A transcription factor signature predicts prognosis of patients with adrenocortical carcinoma

A transcription factor signature predicts prognosis of patients with adrenocortical carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article A transcription factor signature predicts prognosis of patients with adrenocortical carcinoma Jianyu Zhao, Bo Liu, Xiaoping Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-54684/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients. Methods The gene expression profile for ACC patients were downloaded from TCGA and GEO datasets. The univariate Cox analysis was applied to identify survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature. The multivariate analysis was used to reveal the independent prognostic factors. Results We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor. Conclusions Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients. Oncology Cancer Biology Adrenocortical carcinoma TCGA GEO transcription factor prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Background Adrenocortical carcinoma (ACC) is a rare endocrine cancer with an annual incidence of 0.7-2.0 cases per million [ 1 , 2 ]. It usually affects adults aged around 40–50 years and children younger than 10 years [ 3 , 4 ]. Clinical manifestations of ACC include abdominal masses and elevated steroid hormones, and result in overall poor outcomes with five-year survival ranging from 32–45% [ 5 ]. Therefore, it is essential to identify prognostic markers of ACC in order to screen for patients at high risk. Transcription factors (TFs) are regulatory proteins that bind to the promoter sequences of genes and decrease or increase their transcription [ 6 ], and thus control cell differentiation [ 7 ], proliferation [ 8 ] and death [ 9 ]. Not surprisingly, the genes encoding TFs are often aberrantly expressed in human cancers and developmental disorders [ 10 ]. For instance, p53 and c-Myc mutations are correlated with poor clinical outcomes in cancer patients [ 11 – 14 ]. In addition, overexpression of the forkhead box transcription factor FoxP3 is an independent prognostic factor for the overall survival of patients with ovarian cancer [ 15 ], while E2F1 is related to adverse prognosis in patients with non-small cell lung carcinomas [ 16 ]. In recent years, TF-related gene expression signatures, such as that of p53 [ 17 ] and STAT3 [ 18 ], have been identified in several cancers. Snail is overexpressed in numerous ACC patients and associated with decreased survival [ 19 ]. In addition, TGF-β pathway components including GATA-6 and SF-1 are also correlated with poor outcomes in ACC patients [ 20 ]. Therefore, it is worth investigating the correlation between TFs and ACC prognosis, and constructing a prognostic TF signature for predicting patient survival. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets have enabled researchers to correlate clinical outcomes with transcriptomic profiles. To this end, we systematically analyzed the gene expression data of ACC datasets using univariate and multivariate Cox regression models. Based on the survival analysis, we then developed a 13-TF prognostic signature and validated this model in an independent microarray data set from GEO. 2 Materials And Methods 2.1 Data collection and preprocessing A total of 1639 human TFs were identified from a previously published study [ 21 ]. TCGA ACC level 3 RNAseqv2 data (RSEM_genes_normalized file) and corresponding clinical information were downloaded from TCGA database ( https://tcga-data.nci.nih.gov/ ). A total of 79 patients with ACC were included after excluding those lacking complete clinical and survival data. The microarray dataset GSE19776 based on the GPL570 platform was downloaded from GEO ( http://www.ncbi.nlm.nih.gov/geo/ ). The gene expression profiling of 22 patients with ACC were performed using [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. The clinical data included age, gender and tumor stage. Firstly, we obtained 1554 overlapped TFs between TFs in literature and TGCA dataset, and 1508 overlapped TFs between TFs in literature and GEO dataset. A total of 118 TFs were excluded from TGCA dataset by removing lower expression genes. Next, we acquired the common TFs between TGCA and GEO datasets. 2.2 Survival analysis After obtained common TFs between TCGA and GEO datasets, univariate Cox survival analysis was performed using Cox proportional hazards regression model. Only TFs with Cox P < 0.001 and Log Rank P < 0.001 on univariate analysis were incorporated into the Lasso Cox regression analysis. Kaplan-Meier method was used to analyze the correlation between overall survival (OS) and TF expression, and the OS of different patient groups were compared using the Log-Rank test. The “survival” package [ 22 ] in R software was used for survival analysis and the time-dependent ROC (Receiver Operating Characteristic) curve analysis was performed using the “survival ROC” package [ 23 ]. 2.3 Construction of ACC prognostic signature LASSO Cox regression model was widely used for high-dimensional predictor identification. In the present study, OS-associated TFs used to to select the significant TFs associated with the OS of ACC patients according to the coefficient value. These factors were incorporated in the multivariate Cox regression model to construct the ACC prognostic signature. The risk score for each TF-encoding gene was calculated as follows: where n is the number of selected genes, exp i is the expression level of gene i and β i is the coefficient of gene i. 2.4 Gene set enrichment analysis (GSEA) GSEA was performed to analyze the significance of the 13 TFs constituting the prognostic signature using GSEA v2.0.12 ( http://www.broadinstitute.org/gsea ) by computing the enrichment score for each gene set [ 24 ]. The distributions of these TFs against the rank-ordered gene ontology (GO) hallmarks were characterized using GSEA with the default settings. Positive and negative normalized scores indicated enrichment in the high-risk and low-risk groups respectively. 3 Results 3.1 Construction of the TF prognostic signature The scheme for developing the TF signature is outlined in Fig. 1 . After initially identifying 1639 TFs by literature search, the low expressing genes were removed and 1304 common TFs were screened from TCGA and GEO datasets. Univariate regression analysis showed that 23 TFs were correlated with OS (Cox- P < 0.001 and Log-Rank P < 0.001), of which 13 were identified by the Lasso regression analysis, and the risk score was calculated by multivariate cox analysis. The λ value was selected in the LASSO Cox regression analysis when the median of the sum of squared residuals was the smallest (Fig. 2 A). The following survival-related TFs with non-zero coefficients were then screened: CREB3L3 (cAMP-responsive element-binding protein 3-like 3), NR0B1 (nuclear receptor subfamily 0, group B, member 1), CENPA (centromere protein-A), FOXM1 (Forkhead Box M1), E2F2 (E2F transcription factor 2), MYBL2 (v-myb myeloblastosis viral oncogene homolog (avian)-like 2), HOXC11 (homeobox C11), ZIC2 (Zic family member 2), ZNF282 (zinc finger protein 282), DNMT1 (DNA methyltransferase 1), TCF3 (transcription factor 3), ELK4 (ETS transcription factor ELK4) and KLF6 (Krüppel-like factor 6) (Fig. 2 B). Only CREB3L3 and NR0B1 were negatively correlated with the remaining TFs (Fig. 2 C), while CENPA, FOXM1 and E2F2 displayed a strong correlation (Table 1 ). As shown in Fig. 3 , high expression of CREB3L3 (HR = 0.663, Cox P = 5e-05, Log-Rank P = 1.97e-07) and NR0B1 (HR = 0.799, Cox P = 6.93e-05, Log-Rank P = 3.18e-06) were associated with good prognosis, and that of other TFs with poor prognosis (HR > 1, P < 0.001). Table 1 Information of the 13 transcription factors for constructing the prognostic signature Gene symbol Stable ID Gene type Chr position(start-end) CENPA ENSG00000115163 Protein coding 2(26764289–26801067) CREB3L3 ENSG00000060566 Protein coding 19(4153631–4173054) DNMT1 ENSG00000130816 Protein coding 19(10133345–10231286) E2F2 ENSG00000007968 Protein coding 1(23506438–23531233) ELK4 ENSG00000158711 Protein coding 1(205597556–205631962) FOXM1 ENSG00000111206 Protein coding 12(2857681–2877155) HOXC11 ENSG00000123388 Protein coding 12(53973126–53977643) KLF6 ENSG00000067082 Protein coding 10(3775996–3785281) MYBL2 ENSG00000101057 Protein coding 20(43667019–43716495) NR0B1 ENSG00000169297 Protein coding X(30304206–30309598) TCF3 ENSG00000071564 Protein coding 19(1609290–1652615) ZIC2 ENSG00000043355 Protein coding 13(99981784–99986765) ZNF282 ENSG00000170265 Protein coding 7(149195546–149226238) Chr, chromosome 3.2 TF signature risk score can predict prognosis of ACC patients The clinical relevance of these TFs was further assessed by multivariate Cox regression analysis, and the risk score based on their expression levels and coefficients was calculated. The 13-TF risk score classified the patients from TCGA training set into the high- and low-risk groups (Fig. 4 A). Expect for CREB3L3 and NR0B1, all TFs were overexpressed in high-risk group (Fig. 4 B). Furthermore, ACC patients in the high-risk group had significantly shorter survival compared to the low-risk patients (HR = 16.95 (5.02–57.2); Cox P = 5.11e-06; Log Rank P = 2.09e-09) (Fig. 4 C). The sensitivity and specificity of the 13-TF signature was determined using time-dependent receiver operating characteristic (ROC) analysis, and the area under curve (AUC) at all follow-up time points were greater than 0.9 (Fig. 4 D). The predictive model was then validated in a GEO dataset, and as shown in Fig. 5 A, the high-risk group had worse survival compared to the low-risk group. In addition, the AUC values of the signature was greater than 0.75 (Fig. 5 B). Taken together, the 13-TF signature can predict prognosis of ACC patients with high sensitivity and specificity. 3.3 The risk score is an independent prognostic factor of ACC The prognostic relevance of the 13-TF signature was further validated by multivariate Cox regression analysis after normalizing for variables including age, gender and pathological stage. In both the training and validation ACC cohorts, the 13-TF risk score was an independent prognostic factor (Table 2 ). However, no significant correlation was seen between OS and age, gender or pathological stage. The high- and low-risk groups of both training and validation datasets were further divided into subgroups based on age (≤ 50 vs > 50 years), gender (male vs female) and pathological stage (Ⅰ-Ⅱ vs Ⅲ-Ⅳ). As shown in Fig. 6 A and 6 B, patients in the high-risk group had poor prognosis and significantly shorter survival compared to those in the low-risk group regardless of other variables. Thus, the 13-TF signature is an independent prognostic predictor of ACC. Table 2 Cox regression analysis of the correlation between clinicopathological factors and overall survival of ACC patients Variables Group Patients (N) Univariate analysis Patients (N) Multivariate analysis HR (95% CI) P HR (95% CI) P TCGA Risk score Low/High 40/39 16.95(5.02–57.2) 5.11E-06 39/38 22.59(4.55-112.22) 1.38E-04 Age 50 41/38 1.8(0.85–3.82) 1.27E-01 40/37 1.47(0.63–3.45) 3.73E-01 Gender F/M 48/31 1.00(0.47–2.14) 9.99E-01 48/29 1.96(0.79–4.84) 1.46E-01 Stage I-II/III-IV 46/31 6.48(2.71–15.5) 2.72E-05 46/31 1.78(0.72–4.39) 2.13E-01 GSE19776 Risk score Low/High 11/11 16.57(2.02-135.76) 8.89E-03 11/11 18.85(2.09-170.08) 8.89E-03 Age 50 15/7 1.72(0.60–4.88) 3.11E-01 15/7 1.23(0.38–3.97) 7.33E-01 Gender F/M 11/11 1.26(0.46–3.44) 6.50E-01 11/11 0.77(0.26–2.26) 6.32E-01 Stage I-II/III-IV 14/8 1.21(0.44–3.29) 7.12E-01 14/8 1.46(0.51–4.14) 4.81E-01 HR, Hazard ratio; CI, Confidence interval 3.4 The 13-TF signature is associated with cancer-related functions GSEA results showed that four hallmarks including G2M_CHECKPOINT (P = 0.021), E2F_TARGETS (P = 0.023), SPERMATOGENESIS (P = 0.046) and MITOTIC_SPINDLE (P = 0.048) were significantly enriched in high-risk patients, suggesting a mechanistic basis of the prognostic role of 13-TF signature in ACC (Fig. 7 ). 4 Discussion ACC is a rare endocrine cancer with limited therapeutic options and poor clinical outcomes. Studies have increasingly identified molecular diagnostic and prognostic signatures of various cancers by screening multiple databases via high-throughput technologies such as microarrays and next-generation sequencing. Transcription factors (TFs) are often aberrantly expressed in tumors, and correlated with cancer prognosis [ 25 , 26 ]. Recent studies have identified specific TFs that are independent prognostic factors in various cancers [ 15 , 27 , 28 ]. The zinc-finger transcription factor Snail is associated with decreased survival of ACC patients and higher risk of distant metastasis [ 19 ]. However, a TF-related prognostic signature has not yet been identified for ACC. We analyzed the gene expression profiles of ACC patients deposited in GEO and TCGA databases, and constructed a prognostic signature of 13 TFs, including CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4 and KLF6. NR0B1, also known as DAX1, is an atypical orphan nuclear receptor that plays a key role in several cancers [ 29 ]. NR0B1 silencing decreased the in vitro invasiveness of the lung cancer cell line A549 and inhibited xenograft growth without affecting cell proliferation [ 30 ]. In addition, NR0B1 is overexpressed in cervical cancer and promotes cancer cell proliferation via the Wnt/β-catenin pathway [ 31 ]. It is also overexpressed in adrenocortical tumors [ 32 ], ovarian cancer [ 33 ], breast cancer [ 34 ], endometrial cancer [ 35 ] and prostate cancer [ 36 ], although its potential role in ACC has not been investigated. CEPNA is a histone-H3 variant that regulates cell division by establishing kinetochore assembly and ensuring proper centromere segregation, and is associated with cancer progression [ 37 , 38 ]. CENPA expression level is a potential biomarker of poor prognosis in cancer patients [ 39 ]. FOXM1 and E2F2 are the upstream regulators of CENPA and play critical roles in cell cycle progression and tumorigenesis [ 40 , 41 ]. Both TFs can potentially bind to the CENPA promoter sequence indicating that they regulate CENPA transcription [ 42 ]. Previous studies have correlated CENPA with poor prognosis in ACC patients and identified E2F2 as an ACC-related TF [ 43 , 44 ]. Although few studies have associated CREB3L3, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF2, FLK4 and KLF6 with ACC progression [ 45 , 46 ], there is no evidence linking FOXM1 to ACC. We found that high levels of CREB3L3 and NR0B1 were correlated with good prognosis, while that of other TFs were correlated with poor prognosis in the ACC patients. Tumor stage was correlated with OS of patients in the training set but not in the validation set. Furthermore, the 13-TF signature was an independent prognostic factor in both TCGA and GEO datasets. The GSEA results showed that G2M-CHECK POINT and E2F-TARGET were significantly enriched in the high-risk group. The G2/M checkpoint is frequently impaired in cancer cells, which promotes genomic instability and tumorigenesis [ 47 ]. Since the E2F transcription factors regulate DNA replication and are aberrantly expressed in almost all human cancers [ 48 ], targeting E2Fs could be a generic approach in anti-cancer treatment. Conclusions To the best of our knowledge, this report is the first to investigate the cancer-specific TFs and their association with clinical outcomes in ACC patients. The 13-TF signature showed accurate predictive ability and is a promising prognostic biomarker for clinical applications. However, the in-silico results were not validated by PCR or Western blotting. Future studies should focus on validating these survival-related TFs through molecular and functional assays, and determine the mechanistic basis. Abbreviations RCC: adrenocortical carcinoma; TF: transcription factor; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; ROC: Receiver Operating Characteristic; OS: overall survival; CREB3L3: cAMP-responsive element-binding protein 3-like 3; NR0B1: nuclear receptor subfamily 0, group B, member; CENPA: centromere protein-A; FOXM1: Forkhead Box M1; E2F2: E2F transcription factor 2; MYBL2: v-myb myeloblastosis viral oncogene homolog (avian)-like 2; HOXC11: homeobox C11; ZIC2: Zic family member 2; ZNF282: zinc finger protein 282; DNMT1: DNA methyltransferase; TCF3: transcription factor 3; ELK4: ETS transcription factor ELK4; KLF6: Krüppel-like factor 6; GSEA: gene set enrichment analysis. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgements Not applicable. Authors’ contributions In this study, JYZ and XPL designed the study; JYZ and BL processed the bioinformatics data and wrote the manuscript. XPL revised the final manuscript. All authors read and approved the final manuscript. References Bilimoria KY, Shen WT, Elaraj D, Bentrem DJ, Winchester DJ, Kebebew E, Sturgeon C. Adrenocortical carcinoma in the United States: treatment utilization and prognostic factors. Cancer. 2008;113(11):3130–6. Else T, Kim AC, Sabolch A, Raymond VM, Kandathil A, Caoili EM, Jolly S, Miller BS, Giordano TJ, Hammer GD. Adrenocortical carcinoma. Endocr Rev. 2014;35(2):282–326. Koschker AC, Fassnacht M, Hahner S, Weismann D, Allolio B. Adrenocortical carcinoma -- improving patient care by establishing new structures . Experimental and clinical endocrinology & diabetes: official journal . German Society of Endocrinology [and] German Diabetes Association. 2006;114(2):45–51. Rodgers SE, Evans DB, Lee JE, Perrier ND. Adrenocortical carcinoma. Surg Oncol Clin N Am. 2006;15(3):535–53. Icard P, Goudet P, Charpenay C, Andreassian B, Carnaille B, Chapuis Y, Cougard P, Henry JF, Proye C. Adrenocortical carcinomas: surgical trends and results of a 253-patient series from the French Association of Endocrine Surgeons study group. World journal of surgery. 2001;25(7):891–7. Latchman DS. Transcription factors: an overview. Int J Biochem Cell Biol. 1997;29(12):1305–12. Arendt D, Musser JM, Baker CVH, Bergman A, Cepko C, Erwin DH, Pavlicev M, Schlosser G, Widder S, Laubichler MD, et al. The origin and evolution of cell types. Nature reviews Genetics. 2016;17(12):744–57. Bouchard C, Staller P, Eilers M. Control of cell proliferation by Myc. Trends in cell biology. 1998;8(5):202–6. Shaulian E, Karin M. AP-1 as a regulator of cell life and death. Nat Cell Biol. 2002;4(5):E131–6. Jimenez-Sanchez G, Childs B, Valle D. Human disease genes. Nature. 2001;409(6822):853–5. Knezovic Florijan M, Ozretic P, Bujak M, Pezze L, Ciribilli Y, Kastelan Z, Slade N, Hudolin T. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol Oncol. 2019;37(9):578. e571-578 e510. Baker L, Quinlan PR, Patten N, Ashfield A, Birse-Stewart-Bell LJ, McCowan C, Bourdon JC, Purdie CA, Jordan LB, Dewar JA, et al. p53 mutation, deprivation and poor prognosis in primary breast cancer. British journal of cancer. 2010;102(4):719–26. Ren J, Jin F, Yu Z, Zhao L, Wang L, Bai X, Zhao H, Yao W, Mi X, Wang E, et al. MYC overexpression and poor prognosis in sporadic breast cancer with BRCA1 deficiency. Tumour biology: the journal of the International Society for Oncodevelopmental Biology Medicine. 2013;34(6):3945–58. Wolfer A, Wittner BS, Irimia D, Flavin RJ, Lupien M, Gunawardane RN, Meyer CA, Lightcap ES, Tamayo P, Mesirov JP, et al. MYC regulation of a "poor-prognosis" metastatic cancer cell state. Proc Natl Acad Sci USA. 2010;107(8):3698–703. Wolf D, Wolf AM, Rumpold H, Fiegl H, Zeimet AG, Muller-Holzner E, Deibl M, Gastl G, Gunsilius E, Marth C. The expression of the regulatory T cell-specific forkhead box transcription factor FoxP3 is associated with poor prognosis in ovarian cancer. Clinical cancer research: an official journal of the American Association for Cancer Research. 2005;11(23):8326–31. Gorgoulis VG, Zacharatos P, Mariatos G, Kotsinas A, Bouda M, Kletsas D, Asimacopoulos PJ, Agnantis N, Kittas C, Papavassiliou AG. Transcription factor E2F-1 acts as a growth-promoting factor and is associated with adverse prognosis in non-small cell lung carcinomas. J Pathol. 2002;198(2):142–56. Coutant C, Rouzier R, Qi Y, Lehmann-Che J, Bianchini G, Iwamoto T, Hortobagyi GN, Symmans WF, Uzan S, Andre F, et al. Distinct p53 gene signatures are needed to predict prognosis and response to chemotherapy in ER-positive and ER-negative breast cancers. Clinical cancer research: an official journal of the American Association for Cancer Research. 2011;17(8):2591–601. Alvarez JV, Mukherjee N, Chakravarti A, Robe P, Zhai G, Chakladar A, Loeffler J, Black P, Frank DA. A STAT3 Gene Expression Signature in Gliomas is Associated with a Poor Prognosis. Translational oncogenomics. 2007;2:99–105. Waldmann J, Feldmann G, Slater EP, Langer P, Buchholz M, Ramaswamy A, Saeger W, Rothmund M, Fendrich V. Expression of the zinc-finger transcription factor Snail in adrenocortical carcinoma is associated with decreased survival. British journal of cancer. 2008;99(11):1900–7. Parviainen H, Schrade A, Kiiveri S, Prunskaite-Hyyrylainen R, Haglund C, Vainio S, Wilson DB, Arola J, Heikinheimo M. Expression of Wnt and TGF-beta pathway components and key adrenal transcription factors in adrenocortical tumors: association to carcinoma aggressiveness. Pathol Res Pract. 2013;209(8):503–9. Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, Chen X, Taipale J, Hughes TR, Weirauch MT. The Human Transcription Factors. Cell. 2018;172(4):650–65. Therneau TMLT. Package ‘survival’[J] . R Top Doc 2015, 128. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545–50. Ozaki T, Nakagawara A. p53: the attractive tumor suppressor in the cancer research field. J Biomed Biotechnol. 2011;2011:603925. Lutz W, Leon J, Eilers M. Contributions of Myc to tumorigenesis. Biochim Biophys Acta. 2002;1602(1):61–71. Span PN, Manders P, Heuvel JJ, Thomas CM, Bosch RR, Beex LV, Sweep CG. Expression of the transcription factor Ets-1 is an independent prognostic marker for relapse-free survival in breast cancer. Oncogene. 2002;21(55):8506–9. Jiang J, Tang Y, Zhu G, Zheng M, Yang J, Liang X. Correlation between transcription factor Snail1 expression and prognosis in adenoid cystic carcinoma of salivary gland . Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics 2010, 110(6):764–769. Ranhotra HS. The orphan nuclear receptors in cancer and diabetes. J Recept Signal Transduct Res. 2013;33(4):207–12. Oda T, Tian T, Inoue M, Ikeda J, Qiu Y, Okumura M, Aozasa K, Morii E. Tumorigenic role of orphan nuclear receptor NR0B1 in lung adenocarcinoma. Am J Pathol. 2009;175(3):1235–45. Liu XF, Li XY, Zheng PS, Yang WT: DAX1 promotes cervical cancer cell growth and tumorigenicity through activation of Wnt / beta - catenin pathway via GSK3beta . Cell death & disease 2018, 9 (3):339. de Sousa GR, Soares IC, Faria AM, Domingues VB, Wakamatsu A, Lerario AM, Alves VA, Zerbini MC, Mendonca BB, Fragoso MC, et al: DAX1 Overexpression in Pediatric Adrenocortical Tumors: A Synergic Role with SF1 in Tumorigenesis . Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme 2015, 47(9):656–661. Abd-Elaziz M, Akahira J, Moriya T, Suzuki T, Yaegashi N, Sasano H. Nuclear receptor DAX-1 in human common epithelial ovarian carcinoma: an independent prognostic factor of clinical outcome. Cancer Sci. 2003;94(11):980–5. Conde I, Alfaro JM, Fraile B, Ruiz A, Paniagua R, Arenas MI. DAX-1 expression in human breast cancer: comparison with estrogen receptors ER-alpha, ER-beta and androgen receptor status. Breast cancer research: BCR. 2004;6(3):R140–8. Saito S, Ito K, Suzuki T, Utsunomiya H, Akahira J, Sugihashi Y, Niikura H, Okamura K, Yaegashi N, Sasano H. Orphan nuclear receptor DAX-1 in human endometrium and its disorders. Cancer Sci. 2005;96(10):645–52. Tong SJ, Liu J, Wang X, Qu LX. microRNA-181 promotes prostate cancer cell proliferation by regulating DAX-1 expression. Experimental therapeutic medicine. 2014;8(4):1296–300. Vardabasso C, Hasson D, Ratnakumar K, Chung CY, Duarte LF, Bernstein E. Histone variants: emerging players in cancer biology. Cell Mol Life Sci. 2014;71(3):379–404. Athwal RK, Walkiewicz MP, Baek S, Fu S, Bui M, Camps J, Ried T, Sung MH, Dalal Y. CENP-A nucleosomes localize to transcription factor hotspots and subtelomeric sites in human cancer cells. Epigenetics chromatin. 2015;8:2. Sun X, Clermont PL, Jiao W, Helgason CD, Gout PW, Wang Y, Qu S. Elevated expression of the centromere protein-A(CENP-A)-encoding gene as a prognostic and predictive biomarker in human cancers. International journal of cancer. 2016;139(4):899–907. Wang Z, Park HJ, Carr JR, Chen YJ, Zheng Y, Li J, Tyner AL, Costa RH, Bagchi S, Raychaudhuri P. FoxM1 in tumorigenicity of the neuroblastoma cells and renewal of the neural progenitors. Cancer research. 2011;71(12):4292–302. Nakahata AM, Suzuki DE, Rodini CO, Fiuza ML, Okamoto OK. RNAi-mediated knockdown of E2F2 inhibits tumorigenicity of human glioblastoma cells. Oncology letters. 2014;8(4):1487–91. Grant GD, Brooks L 3rd, Zhang X, Mahoney JM, Martyanov V, Wood TA, Sherlock G, Cheng C, Whitfield ML. Identification of cell cycle-regulated genes periodically expressed in U2OS cells and their regulation by FOXM1 and E2F transcription factors. Molecular biology of the cell. 2013;24(23):3634–50. Xia WX, Yu Q, Li GH, Liu YW, Xiao FH, Yang LQ, Rahman ZU, Wang HT, Kong QP. Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA. PeerJ. 2019;7:e6555. Zhang B, Xu ZW, Wang KH, Lu TC, Du Y. Complex regulatory network of microRNAs, transcription factors, gene alterations in adrenocortical cancer. Asian Pacific journal of cancer prevention: APJCP. 2013;14(4):2265–8. Jain M, Rechache N, Kebebew E. Molecular markers of adrenocortical tumors. Journal of surgical oncology. 2012;106(5):549–56. DiFeo A, Martignetti JA, Narla G. The role of KLF6 and its splice variants in cancer therapy. Drug resistance updates: reviews commentaries in antimicrobial anticancer chemotherapy. 2009;12(1–2):1–7. Lobrich M, Jeggo PA. The impact of a negligent G2/M checkpoint on genomic instability and cancer induction. Nature reviews Cancer. 2007;7(11):861–9. Johnson DG, Schneider-Broussard R. Role of E2F in cell cycle control and cancer. Frontiers in bioscience: a journal virtual library. 1998;3:d447–448. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-54684","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":1466789,"identity":"167dc557-62e8-4f40-97f1-fb948bfdd189","order_by":0,"name":"Jianyu Zhao","email":"","orcid":"","institution":"Jilin University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianyu","middleName":"","lastName":"Zhao","suffix":""},{"id":1466790,"identity":"81c9df02-c925-4f6a-8b8a-e539af880be5","order_by":1,"name":"Bo Liu","email":"","orcid":"","institution":"Jilin University First Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Liu","suffix":""},{"id":1466791,"identity":"ddeece45-388f-468a-9290-92e2930c0e73","order_by":2,"name":"Xiaoping Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3RPQrCMBTA8ZQH6RLb1VLQKzwpOEl7lZReICC4KhR6Br1FvUGlQxfRtYNDpeAsCE4OJn6saUfB/Id+wPsRHiHEZPrFQD2QEAegwEb9FH0JBcqR9yLfKGFIehGs4NwKcQozm11FnJGRU3PrJjTES2kQrPGSZDDIUZLAqzn4aw1xgUx9hmVCPyTOa06B6VYA+/4hrFFk2UlcYK9TQjWmCMcu4qVsLncp5RhF5IfhZLM/p76O4LHatuJRRq5bXvC6mI2dKtnddESlbjNeyb3kjQ7lt7XqAG8SqXfTOWoymUx/2RPug0FbSlFZ5AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6463-7922","institution":"Jilin University First Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2020-08-06 11:06:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-54684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-54684/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1996652,"identity":"7a6b1870-447a-4077-abf2-6ee2314a9344","added_by":"auto","created_at":"2020-08-19 21:08:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":552041,"visible":true,"origin":"","legend":"Flowchart describing the construction and validation of the 13-TF prognostic signature.","description":"","filename":"OnlineFigure1.Png","url":"https://assets-eu.researchsquare.com/files/rs-54684/v1/OnlineFigure1.Png"},{"id":1996653,"identity":"51517941-8191-45a4-b3ab-03d74ed70aae","added_by":"auto","created_at":"2020-08-19 21:08:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122890,"visible":true,"origin":"","legend":"Construction of the 13-TF prognostic signature. A, Selection of optimal tuning parameter (λ) in the LASSO model. The dotted vertical lines are drawn at the optimal values by minimum criteria (left) and 1-SE criteria (right). Error bars represent standard error (SE); B, Coefficient of each transcription factor; C, Spearman correlation analysis for the 13 TFs. Red and green respectively represent positive and negative correlation. The size of the point represents the strength of correlation.","description":"","filename":"OnlineFigure2.Png","url":"https://assets-eu.researchsquare.com/files/rs-54684/v1/OnlineFigure2.Png"},{"id":1996654,"identity":"daed7b92-e911-4305-999f-0e909126c7ff","added_by":"auto","created_at":"2020-08-19 21:08:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137469,"visible":true,"origin":"","legend":"Survival curves of ACC patients demarcated on the basis of TF risk score. The median risk score was used as the cutoff value. ACC, Adrenocortical carcinoma; OS, overall survival. ","description":"","filename":"Onlinefigure3.Png","url":"https://assets-eu.researchsquare.com/files/rs-54684/v1/Onlinefigure3.Png"},{"id":1996655,"identity":"7a129099-fd12-439a-9dbd-5e72b4cbc5f9","added_by":"auto","created_at":"2020-08-19 21:08:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84685,"visible":true,"origin":"","legend":"Risk score calculated by the 13-TF signature and Kaplan-Meier survival in TCGA dataset. A, Risk score distribution of the 13-TF signature and patient survival status; B, Heatmap of each TF; C, Kaplan-Meier survival plots; D, Prognostic accuracy of the 13-TF signature. HR, Hazard ratio; AUC, area under the curve.","description":"","filename":"Onlinefigure4.Png","url":"https://assets-eu.researchsquare.com/files/rs-54684/v1/Onlinefigure4.Png"},{"id":1996656,"identity":"ea76b771-56e5-4cdf-b183-5d3fb7658794","added_by":"auto","created_at":"2020-08-19 21:08:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42677,"visible":true,"origin":"","legend":"Kaplan-Meier survival analysis of ACC patients in GEO (Gene Expression Omnibus) validation set. A, Kaplan-Meier plots for OS. B, ROC curve for predicting OS at different time points. HR, hazard ratio; ROC, receiver operating characteristic; AUC, area under the curve.","description":"","filename":"Onlinefigure5.Png","url":"https://assets-eu.researchsquare.com/files/rs-54684/v1/Onlinefigure5.Png"},{"id":1996657,"identity":"3f9971f0-69ea-4532-9c6e-40de41d9a997","added_by":"auto","created_at":"2020-08-19 21:08:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97166,"visible":true,"origin":"","legend":"Clinical characteristics of TCGA and GEO cohorts stratified by the 13-TF risk score. A, TCGA set; B, GEO set. TCGA, The Cancer Genome Atlas; GEO, Gene Expression Omnibus.","description":"","filename":"Onlinefigure6.Png","url":"https://assets-eu.researchsquare.com/files/rs-54684/v1/Onlinefigure6.Png"},{"id":1996658,"identity":"015eabcd-a61a-47e5-93a2-9e90317e7620","added_by":"auto","created_at":"2020-08-19 21:08:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":142188,"visible":true,"origin":"","legend":"Gene set enrichment analysis of 13 survival-related TFs. ","description":"","filename":"Onlinefigure7.Png","url":"https://assets-eu.researchsquare.com/files/rs-54684/v1/Onlinefigure7.Png"},{"id":13579452,"identity":"da31f836-f102-46bb-9258-45daa58ef333","added_by":"auto","created_at":"2021-09-17 04:19:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2593158,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-54684/v1/3479d192-ac10-459a-b670-11e3adc4ffaf.pdf"}],"financialInterests":"","formattedTitle":"A transcription factor signature predicts prognosis of patients with adrenocortical carcinoma","fulltext":[{"header":"1 Background","content":" \u003cp\u003eAdrenocortical carcinoma (ACC) is a rare endocrine cancer with an annual incidence of 0.7-2.0 cases per million [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It usually affects adults aged around 40\u0026ndash;50\u0026nbsp;years and children younger than 10\u0026nbsp;years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Clinical manifestations of ACC include abdominal masses and elevated steroid hormones, and result in overall poor outcomes with five-year survival ranging from 32\u0026ndash;45% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, it is essential to identify prognostic markers of ACC in order to screen for patients at high risk.\u003c/p\u003e \u003cp\u003eTranscription factors (TFs) are regulatory proteins that bind to the promoter sequences of genes and decrease or increase their transcription [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and thus control cell differentiation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], proliferation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and death [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Not surprisingly, the genes encoding TFs are often aberrantly expressed in human cancers and developmental disorders [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, p53 and c-Myc mutations are correlated with poor clinical outcomes in cancer patients [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, overexpression of the forkhead box transcription factor FoxP3 is an independent prognostic factor for the overall survival of patients with ovarian cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while E2F1 is related to adverse prognosis in patients with non-small cell lung carcinomas [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In recent years, TF-related gene expression signatures, such as that of p53 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and STAT3 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], have been identified in several cancers. Snail is overexpressed in numerous ACC patients and associated with decreased survival [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, TGF-β pathway components including GATA-6 and SF-1 are also correlated with poor outcomes in ACC patients [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, it is worth investigating the correlation between TFs and ACC prognosis, and constructing a prognostic TF signature for predicting patient survival.\u003c/p\u003e \u003cp\u003eThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets have enabled researchers to correlate clinical outcomes with transcriptomic profiles. To this end, we systematically analyzed the gene expression data of ACC datasets using univariate and multivariate Cox regression models. Based on the survival analysis, we then developed a 13-TF prognostic signature and validated this model in an independent microarray data set from GEO.\u003c/p\u003e "},{"header":"2 Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Data collection and preprocessing\u003c/h2\u003e\n\u003cp\u003eA total of 1639 human TFs were identified from a previously published study [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. TCGA ACC level 3 RNAseqv2 data (RSEM_genes_normalized file) and corresponding clinical information were downloaded from TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-data.nci.nih.gov/\u003c/span\u003e\u003c/span\u003e). A total of 79 patients with ACC were included after excluding those lacking complete clinical and survival data. The microarray dataset GSE19776 based on the GPL570 platform was downloaded from GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e). The gene expression profiling of 22 patients with ACC were performed using [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. The clinical data included age, gender and tumor stage. Firstly, we obtained 1554 overlapped TFs between TFs in literature and TGCA dataset, and 1508 overlapped TFs between TFs in literature and GEO dataset. A total of 118 TFs were excluded from TGCA dataset by removing lower expression genes. Next, we acquired the common TFs between TGCA and GEO datasets.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Survival analysis\u003c/h2\u003e\n\u003cp\u003eAfter obtained common TFs between TCGA and GEO datasets, univariate Cox survival analysis was performed using Cox proportional hazards regression model. Only TFs with Cox P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and Log Rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 on univariate analysis were incorporated into the Lasso Cox regression analysis. Kaplan-Meier method was used to analyze the correlation between overall survival (OS) and TF expression, and the OS of different patient groups were compared using the Log-Rank test. The \u0026ldquo;survival\u0026rdquo; package [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] in R software was used for survival analysis and the time-dependent ROC (Receiver Operating Characteristic) curve analysis was performed using the \u0026ldquo;survival ROC\u0026rdquo; package [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 Construction of ACC prognostic signature\u003c/h2\u003e\n\u003cp\u003eLASSO Cox regression model was widely used for high-dimensional predictor identification. In the present study, OS-associated TFs used to to select the significant TFs associated with the OS of ACC patients according to the coefficient value. These factors were incorporated in the multivariate Cox regression model to construct the ACC prognostic signature. The risk score for each TF-encoding gene was calculated as follows:\u0026nbsp;\u003cimg style=\"width: 125px;\" src=\"https://myfiles.space/user_files/44073_bbc9ffc5e562ebe7/44073_custom_files/img1597861097.png\" alt=\"\" height=\"24\" /\u003e\u0026nbsp;where n is the number of selected genes, exp\u003csub\u003ei\u003c/sub\u003e is the expression level of gene i and \u0026beta;\u003csub\u003ei\u003c/sub\u003e is the coefficient of gene i.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Gene set enrichment analysis (GSEA)\u003c/h2\u003e\n\u003cp\u003eGSEA was performed to analyze the significance of the 13 TFs constituting the prognostic signature using GSEA v2.0.12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broadinstitute.org/gsea\u003c/span\u003e\u003c/span\u003e) by computing the enrichment score for each gene set [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The distributions of these TFs against the rank-ordered gene ontology (GO) hallmarks were characterized using GSEA with the default settings. Positive and negative normalized scores indicated enrichment in the high-risk and low-risk groups respectively.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Construction of the TF prognostic signature\u003c/h2\u003e \u003cp\u003eThe scheme for developing the TF signature is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After initially identifying 1639 TFs by literature search, the low expressing genes were removed and 1304 common TFs were screened from TCGA and GEO datasets. Univariate regression analysis showed that 23 TFs were correlated with OS (Cox-\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and Log-Rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), of which 13 were identified by the Lasso regression analysis, and the risk score was calculated by multivariate cox analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe λ value was selected in the LASSO Cox regression analysis when the median of the sum of squared residuals was the smallest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The following survival-related TFs with non-zero coefficients were then screened: CREB3L3 (cAMP-responsive element-binding protein 3-like 3), NR0B1 (nuclear receptor subfamily 0, group B, member 1), CENPA (centromere protein-A), FOXM1 (Forkhead Box M1), E2F2 (E2F transcription factor 2), MYBL2 (v-myb myeloblastosis viral oncogene homolog (avian)-like 2), HOXC11 (homeobox C11), ZIC2 (Zic family member 2), ZNF282 (zinc finger protein 282), DNMT1 (DNA methyltransferase 1), TCF3 (transcription factor 3), ELK4 (ETS transcription factor ELK4) and KLF6 (Kr\u0026uuml;ppel-like factor 6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Only CREB3L3 and NR0B1 were negatively correlated with the remaining TFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), while CENPA, FOXM1 and E2F2 displayed a strong correlation (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, high expression of CREB3L3 (HR\u0026thinsp;=\u0026thinsp;0.663, Cox P\u0026thinsp;=\u0026thinsp;5e-05, Log-Rank P\u0026thinsp;=\u0026thinsp;1.97e-07) and NR0B1 (HR\u0026thinsp;=\u0026thinsp;0.799, Cox P\u0026thinsp;=\u0026thinsp;6.93e-05, Log-Rank P\u0026thinsp;=\u0026thinsp;3.18e-06) were associated with good prognosis, and that of other TFs with poor prognosis (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation of the 13 transcription factors for constructing the prognostic signature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene symbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr position(start-end)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCENPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000115163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(26764289\u0026ndash;26801067)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREB3L3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000060566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(4153631\u0026ndash;4173054)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000130816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(10133345\u0026ndash;10231286)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000007968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(23506438\u0026ndash;23531233)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eELK4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000158711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(205597556\u0026ndash;205631962)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFOXM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000111206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(2857681\u0026ndash;2877155)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOXC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000123388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(53973126\u0026ndash;53977643)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKLF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000067082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(3775996\u0026ndash;3785281)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYBL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000101057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(43667019\u0026ndash;43716495)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR0B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000169297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX(30304206\u0026ndash;30309598)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTCF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000071564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(1609290\u0026ndash;1652615)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZIC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000043355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(99981784\u0026ndash;99986765)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZNF282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000170265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(149195546\u0026ndash;149226238)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eChr, chromosome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 TF signature risk score can predict prognosis of ACC patients\u003c/h2\u003e \u003cp\u003eThe clinical relevance of these TFs was further assessed by multivariate Cox regression analysis, and the risk score based on their expression levels and coefficients was calculated. The 13-TF risk score classified the patients from TCGA training set into the high- and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Expect for CREB3L3 and NR0B1, all TFs were overexpressed in high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Furthermore, ACC patients in the high-risk group had significantly shorter survival compared to the low-risk patients (HR\u0026thinsp;=\u0026thinsp;16.95 (5.02\u0026ndash;57.2); Cox \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.11e-06; Log Rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.09e-09) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The sensitivity and specificity of the 13-TF signature was determined using time-dependent receiver operating characteristic (ROC) analysis, and the area under curve (AUC) at all follow-up time points were greater than 0.9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The predictive model was then validated in a GEO dataset, and as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, the high-risk group had worse survival compared to the low-risk group. In addition, the AUC values of the signature was greater than 0.75 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Taken together, the 13-TF signature can predict prognosis of ACC patients with high sensitivity and specificity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The risk score is an independent prognostic factor of ACC\u003c/h2\u003e \u003cp\u003eThe prognostic relevance of the 13-TF signature was further validated by multivariate Cox regression analysis after normalizing for variables including age, gender and pathological stage. In both the training and validation ACC cohorts, the 13-TF risk score was an independent prognostic factor (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, no significant correlation was seen between OS and age, gender or pathological stage. The high- and low-risk groups of both training and validation datasets were further divided into subgroups based on age (\u0026le;\u0026thinsp;50 vs\u0026thinsp;\u0026gt;\u0026thinsp;50\u0026nbsp;years), gender (male vs female) and pathological stage (Ⅰ-Ⅱ vs Ⅲ-Ⅳ). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, patients in the high-risk group had poor prognosis and significantly shorter survival compared to those in the low-risk group regardless of other variables. Thus, the 13-TF signature is an independent prognostic predictor of ACC.\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\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox regression analysis of the correlation between clinicopathological factors and overall survival of ACC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c4\" namest=\"c3\" rowspan=\"2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003e(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c10\" namest=\"c9\" rowspan=\"2\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003e(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCGA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow/High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40/39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e16.95(5.02\u0026ndash;57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.11E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e39/38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e22.59(4.55-112.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e1.38E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=50/\u0026gt;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41/38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.8(0.85\u0026ndash;3.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.27E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e40/37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.47(0.63\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e3.73E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF/M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48/31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00(0.47\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e9.99E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e48/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.96(0.79\u0026ndash;4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e1.46E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI-II/III-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46/31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.48(2.71\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.72E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e46/31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.78(0.72\u0026ndash;4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e2.13E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGSE19776\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow/High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e16.57(2.02-135.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e8.89E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e11/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e18.85(2.09-170.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e8.89E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=50/\u0026gt;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.72(0.60\u0026ndash;4.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.11E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e15/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.23(0.38\u0026ndash;3.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e7.33E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF/M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.26(0.46\u0026ndash;3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.50E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e11/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.77(0.26\u0026ndash;2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e6.32E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI-II/III-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.21(0.44\u0026ndash;3.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.12E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e14/8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e1.46(0.51\u0026ndash;4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e4.81E-01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eHR, Hazard ratio; CI, Confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The 13-TF signature is associated with cancer-related functions\u003c/h2\u003e \u003cp\u003eGSEA results showed that four hallmarks including G2M_CHECKPOINT (P\u0026thinsp;=\u0026thinsp;0.021), E2F_TARGETS (P\u0026thinsp;=\u0026thinsp;0.023), SPERMATOGENESIS (P\u0026thinsp;=\u0026thinsp;0.046) and MITOTIC_SPINDLE (P\u0026thinsp;=\u0026thinsp;0.048) were significantly enriched in high-risk patients, suggesting a mechanistic basis of the prognostic role of 13-TF signature in ACC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"4 Discussion","content":" \u003cp\u003eACC is a rare endocrine cancer with limited therapeutic options and poor clinical outcomes. Studies have increasingly identified molecular diagnostic and prognostic signatures of various cancers by screening multiple databases via high-throughput technologies such as microarrays and next-generation sequencing. Transcription factors (TFs) are often aberrantly expressed in tumors, and correlated with cancer prognosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Recent studies have identified specific TFs that are independent prognostic factors in various cancers [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The zinc-finger transcription factor Snail is associated with decreased survival of ACC patients and higher risk of distant metastasis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, a TF-related prognostic signature has not yet been identified for ACC.\u003c/p\u003e \u003cp\u003eWe analyzed the gene expression profiles of ACC patients deposited in GEO and TCGA databases, and constructed a prognostic signature of 13 TFs, including CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4 and KLF6. NR0B1, also known as DAX1, is an atypical orphan nuclear receptor that plays a key role in several cancers [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. NR0B1 silencing decreased the \u003cem\u003ein vitro\u003c/em\u003e invasiveness of the lung cancer cell line A549 and inhibited xenograft growth without affecting cell proliferation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, NR0B1 is overexpressed in cervical cancer and promotes cancer cell proliferation via the Wnt/β-catenin pathway [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It is also overexpressed in adrenocortical tumors [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], ovarian cancer [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], breast cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], endometrial cancer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and prostate cancer [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], although its potential role in ACC has not been investigated.\u003c/p\u003e \u003cp\u003eCEPNA is a histone-H3 variant that regulates cell division by establishing kinetochore assembly and ensuring proper centromere segregation, and is associated with cancer progression [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. CENPA expression level is a potential biomarker of poor prognosis in cancer patients [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. FOXM1 and E2F2 are the upstream regulators of CENPA and play critical roles in cell cycle progression and tumorigenesis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Both TFs can potentially bind to the CENPA promoter sequence indicating that they regulate CENPA transcription [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Previous studies have correlated CENPA with poor prognosis in ACC patients and identified E2F2 as an ACC-related TF [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Although few studies have associated CREB3L3, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF2, FLK4 and KLF6 with ACC progression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], there is no evidence linking FOXM1 to ACC. We found that high levels of CREB3L3 and NR0B1 were correlated with good prognosis, while that of other TFs were correlated with poor prognosis in the ACC patients. Tumor stage was correlated with OS of patients in the training set but not in the validation set. Furthermore, the 13-TF signature was an independent prognostic factor in both TCGA and GEO datasets.\u003c/p\u003e \u003cp\u003eThe GSEA results showed that G2M-CHECK POINT and E2F-TARGET were significantly enriched in the high-risk group. The G2/M checkpoint is frequently impaired in cancer cells, which promotes genomic instability and tumorigenesis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Since the E2F transcription factors regulate DNA replication and are aberrantly expressed in almost all human cancers [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], targeting E2Fs could be a generic approach in anti-cancer treatment.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo the best of our knowledge, this report is the first to investigate the cancer-specific TFs and their association with clinical outcomes in ACC patients. The 13-TF signature showed accurate predictive ability and is a promising prognostic biomarker for clinical applications. However, the \u003cem\u003ein-silico\u003c/em\u003e results were not validated by PCR or Western blotting. Future studies should focus on validating these survival-related TFs through molecular and functional assays, and determine the mechanistic basis.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eRCC: adrenocortical carcinoma; TF: transcription factor; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; ROC: Receiver Operating Characteristic; OS: overall survival; CREB3L3: cAMP-responsive element-binding protein 3-like 3; NR0B1: nuclear receptor subfamily 0, group B, member; CENPA: centromere protein-A; FOXM1: Forkhead Box M1; E2F2: E2F transcription factor 2; MYBL2: v-myb myeloblastosis viral oncogene homolog (avian)-like 2; HOXC11: homeobox C11; ZIC2: Zic family member 2; ZNF282: zinc finger protein 282; DNMT1: DNA methyltransferase; TCF3: transcription factor 3; ELK4: ETS transcription factor ELK4; KLF6: Kr\u0026uuml;ppel-like factor 6; GSEA: gene set enrichment analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, JYZ and XPL designed the study; JYZ and BL processed the bioinformatics data and wrote the manuscript. XPL revised the final manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eBilimoria KY, Shen WT, Elaraj D, Bentrem DJ, Winchester DJ, Kebebew E, Sturgeon C. Adrenocortical carcinoma in the United States: treatment utilization and prognostic factors. Cancer. 2008;113(11):3130\u0026ndash;6.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eElse T, Kim AC, Sabolch A, Raymond VM, Kandathil A, Caoili EM, Jolly S, Miller BS, Giordano TJ, Hammer GD. Adrenocortical carcinoma. Endocr Rev. 2014;35(2):282\u0026ndash;326.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKoschker AC, Fassnacht M, Hahner S, Weismann D, Allolio B. \u003cb\u003eAdrenocortical carcinoma -- improving patient care by establishing new structures\u003c/b\u003e. \u003cem\u003eExperimental and clinical endocrinology \u0026amp; diabetes: official journal\u003c/em\u003e. German Society of Endocrinology [and] German Diabetes Association. 2006;114(2):45\u0026ndash;51.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRodgers SE, Evans DB, Lee JE, Perrier ND. Adrenocortical carcinoma. Surg Oncol Clin N Am. 2006;15(3):535\u0026ndash;53.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eIcard P, Goudet P, Charpenay C, Andreassian B, Carnaille B, Chapuis Y, Cougard P, Henry JF, Proye C. Adrenocortical carcinomas: surgical trends and results of a 253-patient series from the French Association of Endocrine Surgeons study group. World journal of surgery. 2001;25(7):891\u0026ndash;7.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLatchman DS. Transcription factors: an overview. Int J Biochem Cell Biol. 1997;29(12):1305\u0026ndash;12.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eArendt D, Musser JM, Baker CVH, Bergman A, Cepko C, Erwin DH, Pavlicev M, Schlosser G, Widder S, Laubichler MD, et al. The origin and evolution of cell types. Nature reviews Genetics. 2016;17(12):744\u0026ndash;57.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBouchard C, Staller P, Eilers M. Control of cell proliferation by Myc. Trends in cell biology. 1998;8(5):202\u0026ndash;6.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eShaulian E, Karin M. AP-1 as a regulator of cell life and death. Nat Cell Biol. 2002;4(5):E131\u0026ndash;6.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eJimenez-Sanchez G, Childs B, Valle D. Human disease genes. Nature. 2001;409(6822):853\u0026ndash;5.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKnezovic Florijan M, Ozretic P, Bujak M, Pezze L, Ciribilli Y, Kastelan Z, Slade N, Hudolin T. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol Oncol. 2019;37(9):578. e571-578 e510.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBaker L, Quinlan PR, Patten N, Ashfield A, Birse-Stewart-Bell LJ, McCowan C, Bourdon JC, Purdie CA, Jordan LB, Dewar JA, et al. p53 mutation, deprivation and poor prognosis in primary breast cancer. British journal of cancer. 2010;102(4):719\u0026ndash;26.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRen J, Jin F, Yu Z, Zhao L, Wang L, Bai X, Zhao H, Yao W, Mi X, Wang E, et al. MYC overexpression and poor prognosis in sporadic breast cancer with BRCA1 deficiency. Tumour biology: the journal of the International Society for Oncodevelopmental Biology Medicine. 2013;34(6):3945\u0026ndash;58.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWolfer A, Wittner BS, Irimia D, Flavin RJ, Lupien M, Gunawardane RN, Meyer CA, Lightcap ES, Tamayo P, Mesirov JP, et al. MYC regulation of a \"poor-prognosis\" metastatic cancer cell state. Proc Natl Acad Sci USA. 2010;107(8):3698\u0026ndash;703.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWolf D, Wolf AM, Rumpold H, Fiegl H, Zeimet AG, Muller-Holzner E, Deibl M, Gastl G, Gunsilius E, Marth C. The expression of the regulatory T cell-specific forkhead box transcription factor FoxP3 is associated with poor prognosis in ovarian cancer. Clinical cancer research: an official journal of the American Association for Cancer Research. 2005;11(23):8326\u0026ndash;31.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eGorgoulis VG, Zacharatos P, Mariatos G, Kotsinas A, Bouda M, Kletsas D, Asimacopoulos PJ, Agnantis N, Kittas C, Papavassiliou AG. Transcription factor E2F-1 acts as a growth-promoting factor and is associated with adverse prognosis in non-small cell lung carcinomas. J Pathol. 2002;198(2):142\u0026ndash;56.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eCoutant C, Rouzier R, Qi Y, Lehmann-Che J, Bianchini G, Iwamoto T, Hortobagyi GN, Symmans WF, Uzan S, Andre F, et al. Distinct p53 gene signatures are needed to predict prognosis and response to chemotherapy in ER-positive and ER-negative breast cancers. Clinical cancer research: an official journal of the American Association for Cancer Research. 2011;17(8):2591\u0026ndash;601.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eAlvarez JV, Mukherjee N, Chakravarti A, Robe P, Zhai G, Chakladar A, Loeffler J, Black P, Frank DA. A STAT3 Gene Expression Signature in Gliomas is Associated with a Poor Prognosis. Translational oncogenomics. 2007;2:99\u0026ndash;105.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWaldmann J, Feldmann G, Slater EP, Langer P, Buchholz M, Ramaswamy A, Saeger W, Rothmund M, Fendrich V. Expression of the zinc-finger transcription factor Snail in adrenocortical carcinoma is associated with decreased survival. British journal of cancer. 2008;99(11):1900\u0026ndash;7.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eParviainen H, Schrade A, Kiiveri S, Prunskaite-Hyyrylainen R, Haglund C, Vainio S, Wilson DB, Arola J, Heikinheimo M. Expression of Wnt and TGF-beta pathway components and key adrenal transcription factors in adrenocortical tumors: association to carcinoma aggressiveness. Pathol Res Pract. 2013;209(8):503\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, Chen X, Taipale J, Hughes TR, Weirauch MT. The Human Transcription Factors. Cell. 2018;172(4):650\u0026ndash;65.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eTherneau TMLT. \u003cb\u003ePackage \u0026lsquo;survival\u0026rsquo;[J]\u003c/b\u003e. \u003cem\u003eR Top Doc\u003c/em\u003e 2015, 128.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S + to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102(43):15545\u0026ndash;50.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eOzaki T, Nakagawara A. p53: the attractive tumor suppressor in the cancer research field. J Biomed Biotechnol. 2011;2011:603925.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLutz W, Leon J, Eilers M. Contributions of Myc to tumorigenesis. Biochim Biophys Acta. 2002;1602(1):61\u0026ndash;71.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSpan PN, Manders P, Heuvel JJ, Thomas CM, Bosch RR, Beex LV, Sweep CG. Expression of the transcription factor Ets-1 is an independent prognostic marker for relapse-free survival in breast cancer. Oncogene. 2002;21(55):8506\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eJiang J, Tang Y, Zhu G, Zheng M, Yang J, Liang X. \u003cb\u003eCorrelation between transcription factor Snail1 expression and prognosis in adenoid cystic carcinoma of salivary gland\u003c/b\u003e. \u003cem\u003eOral surgery, oral medicine, oral pathology, oral radiology, and endodontics\u003c/em\u003e 2010, 110(6):764\u0026ndash;769.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRanhotra HS. The orphan nuclear receptors in cancer and diabetes. J Recept Signal Transduct Res. 2013;33(4):207\u0026ndash;12.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eOda T, Tian T, Inoue M, Ikeda J, Qiu Y, Okumura M, Aozasa K, Morii E. Tumorigenic role of orphan nuclear receptor NR0B1 in lung adenocarcinoma. Am J Pathol. 2009;175(3):1235\u0026ndash;45.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLiu XF, Li XY, Zheng PS, Yang WT: \u003cb\u003eDAX1 promotes cervical cancer cell growth and tumorigenicity through activation of Wnt\u003c/b\u003e/\u003cb\u003ebeta\u003c/b\u003e-\u003cb\u003ecatenin pathway via GSK3beta\u003c/b\u003e. \u003cem\u003eCell death \u0026amp; disease\u003c/em\u003e 2018, \u003cb\u003e9\u003c/b\u003e(3):339.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003ede Sousa GR, Soares IC, Faria AM, Domingues VB, Wakamatsu A, Lerario AM, Alves VA, Zerbini MC, Mendonca BB, Fragoso MC, et al: \u003cb\u003eDAX1 Overexpression in Pediatric Adrenocortical Tumors: A Synergic Role with SF1 in Tumorigenesis\u003c/b\u003e. \u003cem\u003eHormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme\u003c/em\u003e 2015, 47(9):656\u0026ndash;661.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eAbd-Elaziz M, Akahira J, Moriya T, Suzuki T, Yaegashi N, Sasano H. Nuclear receptor DAX-1 in human common epithelial ovarian carcinoma: an independent prognostic factor of clinical outcome. Cancer Sci. 2003;94(11):980\u0026ndash;5.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eConde I, Alfaro JM, Fraile B, Ruiz A, Paniagua R, Arenas MI. DAX-1 expression in human breast cancer: comparison with estrogen receptors ER-alpha, ER-beta and androgen receptor status. Breast cancer research: BCR. 2004;6(3):R140\u0026ndash;8.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSaito S, Ito K, Suzuki T, Utsunomiya H, Akahira J, Sugihashi Y, Niikura H, Okamura K, Yaegashi N, Sasano H. Orphan nuclear receptor DAX-1 in human endometrium and its disorders. Cancer Sci. 2005;96(10):645\u0026ndash;52.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eTong SJ, Liu J, Wang X, Qu LX. microRNA-181 promotes prostate cancer cell proliferation by regulating DAX-1 expression. Experimental therapeutic medicine. 2014;8(4):1296\u0026ndash;300.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eVardabasso C, Hasson D, Ratnakumar K, Chung CY, Duarte LF, Bernstein E. Histone variants: emerging players in cancer biology. Cell Mol Life Sci. 2014;71(3):379\u0026ndash;404.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eAthwal RK, Walkiewicz MP, Baek S, Fu S, Bui M, Camps J, Ried T, Sung MH, Dalal Y. CENP-A nucleosomes localize to transcription factor hotspots and subtelomeric sites in human cancer cells. Epigenetics chromatin. 2015;8:2.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSun X, Clermont PL, Jiao W, Helgason CD, Gout PW, Wang Y, Qu S. Elevated expression of the centromere protein-A(CENP-A)-encoding gene as a prognostic and predictive biomarker in human cancers. International journal of cancer. 2016;139(4):899\u0026ndash;907.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eWang Z, Park HJ, Carr JR, Chen YJ, Zheng Y, Li J, Tyner AL, Costa RH, Bagchi S, Raychaudhuri P. FoxM1 in tumorigenicity of the neuroblastoma cells and renewal of the neural progenitors. Cancer research. 2011;71(12):4292\u0026ndash;302.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eNakahata AM, Suzuki DE, Rodini CO, Fiuza ML, Okamoto OK. RNAi-mediated knockdown of E2F2 inhibits tumorigenicity of human glioblastoma cells. Oncology letters. 2014;8(4):1487\u0026ndash;91.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eGrant GD, Brooks L 3rd, Zhang X, Mahoney JM, Martyanov V, Wood TA, Sherlock G, Cheng C, Whitfield ML. Identification of cell cycle-regulated genes periodically expressed in U2OS cells and their regulation by FOXM1 and E2F transcription factors. Molecular biology of the cell. 2013;24(23):3634\u0026ndash;50.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eXia WX, Yu Q, Li GH, Liu YW, Xiao FH, Yang LQ, Rahman ZU, Wang HT, Kong QP. Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA. PeerJ. 2019;7:e6555.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eZhang B, Xu ZW, Wang KH, Lu TC, Du Y. Complex regulatory network of microRNAs, transcription factors, gene alterations in adrenocortical cancer. Asian Pacific journal of cancer prevention: APJCP. 2013;14(4):2265\u0026ndash;8.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eJain M, Rechache N, Kebebew E. Molecular markers of adrenocortical tumors. Journal of surgical oncology. 2012;106(5):549\u0026ndash;56.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eDiFeo A, Martignetti JA, Narla G. The role of KLF6 and its splice variants in cancer therapy. Drug resistance updates: reviews commentaries in antimicrobial anticancer chemotherapy. 2009;12(1\u0026ndash;2):1\u0026ndash;7.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLobrich M, Jeggo PA. The impact of a negligent G2/M checkpoint on genomic instability and cancer induction. Nature reviews Cancer. 2007;7(11):861\u0026ndash;9.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eJohnson DG, Schneider-Broussard R. Role of E2F in cell cycle control and cancer. Frontiers in bioscience: a journal virtual library. 1998;3:d447\u0026ndash;448.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adrenocortical carcinoma, TCGA, GEO, transcription factor, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-54684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-54684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe gene expression profile for ACC patients were downloaded from TCGA and GEO datasets. The univariate Cox analysis was applied to identify survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature. The multivariate analysis was used to reveal the independent prognostic factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTaken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.\u003c/p\u003e","manuscriptTitle":"A transcription factor signature predicts prognosis of patients with adrenocortical carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-19 21:08:22","doi":"10.21203/rs.3.rs-54684/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ab9a5c9-f395-4d0a-824c-9b6413fe5822","owner":[],"postedDate":"August 19th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":327583,"name":"Oncology"},{"id":327584,"name":"Cancer Biology"}],"tags":[],"updatedAt":"2021-04-30T23:04:46+00:00","versionOfRecord":[],"versionCreatedAt":"2020-08-19 21:08:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-54684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-54684","identity":"rs-54684","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}