of reactive oxygen/nitrogen species[J]. J Toxicol Environ Health B Crit Rev, 2008, 11(1): 1–15. doi: 10.1080/10937400701436460
[51]
Reczek CR, Chandel NS. The two faces of reactive oxygen species in cancer[J]. Annu Rev Cancer Biol, 2017, 1: 79–98. doi: 10.1146/annurev-cancerbio-041916-065808
[52]
Kometani T, Yoshino I, Miura N, et al. Benzo[a]pyrene promotes proliferation of human lung cancer cells by accelerating the epidermal growth factor receptor signaling pathway[J]. Cancer Lett, 2009, 278(1): 27–33. doi: 10.1016/j.canlet.2008.12.017
[53]
Weinberg F, Hamanaka R, Wheaton WW, et al. Mitochondrial metabolism and ROS generation are essential for Kras-mediated tumorigenicity[J]. Proc Natl Acad Sci U S A, 2010, 107(19): 8788–8793. doi: 10.1073/pnas.1003428107
[54]
Wu SY, Green WL, Huang W, et al. Alternate pathways of thyroid hormone metabolism[J]. Thyroid, 2005, 15(8): 943–958. doi: 10.1089/thy.2005.15.943
[55]
Cheng SY, Leonard JL, Davis PJ. Molecular aspects of thyroid hormone actions[J]. Endocr Rev, 2010, 31(2): 139–170. doi: 10.1210/er.2009-0007
[56]
Davis PJ, Leonard JL, Davis FB. Mechanisms of nongenomic actions of thyroid hormone[J]. Front Neuroendocrinol, 2008, 29(2): 211–218. doi: 10.1016/j.yfrne.2007.09.003
[57]
Schmohl KA, Müller AM, Wechselberger A, et al. Thyroid hormones and tetrac: new regulators of tumour stroma formation via integrin αvβ3[J]. Endocr Relat Cancer, 2015, 22(6): 941–952. doi: 10.1530/ERC-15-0245
[58]
Schmohl KA, Nelson PJ, Spitzweg C. Tetrac as an anti-angiogenic agent in cancer[J]. Endocr Relat Cancer, 2019, 26(6): R287–R304. doi: 10.1530/ERC-19-0058
[59]
Davis PJ, Mousa SA, Lin HY. Nongenomic actions of thyroid hormone: the integrin component[J]. Physiol Rev, 2021, 101(1): 319–352. doi: 10.1152/physrev.00038.2019
[60]
Mousa SA, Yalcin M, Bharali DJ, et al. Tetraiodothyroacetic acid and its nanoformulation inhibit thyroid hormone stimulation of non-small cell lung cancer cells in vitro and its growth in xenografts[J]. Lung Cancer, 2012, 76(1): 39–45. doi: 10.1016/j.lungcan.2011.10.003
The protein encoded by this gene is involved in cell motility. It is expressed in breast tissue and together with other proteins, it forms a complex with BRCA1 and BRCA2, thus is potentially associated with higher risk of breast cancer. Alternatively spliced transcript variants encoding different isoforms have been noted for this gene.
6.82e−04, 1.11
3.84e−35, 3.22
5.32e−22, 1.20
2.63e−04, 1.63
PAICS
This gene encodes a bifunctional enzyme containing phosphoribosylaminoimidazole carboxylase activity in its N-terminal region and phosphoribosylaminoimidazole succinocarboxamide synthetase in its C-terminal region. It catalyzes steps 6 and 7 of purine biosynthesis. The gene is closely linked and divergently transcribed with a locus that encodes an enzyme in the same pathway, and transcription of the two genes is coordinately regulated. The human genome contains several pseudogenes of this gene. Multiple transcript variants encoding different isoforms have been found for this gene.
4.77e−08, 1.31
5.71e−47, 2.48
2.14e−55, 1.49
1.38e−06, 1.73
ETFDH
This gene encodes a component of the electron-transfer system in mitochondria and is essential for electron transfer from a number of mitochondrial flavin-containing dehydrogenases to the main respiratory chain. Mutations in this gene are associated with glutaric acidemia. Alternatively spliced transcript variants that encode distinct isoforms have been observed.
1.78e−08, −1.47
1.67e−27, −1.92
1.80e−71, −1.85
3.14e−08, −1.78
SCG2
The protein encoded by this gene is a member of the chromogranin/secretogranin family of neuroendocrine secretory proteins. Studies in rodents suggest that the full-length protein, secretogranin II, is involved in the packaging or sorting of peptide hormones and neuropeptides into secretory vesicles. The full-length protein is cleaved to produce the active peptide secretoneurin, which exerts chemotaxic effects on specific cell types, and EM66, whose function is unknown.
Authors and Reviewers
DownLoad:
