Investigation on factors associated with ovarian cancer: an umbrella review of systematic review and meta-analyses

A systematic review of systematic reviews was conducted to identify the associated factors with OC. This study was performed according to Smith et al. methodology for conducting a systematic review of systematic reviews [ 7 ]. What are the most important factors associated with ovarian cancer found in systematic reviews? A comprehensive systematic literature search was performed to identify all published systematic reviews and meta-analysis on associated factors with OC. Medline through PubMed, Scopus, Embase, Web of Science, Cochrane Library databases, and Google Scholar all were searched up to 17th January 2020 without time limitation. The search strategy included the use of Mesh terms and keywords related to subject and study design (ovarian; ovary; cancer; carcinoma; neoplasm; tumor; Malignancy; review; systematic review; systematic literature review; meta-analysis). The detailed search strategy for the Medline can be found in the supplementary, Table  1  S. The reference lists of selected articles were also manually searched to identify any additional related documents. This overview only included systematic reviews of factors associated with OC. The articles which met the following criteria were included in our study: (1) systematic reviews or meta-analysis; (2) have evaluated risk factors of Ovarian cancer; (3) have at least abstracts in English. The articles that were narrative reviews or had assessed prognostic factors of OC or did not provide at least abstract in English were excluded. Characteristic of included studies are illustrate in Table  1 . Table 1  Characteristic of included studies No. Author Year No. of Articles No. of Patient (total) No. of Cases No. of Control Evaluated Factors 1 Yan Qiao 2018 21 309 - - Aspirin 2 Hongmei Chen 2017 14 11,690 4448 7242 VDR rs2228570 3 Li-Hui Yan 2018 46 84,772 36,298 48,474 BRCA2 N372H 4 Jie Ruan 2018 24 1217 - - P16INK4a 5 liang Tang 2018 13 13,064 5461 7603 HER2 and ESR2 polymorphisms 6 Ross Penninkilampi 2018 27 - 14,311 - Talc Use 7 Chao-Huan Xu 2017 7 3016 1,345 1,671 Genetic polymorphisms 8 Xu-Ming Zhu 2017 10 4621 1930 2464 Genetic polymorphisms 9 JieNa Li 2017 9 4024 1333 2691 ERCC2 rs13181 10 Jing Li 2017 7 - 1898 - C-reactive protein 11 Dongyu Zhang 2017 14 2,342,245 4184 ‬‬ Diabetes mellitus 12 Xingxing Song 2017 15 493,415 7453 485,962 Calcium Intake 13 Wera Berge 2016 27 34,176 15,154 19,022 Talc Use 14 Xin Zhan 2017 18 701,857 8,683 693,174 Tea consumption 15 A Darelius 2017 11 - - - Hysterectomy 16 Zhiyi Zhou 2017 13 2,951,539 13,616 2,937,923 Pelvic inflammatory disease 17 Yang Deng 2017 8 14,014 6613 7401 Androgen receptor gene 18 Bamia Christina 2016 32 - 11,411 - Coffee Intake 19 Lihua Wang 2017 13 3,708,313 5534 3,702,779 Diabetes mellitus 20 lilin he 2017 8 45,624 19,260 26,364 MTHFR C677T 21 Chunpeng Wang 2016 38 409,061 40,609 368,452 Endometriosis, Tubal Ligation, Hysterectomy 22 Chunyan Shen 2016 12 1235 806 429 Adenomatous polyposis coli (APC) gene 23 Xiyue Xiao 2016 12 901 612 289 P16INK4a 24 Fangfang Zeng 2016 7 33,456 2011 31,445 Inflammatory markers 25 Dongyu Zhang 2016 23 499,950 15,163 484,787 Aspirin 26 Wenlong Qiu 2016 25 900,000 6612 893,388 Dietary fat intake 27 Qiang Wang 2016 9 740 485 255 CDH1 promoter 28 Xiaoli Hua 2016 12 2,361,494 6,275 2,355,219 Dietary Flavonoids 29 Li-feng Shi 2015 12 2,353,945 8896 2,345,049 Hormone therapy 30 Christos Iavazzo 2016 4 725 385 340 Hypodontia 31 Sang-Hee Yoon 2016 3 5,659,211 3509 5,655,702 salpingectomy 32 Wei Liu 2016 35 42,650 19,527 23,123 A1298C POLYMORPHISM 33 Vida Mohammadi 2019 7 381,810 3653 378,157 flavonoids 34 Lifeng Li 2016 9 - - - Metformin 35 Arefe Parvaresh 2019 13 - - - Quercetin 36 Xiaowei Yu 2016 14 11,471 3796 7675 ERCC2 rs13181 - XRCC2 rs3218536 37 Rui Hou 2015 20 1,117,992 12,046 1,105,946 Dietary fat 38 Zhen Liu 2015 26 34,817 12,963 21 854 overweight, obesity 39 N. Keum 2015 18 - 2636 - Egg intake 40 Liangxiang Su 2015 4 12,016 2344 9672 BRCA2 N372H 41 Sai-tian Zeng 2014 12 629,453 3728 625,725 Egg intake 42 Xiaolian Zhang 2015 5 4233 1791 2,196 Vascular Endothelial Polymorphisms 43 Li-Ping Feng 2014 19 469,095 9438 459,657 Breastfeeding 44 collaborative Group 2015 52 12,110 - - Menopausal hormone use 45 Huang Yan-Hong 2015 13 1,996,841 5857 1,990,984 alcohol consumption 46 Jiyi Hu 2015 8 305,338 3555 301,783 cruciferous vegetables 47 Jing Liao 2014 21 3117 2842 4305 progesterone receptor Polymorphisms 48 Xingzhong Hu 2015 5 5884 2336 3548 RAD51 Gene 135G/C 49 Jing Liu 2014 19 - - - Milk, Yogurt, and Lactose Intake 50 Jun Qin 2014 62 92,857 42,315 50,542 STK15 polymorphisms 51 Luliang Liu 2015 15 14,798 7,450 7,348 MMP-12-82 A/G polymorphism 52 X.Y. Shi 2015 3 7026 - - MTHFR A1298C polymorphism 53 M. Zhai 2015 4 10,169 3565 6604 Arg188His polymorphism 54 Yue-Dong Wang 2014 15 1653 822 831 serum levels of osteopontin 55 John A. Barry 2014 3 72,973 919 72,054 polycystic ovary syndrome 56 Xinli Li 2014 10 72,054 6127 65,927 dietary lycopene intake 57 Xue Qin 2014 4 1133 474 659 Asn680Ser polymorphism 58 Shujing Shi 2014 13 16,230 5,927 10,303 RAD51 135 G>C and XRCC2 G>A (rs3218536) 59 M. A. Alqumber 2014 12 2257 993 1264 72 Arg.Pro Polymorphism 60 Pei-yue Jiang 2014 15 889,033 6,087 882,946 Fish Intake 61 Danhua Pu 2014 7 7356 3493 3863 MTHFR Polymorphism 62 Xinwei Pan 2013 8 7724 3,723 4,001 Ala222Val 63 Yulan Yan 2013 4 9108 3,635 5,473 XRCC3 Thr241Met polymorphism 64 Tracy E. Crane 2013 24 519,431 2091 517,340 Dietary Intake 65 Su Li 2014 14 10,964 - - VDR rs2228570 66 Dan Cheng 2014 22 15,343 6836 8507 RAD51 Gene 135G/C polymorphism 67 Bo Han 2014 11 379,868 4,306 375,562 Cruciferous vegetables 68 Xin-Lan Qu 2014 10 297,892 4392 293,500 Phytoestrogen Intake 69 Jin-Ze Du 2014 8 3940 1,293 2,647 COMT rs4680 Polymorphism 70 Li-Yuan Han 2014 10 6001 2578 3423 GST Genetic Polymorphisms 71 Da-Peng Li 2014 40 415,949 17,139 398,810 Breastfeeding 72 Yong-Jun Ma 2014 6 3839 1,766 2,073 Rs11615 (C>T) 73 Jalal Poorolajal 2014 19 - - - BMI 74 Li-Min Zhou 2014 6 435,398 2983 432,415 Recreational Physical Activity 75 Piyemeth Dilokthornsakul 2013 4 - - - Metformin 76 Chenglin Li 2013 18 227,859 5677 222,182 Folate intake and MTHFR polymorphism C677T 77 Susan J. Jordan 2013 22 - - - hysterectomy 78 Nan-Nan Luan 2013 35 720,617 14,465 706,152 Breastfeeding 79 Xue Qin 2013 7 4,809 1977 2832 VDR 80 Laura J. Havrilesky 2013 55 31,056 10,031 21,025 Oral Contraceptive 81 Ting-Ting Gong 2012 27 1,020,516 9859 1,010,657 Age at menarche 82 Yanling Liu 2013 6 10,768 4,107 6,661 VDR 83 Louise Baandrup 2012 21 563,976 11,759 552,217 NSAIDs 84 Jung-Yun Lee 2012 19 - - - Diabetes Mellitus 85 Chengbin Ma 2013 10 18, 628 5, 932 12,696 MTHFR C677T polymorphism 86 Ying-Yu Ma 2013 6 3745 1534 2211 MDM2 309T.G Polymorphism 87 Gwan Gyu Song 2013 12 8775 3716 5059 VDR 88 Ketan Gajjar 2012 5 3795 1199 2596 Cytochrome P1B1 (CYP1B1) 89 Xiaojian Ni 2012 17 193,424 10 373 183,051 NSAIDs 90 Lu Liu 2012 4 7127 3,496 3,631 C677T and A1298C polymorphism 91 T.N. Sergentanis 2012 11 5025 1,680 3345 MspI and Ile462-Val and Thr461Asn 92 Collaborative Group 2012 51 123,056 28 114 94,942 Smoking 93 Megan S Rice 2012 30 18,929 - - Tubal ligation and Hysterectomy 94 Matteo Rota 2012 27 15,762,134 16,554 15,745,580 Alcohol drinking 95 Collaborative Group 2012 47 106,468 25,157 81,313 Body Size 96 Su-Qin Shen 2012 18 7368 2,193 5,175 TP53 Arg72Pro 97 Xiao-Ping Ding 2012 8 7457 3,379 4,078 MTHFR C677T Polymorphism 98 M.G.M. Braem 2011 150 - - - Genetic variants 99 M. Constanza Camargo 2011 18 21,973 117 22,090 Asbestos 100 David Cibula 2011 3 - - - Oral contraceptives 101 Sarah J. Oppeneer 2011 16 - 7234 - Tea Consumption 102 Lu Yin 2011 10 157,292 - - Circulating vitamin D 103 A Wallin 2011 8 754 836 2349 752,487 Red and processed meat consumption 104 D. Cibula 2011 13 - - - Tubal ligation 105 Ru-Yan Liao 2010 4 15,104 5532 9572 TGFBR1*6A/9A polymorphism 106 Linda S. Cook 2010 20 - - - vitamin D 107 K. P. Economopoulos (2010) 2010 2 4240 2049 2191 Meat, fish 108 Hee Seung Kim 2010 10 135,871 65,578 70,293 Wine 109 S-K Myung 2009 7 169 051 3516 165 535 Soy intake 110 BG Chittenden 2009 1 4547 476 4071 Polycystic ovary syndrome 111 Bo Zhou 2008 27 1,584,610 12,955 1,571,655 Hormone replacement therapy 112 HG Mulholland 2008 2 - - - Dietary glycemic index 113 Catherine M. Olsen 2007 12 2778 1269 1509 Recreational Physical Activity 114 J Steevens 2007 21 - 280 - Tea and coffee drinking 115 C. M. Greiser 2007 42 48,153 12 238 ‬‬ Menopausal hormone therapy 116 Catherine M. Olsen 2007 28 1,640,615 53,182 1,587,433 Obesity 117 S. J. Jordan 2006 9 6474 910 5564 smoking 118 Stefanos Bonovas 2005 8 746,293 ‬‬ 741,888 Paracetamol 119 Susanna C. Larsson 2006 21 - - - Milk, milk products and lactose intake 120 Grimes DA 2009 3 500 - - Oral contraceptives 121 Stefanos Bonovas 2005 10 320,544 3803 316,741 Nonsteroidal anti-inflammatory drugs 122 L-Q Qin 2005 22 134,406 8372 126,034 Milk/dairy products consumption 123 Sonya Kashyap 2004 10 13,480 3624 9856 Assisted Reproductive Technology 124 M. Huncharek 2003 16 11,933 - - Cosmetic talc 125 V Bagnardi 2001 235 117 471 235 ‬‬ Alcohol drinking 126 Michael Huncharek 2009 8 6,689 2529 4160 Dietary Fat Intake 127 S. S. Coughlin 2000 15 - - - Estrogen replacement therapy 128 Pushkal P. Garg 1998 9 259,794 4392 255,402 Hormone replacement therapy 129 John F. Stratton 1998 15 - 6077 - Family history 130 Bowen Zheng 2018 13 142,189 5777 136,412 Dietary fiber intake 131 Hai-Fang Wang 2017 22 1,485,988 - - Empirically derived dietary patterns 132 Hui Xu 2018 19 567,742 - - Dietary fiber intake 133 Dongyu Zhang 2018 14 180,833 7500 ‬‬ Non-herbal tea consumption 134 Yun-Long Huo 2018 6 81,791 7878 73,913 antidepressant medication 135 Massimiliano Berretta 2018 9 787,076 3,541 ‬‬ Coffee consumption 136 Jiaqi Li 2018 7 65,754 - - vitamin D receptor 137 Xianling Zeng 2018 11 9987 4097 5890 RAD51 135 G/C polymorphism 138 Marieke GM Braem 2012 3 330,849 1244 329,605 Coffee and tea consumption 139 Shanliang Zhong 2014 19 730,703 9,459 ‬‬ Nonoccupational physical activity 140 Xiumin Huang 2018 17 149,177 7609 73,168 dietary fiber intake 141 Ting Liu 2013 17 16,363 6,365 9,998 Progesterone receptor PROGINS 142 Yanyang Pang 2018 10 2354 - - Dietary protein intake 143 Ke Wei Foong 2017 43 3,491,943 - - Obesity 144 Lingling Zhou 2015 2 774 389 385 SNP rs763110 145 Rizzuto I 2013 25 182,972 - - ovarian stimulating drugs for infertility 146 Yanqiong Liu 2014 5 624 - - Statin 147 Ahmad Sayasneh 2011 8 - 653 - Endometriosis 148 Jia li 2018 25 957,152 - - Endometriosis 149 Ho Kyung Sung 2016 32 530,950 7639 523,311 Breastfeeding 150 Mahdieh Kamali 2017 17 10,817 4464 6353 XRCC2 rs3218536 151 Menelaos Zafrakas 2014 16 - 17,445 - Endometriosis 152 Dagfinn Aune 2015 28 - - - Anthropometric factors 153 QIAO WANG 2015 4 1985 627 1358 circulating insulin 154 Yihua Yin 2013 11 6192 2,673 3519 glutathione S-transferase 155 Ximena Gianuzzi 2016 14 8130 1,149 6981 Insulin growth factor (IGF) 156 Li-Ling Liu 2014 4 2675 1073 1602 transforming growth factor b receptor 157 Yong-qiang Wang 2012 4 580,581 2444 578,137 TGFBR1 Polymorphisms 158 Dongyang Li 2018 44 1,082,092 48,345 1,033,747 Dietary inflammatory index 159 Si Huang 2018 10 4605 2394 2211 miR-502-binding site 160 Eileen Deuster 2017 200 - - - VDR 161 Ru Chen 2017 28 3362 2,171 1191 MGMT Promoter 162 Joanna Kruk 2017 26 - - - Dietary alkylresorcinols 163 Xue-Feng Li 2017 11 33,209 14,030 19,179 lncRNA H19 polymorphisms 164 Yan Jiang 2017 1 285 165 120 ARLTS1 polymorphism 165 Qiuyan Li 2017 7 - - - BRCA2 rs144848 polymorphism 166 Mohamed Hosny Osman 2017 1 2,116,029 7124 2,108,905 Cardiac glycosides 167 Erjiang Zhao 2017 4 - - - Glutathione S-transferase 168 Giuseppe Grosso 2017 4 - - - Diet 169 Limin Miao 2017 6 6027 2156 3871 BRCA1 P871L polymorphism 170 Na-Na Yang 2017 4 2110 944 1166 XRCC1 polymorphism 171 Giuseppe Grosso 2016 53 - - - Dietary flavonoid 172 Juan Enrique Schwarze 2017 4 - - - Reproduction technologies 173 Rosanne M. Kho 2016 10 - - - Hysterectomy 174 K Robinson 2016 11 - - - Bisexual 175 Hong-Bae Kim 2016 6 1937 - - Benzodiazepine 176 Chuanjie Zhang 2017 3 2628 1276 1352 NFκB1-94ins/del ATTG 177 Minjie Chu 2016 2 18,540 6,857 11,683 H19 lncRNA 178 Duan Wang 2016 4 3036 1463 1573 NFKB1 −94 ins/del ATTG 179 Jun Wang 2016 19 3,87,71,388 13,116 38,758,272 BMI 180 Yun-Feng Zhang 2015 1 549 229 320 IL-27 Genes 181 Ping Wang 2016 2 - - - MDM2 SNP285 182 Wenkai Xia 2015 4 1248 497 751 ESR2 183 Lei Chen 2016 2 - - - L55M polymorphism 184 Davide Serrano 2015 3 5456 2313 3143 VDR 185 Ranadip Chowdhury 2015 41 - - - Breastfeeding 186 Zhi-Ming Dai 2015 3 3530 1475 2055 VDR 187 Claudio Pelucchi 2014 4 - 2,010 - Dietary acrylamide 188 Yu-Fei Zhang 2015 6 619 714 2933 ‬‬ Tea consumption 189 Jin-Lin Cao 2015 2 9245 3102 6143 TERT Genetic Polymorphism 190 Myung-Jin Muna 2015 6 ‬‬ 4107 6661 VDR 191 NaNa Keum 2015 6 - - - Weight Gain 192 Sheng-Song Chen 2015 2 1185 556 629 MMP-12 82 A/G polymorphism 193 Bei-bei Zhang 2014 45 57,328 28,956 28,372 Genetic 135G/C polymorphism 194 Sara Raimondi 2014 5 97,275 45,218 52,057 BsmI polymorphism 195 Shang Xie 2014 15 11,644 5873 5771 LIG4 gene polymorphisms 196 Wen-Qiong Xue 2014 4 ‬‬ 36,299 48,483 BRCA2 N372H 197 Patrizia Gnagnarella 2014 6 10,588 4051 6537 VDR 198 Peter Boyle 2014 2 - - - Sweetened carbonated beverage consumption 199 Tara M. Friebel 2014 5 - - - BRCA1 and BRCA2 200 Xin Wang 2014 41 42,121 17,814 24,307 FAS rs2234767G/A Polymorphism 201 Yeqiong Xu 2013 7 11,009 4210 6799 VDR 202 H S Kim 2014 35 444 255 - - Endometriosis 203 Yazhou He 2014 7 69,524 30,868 38,656 XRCC2 Arg188His Polymorphismc 204 Weifeng Tang 2014 14 27,269 11,245 16,024 Aurora-A V57I (rs1047972) Polymorphism 205 Yeqiong Xu 2014 3 937 457 480 Polymorphisms 206 Mengmeng Zhao 2014 42 39,505 19,142 20,363 Rad51 G135C 207 Xiao Yang 2014 21 ‬‬ 6127 9238 NFKB1 −94ins/del ATTG Promoter 208 Bai-Lin Zhang 2014 7 - 9956 - Blood Groups 209 Ursula Schwab 2014 - - - - Dietary fat on cardiometabolic 210 Tie-Jun Liang 2013 21 8720 3,498 5,222 137G>C polymorphism 211 Wei Wang 2013 39 41,698 19,068 22,630 RAD51 135 G.C Polymorphism 212 Lei Xu 2013 47 43,295 19,810 23,485 FASL rs763110 Polymorphism 213 Jingxiang Chen 2013 19 48,670 14,814 33,856 TCF7L2 Gene Polymorphism 214 Monica Franciosi 2013 53 1,050,984 - - Metformin 215 Zhou Zhong-Xing 2013 41 42,169 17,858 24,311 FAS-1377 G/A (rs2234767) Polymorphism 216 Zhibin Yu 2013 73 38,278 15,942 22,336 Interleukin 10 - 819 C/T Polymorphism 217 Shangqian Wang 2013 2 1706 794 912 PAI-1 4G/5G Polymorphism 218 Li Li Li 2013 8 746,455 - - Fertilization 219 XIN XU 2012 21 17,623 8,415 9,208 PAI-1 promoter 220 Dominique Trudel 2012 22 - - - Green tea 221 Tian-Biao Zhou 2012 6 2,658 1,461 1,197 Gene Polymorphism 222 Xin-Min Pan 2011 17 27,759 13 691 14 068 MLH1 -93 G>A polymorphism 223 Jane Green 2011 - - 4830 - Height 224 C. Pelucchi 2011 3 - 1594 - Acrylamide 225 Bo Peng 2010 4 1240 443 797 Polymorphisms 226 Bahi Takkouche 2009 10 - - - Hairdressers 227 Bahi Takkouche 2005 2 556 238 318 Hair Dyes 228 V. G. Kaklamani 2003 1 907 659 248 TGFBR1*6A 229 Song Mao 2018 3 - - - klotho expression 230 Mukete Franklin Sona 2018 15 1 915 179 31 893 1,911,045 Type 1 diabetes mellitus 231 Christine Schwarz 2018 4 - - - Night shift work 232 Xiaoqing Shi 2018 - 1208 604 604 NME1 polymorphisms 233 H.J. van der Rhee 2006 2 - - - Sunlight 234 Nadin Younes 2018 44 - 805 - Polymorphisms 235 Yue Xu 2016 1 - - - BHMT gene rs3733890 236 Zhong Tian 2013 46 51,413 22,993 28,420 CYP1A2*1F polymorphism 237 Yu Wang 2018 1 79,988 - - Renal transplants 238 T. O. Yang 2014 - 453 023 2009 451,014 Birth weight 239 Lanhua Tang 2017 - - - - Night work 240 Steven M. Koehler 2012 8 - - - BMP-2 241 Yan Zhang 2013 9 5632 2,331 3,301 VDR 242 Ivana Rizzuto 2013 25 182,972 - - Stimulating drugs for infertility 243 Xiao-san Zhang 2018 7 105,507 6783 98,724 Bisphosphonates use 244 Yun Ye 2018 10 1045 - - B7-H4 expression 245 Junga Lee 2018 34 - - - Physical activity 246 Huijun Yang 2019 26 1,174,527 11 410 1 163 117 Age at menarche 247 M. Kadry Taher 2019 27 214,447 15,303 199,144 Perineal use of talc powder 248 Yanjun Wu 2019 13 2,471,030 19,959 2,451,071 Age at last birth 249 A. Moazeni-Roodi 2019 19 37,036 13,562 23,474 MDM2 40 bp indel polymorphism 250 Fateme Shafiei (2018) 2019 22 40 140 8568 31,572 Caffeine 251 Lindsay J. Wheeler 2019 11 13,591 4,484 9,107 Intrauterine Device Use 252 Yuhang Long 2019 16 437,689 4,553 433,136 vitamin C intake 253 M. Arjmand (2020) 2019 16 4184 1106 3078 Circulating omentin levels 254 Claudia Santucci 2019 37 - 70,646 - smoking 255 A. Salari-Moghaddam 2019 14 - 4434 - Caffeine 256 M. Karimi-Zarchi 2019 11 12,720 4990 7730 MTHFR 677 C>T Polymorphism 257 Fan Yang 2019 2 445 - - ERCC1 gene polymorphisms 258 Tingting Yang 2019 3 - - - Work Stress 259 Youxu Leng 2019 14 - 4597 - vitamin E 260 Jalal Choupani 2019 4 9532 843 110 mir-196a-2 rs11614913 261 Xiaqin Huo 2019 18 - 14,440 - Hysterectomy 262 A. Bodurtha Smith 2019 58 292,730 528 292,202 HIV 263 Alireza Sadeghia 2019 21 900,000 - - Dietary Fat Intake 264 Kui Zhang 2019 13 40,404 6449 33,955 Fermented dairy foods 265 Zohre Momenimovahed 2019 20 - - - Fertility Drugs 266 Christina Bamia 2019 31 - 13,111 - Coffee consumption 267 Boris Janssen 2019 115 - - - predicted pathogenic PALB2 268 Yang Liu 2019 12 1,193,201 - - Menopausal Hormone Replacement 269 Javaid Iqbal 2018 2 5093 1114 3979 Hormone Levels 270 Sen Li 2019 12 12,933 5057 7876 Genetic polymorphism of MTHFR C677T 271 Guisheng He 2019 45 1,059,975 329,035 730,940 TERT rs10069690 polymorphism 272 Yizi Wang 2019 36 4, 229,061 - - Statin use 273 Jun Yu 2019 83 21,612 - - SFRP promoter hypermethylation 274 Qiao Wen 2019 7 1,710,080 - - Metformin 275 Suszynska M1 2019 5 3748 1919 1829 EPHX1 polymorphism rs1051740 276 Tian Xu1 2019 21 29,981 13,675 16,306 HOTAIR polymorphisms 277 Jinghua Shi 2018 13 901,287 - - Metformin Characteristic of included studies Bisphosphonates use Four authors (RR, MM, SL, and KT) independently screened the titles and abstracts of citations to identify potentially relevant studies. Then, the full texts of potentially eligible articles were obtained and reviewed for further assessment according to the inclusion and exclusion criteria. Controversies were resolved by consulting a third person (LJ). Data were extracted from eligible studies using a prespecified form in Microsoft Excel by four authors (RR, MM, SL, and KT) independently. The following information was collected: first author, year of publication, number of included primary studies, number of participants, age of participants, factors associated with OC, besides the measure of association (e.g., RR, OR), and its confidence intervals. Any discrepancy was resolved through discussion with a third author (LJ). EndNote X9 was used to extracting the records and removing duplicates (The EndNote Team. EndNote. EndNote X9 ed. Philadelphia, PA: Clarivate; 2013.). The SIGN checklist was used to assess the methodological quality of systematic reviews (2); it is composed of 12 items containing ‘yes,‘ ‘no,‘ ‘can’t,‘ or ‘not applicable’ options. Generally, the methodological quality of the studies in this checklist was categorized into low quality, acceptable, and high quality, (Fig.  1 ). Fig. 1 SIGN Checklist scoring SIGN Checklist scoring The quality assessment of the eligible studies was undertaken independently by four authors (RR, MM, SL, and KT). Any disagreements were resolved through discussion. All statistical analyses were performed using Stata version 16 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.). Most of the studies reported measures of the association between each factor and OC using the odds ratio (OR) or risk ratio (RR) with their corresponding CIs. Only one study used a standardized incidence rate ratio (SIR) and standardized mean difference (SMD) as an effect size. Thus, OR or RR and 95 % confidence intervals (CIs) were used to present the association between the factors and OC. For conducting the meta-analysis, all related information about measures of association (e.g., Pooled OR, Pooled RR, Standard error, 95 % Confidence Interval) were extracted and converted to pooled effect size and its SE for every factor in each study. Since the reported combined effects from systematic reviews were used in the analysis, so primary studies may have been included in different systematic reviews and meta-analyses in the different years which we were not able to exclude them in the analysis. Heterogeneity was evaluated among the primary studies using the forest plots, Cochran’s Q statistic, and I 2 statistic. A random-effects model using restricted maximum-likelihood was used if heterogeneity was high (I 2 > 50 %); otherwise, a fixed-effects model was applied. Since the number of first reviews combined for the meta-analysis was less than 10, Egger’s regression asymmetry tests were used for assessing the publication bias instead of funnel plots (Egger et al., 1997), where p <0.10 was considered as evidence of bias. The characteristics of the included studies were descriptively summarized using a structured table.

Twenty-eight thousand sixty-two papers were initially retrieved from the electronic databases, among which 20,104 studies were screened. Two hundred seventy-seven articles met our inclusion criteria, 226 of which included in the meta-analysis (Fig.  2 ). The eligible articles were those published between 1998 (when meta-analyses in this field first became available) and 2020. All of the studies had utilized a healthy control group against women with OC. Fig. 2 PRISMA flow diagram PRISMA flow diagram Overall, from the 277 eligible meta-analyses or systematic reviews, 216 putative risk/protective factors of OC were reported. Due to the number of evaluated factors, all were categorized into 5 main groups: (1) Nutritional factors, (2) Drug use and Medical history, (3) Diseases, (4) Genetic factors, (5) Other factors. Among all of the studied factors, 109 had one quantitative synthesis report, and 53 did not have any quantitative synthesis of individual findings but reported valuable data in systematic review articles (Table 2 S and Table 3 S). Meta-analyses were conducted on the 53 associated factors with OC with sufficient data (two or more reports with the same measures). Most commonly reported genetic factors were MTHFR C677T (OR=1.077; 95 % CI (1.032, 1.124); P-value<0.001), BSML rs1544410 (OR=1.078; 95 %CI (1.024, 1.153); and P-value=0.004) and Fokl rs2228570 (OR=1.123; 95 % CI (1.089, 1.157); P-value<0.001), which were significantly associated with increasing risk of OC ( Fig.  3 ). The results of publication bias assessed using the Egger’s test indicate significant publication bias only for MTHFR C677T factor (P-value=0.017). Fig. 3 Meta-analysis of OR for MTHFR C677T, BSML rs1544410 and Fokl rs2228570 Meta-analysis of OR for MTHFR C677T, BSML rs1544410 and Fokl rs2228570 Among the other factors, coffee intake (OR=1.106; 95 % CI (1.009, 1.211); P-value=0.030), hormone therapy (RR=1.057; 95 % CI (1.030, 1.400); P-value<0.001), hysterectomy (OR=0.863; 95 % CI (0.745, 0.999); P-value=0.049), and breast feeding (OR=0.719; 95 % CI (0.679, 0.762); P-value<0.001) were mostly reported in studies. Final results of all conducted meta-analysis are presented in Table  2 . Table 2 Results of all conducted meta-analysis Variables Measure of Association Odds Ratio (95 % CI) P-value I 2 % No. of study in analysis Nutritional factors Alcohol use RR 1.015 (0.974 – 1.052) 0.485 0.01 3 Coffee intake OR 1.106 (1.009 – 1.211) 0.030 0.00 4 RR 1.036 (0.967 – 1.109) 0.317 0.00 3 Egg intake RR 1.147 (1.045 – 1.250) <0.001 17.73 2 Fat intake RR 1.188 (1.090 – 1.296) <0.001 0.00 3 Fiber intake OR 0.760 (0.714 – 0.810) <0.001 0.00 3 Milk intake RR 1.016 (0.664 – 1.554) 0.941 0.08 2 Tea intake OR 0.833 (0.741 – 0.936) 0.002 0.00 3 RR 0.856 (0.779 – 0.959) 0.005 0.00 2 Vegetables intake RR 0.896 (0.837 – 0.958) <0.001 0.00 2 Drug use and Medical history Aspirin OR 0.894 (0.854 – 0.935) <0.001 0.00 3 Metformin RR 0.718 (0.602 – 0.855) <0.001 0.00 3 NSAIDs RR 0.898 (0.819 – 0.984) 0.020 0.00 3 Oral contraceptive OR 0.655 (0.515 – 0.833) <0.001 78.23 2 Statin RR 0.849 (0.749 – 0.962) 0.010 0.00 2 Hormone therapy (estrogen) RR 1.305 (1.210 – 1.407) <0.001 0.00 2 Hormone therapy (Overall) RR 1.057 (1.030 – 1.400) <0.001 94.44 4 Hormone therapy (estrogen-progestin) OR 1.190 (1.043 – 1.357) 0.009 82.24 2 Hysterectomy OR 0.863 (0.745 – 0.999) 0.049 67.12 4 Tubal ligation OR 0.693 (0.657 – 0.731) <0.001 0.00 Diseases Diabetes RR 1.24 (1.32 – 1.35) <0.001 0.00 3 Endometriosis OR 1.433 (1.294 – 1.586) <0.001 3.05 2 Poly cystic ovarian syndrome OR 1.580 (1.081 – 2.310) 0.018 29.48 2 Genetic factors Asn680Ser OR 1.120 (0.594 – 2.110) 0.726 86.32 2 BRCA2 N372H rs144848 OR 1.079 (1.018 – 1.143) 0.010 44.61 4 BSML rs1544410 OR 1.078 (1.024 – 1.153) 0.004 0.00 8 ESR2 rs3020450 OR 0.818 (0.719 – 1.040) 0.151 61.20 2 Fokl rs2228570 OR 1.123 (1.089 – 1.157) <0.001 0.00 8 GSTM1 OR 1.015 (0.928 – 1.111) 0.741 0.00 2 MTHFR A1298C OR 0.997 (0.943 – 1.054) 0.907 0.00 3 MTHFR C677T OR 1.077 (1.032 – 1.124) <0.001 45.55 9 NFƙB1 OR 1.680 (1.08 – 2.62) 0.020 69.07 2 P16INK4a OR 2.657 (1.173 – 6.014) 0.019 51.28 2 RAD51 135G-C OR 0.996 (0.922 – 1.075) 0.910 0.00 4 ERCC1 rs11615 OR 0.987 (0.756 – 1.287) 0.920 0.00 2 ERCC2 rs13181 OR 1.42 (1.15 – 1.76) 0.001 0.00 2 VGEGF rs699947 OR 0.983 (0.644 – 1.502) 0.938 78.04 2 VDR rs731236 OR 0.996 (0.882 – 1.125) 0.842 56.81 6 FASL rs763110 OR 0.640 (0.520 – 0.788) <0.001 <0.01 2 VEGFA rs833061 OR 0.834 (0.324 – 2.149) 0.707 76.02 2 RAD51 rs1801320 OR 0.656 (0.349 – 1.232) 0.189 41.43 3 FAS/APO-1 rs2234767 OR 1.001 (0.956 – 1.068) 0.982 0.00 3 MMP-12 rs2276109 OR 1.588 (0.694 – 3.630) 0.273 88.80 2 VEGF rs3025039 OR 0.869 (0.719 – 1.04) 0.144 0.00 2 VDR rs7975232 OR 0.990 (0.901 – 1.088) 0.842 0.00 5 VDR rs11568820 OR 1.164 (1.087 – 1.248) <0.001 0.00 4 XRCC2r rs3218536 OR 0.887 (0.750 – 1.050) 0.163 51.57 3 Other factors Acrylamide RR 0.994 (0.930 – 1.063) 0.865 0.00 2 Obesity RR 1.274 (1.194 – 1.36) <0.001 0.00 2 Overweight OR 1.079 (1.041 – 1.119) <0.001 24.04 3 RR 1.071 (1.041 – 1.102) <0.001 0.00 3 Height RR 1.128 (1.064 – 1.196) <0.001 87.71 3 Weight RR 1.067 (0.977 – 1.165) 0.149 74.99 2 Smoking RR 1.311 (0.847 – 2.029) 0.225 98.13 3 Recreational physical activity RR 0.830 (0.745 – 0.925) <0.001 0.00 3 Perineal talc OR 1.297 (1.242 – 1.355) <0.001 0.00 2 RR 1.250)1.177 – 1.327) <0.001 38.11 2 Breast feeding OR 0.719 (0.679 – 0.762) <0.001 4.63 4 Results of all conducted meta-analysis The risk of bias was assessed using the SIGN checklist. Among 277 included studies, 24.19 %, 39.35 %, and 36.46 % had “low quality”, “acceptable” and “high quality,“ respectively.

Following cervical and uterine cancer, ovarian cancer (OC) has the third rank in gynecologic cancers. A woman’s risk of getting ovarian cancer during her lifetime is about 1 in 78. Mortality rate of ovarian cancer is about 1 in 108. (These statistics don’t count low malignant potential ovarian tumors.) It often remains non-diagnosed until it spreads throughout the pelvis and abdomen, making its treatment even more difficult. At its early stages, when it is limited to the ovary, the treatment success has a higher rate. The silent tumor growth in OC increases its mortality rate and deteriorates its prognosis [ 1 ]. OC has a 46 % five-year survival rate. Early detection is important. Most women with Stage 1 ovarian cancer have an excellent prognosis. Stage 1 patients with grade 1 tumors have a 5-year survival of over 90 %, as do patients in stages 1 A and 1B [ 2 ]. Besides the undetectable progress of this type of cancer, improper screening methods further delay its diagnosis [ 3 ]. Due to the low prevalence of ovarian cancer even amongst postmenopausal women (1:2500), an efficient screening tool requires high sensitivity (>75 %) and extremely high specificity (99.7 %) [ 4 ]. A significant increase is estimated in its mortality rate by 2040. Nonetheless, identification of the most effective risk factors can be helpful in prevention measures concerning OC [ 5 ]. Conflicting results can be found in the literature describing the role of several factors (e.g., nutritional, environmental, and genetic factors, as well as lifestyle, drug use, and medical history). Genetic predisposition is related to a higher risk of ovarian cancer that also tends to occur at a younger age. BRCA1 and 2 mutation carriers harbor significantly increased ovarian cancer risk (40–45 % resp. 15–20 %) by the age of 70. Risk of OC in the high risk women under 40 years old is low [ 6 ]. Several studies on ovarian cancer have been published that have examined various factors influencing the incidence, prevalence and mortality rate. Some of these studies were purely observational and some were meta-analyzes. So far, no study has been published that has summarized and re-analyzed the results of various meta-analyzes in this field, and this issue shows the importance of this study. The present study examined up to 50 factors (nutritional and genetic factors, drugs use, some diseases, breast feeding, smoking and physical activity) that other studies had examined and sometimes presented conflicting results. The presented umbrella meta-analysis and systematic review is focused on any kind of risk factors on ovarian cancer among all women and aimed to summarize the available reviews and find the most important OC risk factors. This study is focused on any kind of risk factors on ovarian cancer among all women.

Numerous studies have addressed the effective factors of OC; however, these works have resulted in contradicting outcomes. The current study explores all previous meta-analyses and systematic reviews to provide a valuable summary of the OC protective and risk factors, among which nutritional and genetic factors play a more profound role. Although the genetic factors cannot be changed due to their inheritance, nutritional ones could be well regulated to prevent OC.

This study focuses on OC risk factors and protective measures. The factors can be classified into nutritional, drug use and medical history, diseases, and genetic. As regards nutritional factors, intake of coffee, egg, and fat can significantly enhance the risk of OC. Estrogen and estrogen-progesterone therapies (generally, hormone therapy) are also associated with the elevated risk of OC. Several diseases (e.g., diabetes, endometriosis, and polycystic ovarian syndrome), as well as some genetic polymorphisms (e.g., BRCA2 N372H rs144848, BSML rs1544410, Fokl rs2228570, MTHFR C677T, P16INK4a, ERCC2 rs13181, MMP-12 rs2276109, and VDR rs11568820), can significantly increase the incidence of OC. Other factors, like obesity, overweight, smoking, and the use of perineal talc, are also accompanied by an increased risk of OC. Coffee is rich in several anti-oxidant and anti-carcinogenic bioactive compounds (e.g., phenolic acids, cafestol, and kahweol, respectively) [ 6 ]. This beverage has shown an inverse correlation with liver and endometrial cancer risk [ 4 ]. Furthermore, coffee and caffeine have an inverse relationship with sex hormones (testosterone and estradiol) [ 2 ]. High levels of these hormones have exhibited direct association with enhanced breast and ovarian cancer [ 8 , 9 ]. Coffee contains acrylamide, which has been shown to increase the risk of breast and ovarian cancer as well [ 10 ]. The meta-analysis in the present study indicates a positive correlation between coffee drinking and OC risk. Eggs are rich in cholesterol and choline, thus providing quite high protein per energy content, all of which are linked to the risk of breast, ovarian, and prostate cancers. Nonetheless, the majority of these studies on the mentioned cancers have not explored egg consumption as a primary exposure of interest, restricting a robust assessment of the hypothesized correlations. Since eggs have been considered as a source of protein and fat, its intake association with the OC risk has been primarily explored to examine the impact of protein or fat [ 11 ]. In this meta-analysis, egg consumption has been shown to be significantly and positively correlated with OC. As one of the most controversial nutritional factors, dietary fat can enhance the development of hormone-related cancers (e.g., breast, endometrial, and OCs). However, the reports on this field are discrepant. High-fat diets may stimulate over-secretion of ovarian estrogen, leading to tumor-promoting mechanisms through mitogenic impacts on ERα- positive or negative tumor cells [ 12 ]. Epidemiologic reports indicate an association between estrogen exposure duration and OC induction and biology [ 13 ]. Recent research has expressed that besides inhibiting estrogen-driven growth in the uterus, progesterone can protect the ovaries against neoplastic transformation [ 14 ]. Despite the available poor knowledge of the etiology of OC, the role of estrogen and progestin seems biologically plausible. Based on a theory, high levels of menopausal gonadotropins due to estradiol expression may elevate OC risk. In other words, HRT can decrease the risk of OC by reducing the levels of menopausal gonadotropins. However, due to small HRT-related decrease, the mentioned advantages could be overruled by the estrogen-induced proliferation of ovarian cells. Moreover, the epithelial surface of both normal and malignant ovaries expresses estrogen receptors [ 15 ]. Furthermore, progestin is responsible for the declined risk associated with oral contraceptive use. Pregnancy can also offer a biologic basis for weak correlations with HRT formulations, including progestins [ 16 ]. The current work indicates a significant positive association between hormone therapy (estrogen, estrogen-progestin, and overall) and OC. Diabetes mellitus (DM) is also positively and significantly associated with the risk of OC. Although the carcinogenic influence of DM on the ovary has not been completely understood, some mechanisms have been introduced to describe it partially. Hyperinsulinemia (often associated with insulin resistance) is commonly observed in type 2 DM patients. Chronic hyperinsulinemia has an association with tumor promotion due to the oncogenic potentials of insulin by stimulating cellular signaling cascade or incrementing growth factor-related cell proliferation [ 17 ]. Moreover, increased levels of insulin are associated with high bioactivity of insulin growth factor-1 (IGF-1) [ 18 ]. Considering the anti-apoptosis and mitogenic influences of IGF-1 on normal and cancerous human cells, type 2 DM can promote tumor development [ 19 ]. Besides, hyperglycemia has been recognized as one of the major health consequences of DM. Based on numerous animal and clinical studies, hyperglycemia is related to oxidative stress [ 20 ]. Oxidative stress refers to an imbalance between the reactive oxygen species (ROS) production and antioxidant defense mechanisms. ROS can damage the biomolecules of the cells, including those involved in cell proliferation and repair [ 21 ]. Based on the results, the risk of developing OC is 43 % in women with endometriosis. The endometriosis mechanisms in epithelial OC can be divided into 3 types. The first one is estrogen-dependent. Ness et al. introduce endometriosis as a precursor for epithelial OC, which is easily developed in the low-progesterone and high-estrogen conditions [ 22 ]. The second involves the genetic mutation in endometriotic tissues, like hepatocyte nuclear factor-1β (HNF-1β) [ 23 ] and ARID1A [ 24 ]. Furthermore, chronic inflammations, heme, or free iron-induced oxidative stress in endometriotic tissues also exhibit an association with epithelial OC [ 25 ]. The risk of OC shows a 60 % increase in women suffering from polycystic ovary syndrome (PCOS). PCOS has various risk factors, including obesity, diabetes, inflammation, metabolic syndrome, and aging. However, it is not clear whether the elevated risk of endometrial cancer is due to separate risk factors (e.g., diabetes, obesity) or PCOS itself. PCOS has its own metabolic characteristics, including hyperinsulinism, hyperglycemia, insulin resistance, and hyperandrogenism, enhancing cancer risk. Moreover, such a relationship between PCOS and endometrial cancer could be due to common inherited genetic variants. Other factors, such as parity (nulliparous versus multi), age at first pregnancy, and use/length of hormone therapy (HRT, OCP), could confound the results. Some genetic factors may enhance the risk of developing OC. In the present study, Asn680Ser, BRCA2 N372H rs144848, BSML rs1544410, Fokl rs2228570, GSTM1 , MTHFR C677T, NFƙB1 , P16 INK4a , ERCC2 rs13181, MMP-12 rs2276109, and VDR rs11568820 have been found to increase the risk of OC significantly. Among the mentioned polymorphisms, P16INK4a has the strongest impact on the risk of OC (2.6-fold increase), followed by NFƙB1 and MMP-12. rs2276109. Some studies have mentioned the crucial role of p16 INK4a inactivation as the result of aberrant hypermethylation in the lung, liver, stomach, breast, and uterus carcinogeneses [ 26 , 27 ]. In a meta-analysis on 6 eligible research encompassing 261 patients, Hu et al. show a correlation between p16 INK4a promoter hypermethylation and elevated risk of endometrial carcinoma [ 27 ]. A meta-analysis by Xiao et al. also report the significant association of aberrant methylation of p16 INK4a promoter with OC [ 28 ]. This could be regarded as a potential molecular marker for monitoring the diseases and providing new insights into OC therapies. NFκB1 can significantly inhibit cell apoptosis through regulation of the level of survival genes, such as BCL-2 homolog A1, PAI-2, and IAP family. Moreover, studies have indicated the role of the NFκB1 signaling pathway in cellular proliferation by IL-5 enhancement, MAPK phosphorylation, and cyclin D1 expression modulation [ 29 ]. Numerous meta-analyses have addressed the relationship between NFκB1 promoter -94ins/del ATTG polymorphism and cancer risk, although their findings are not entirely consistent. For instance, Yang et al. [ 30 ] and Duan et al. [ 31 ] express that the polymorphism in NFκB1 -94ins/del ATTG promoter can increase the overall cancer risk. These results do not agree with those reported by Zou et al. [ 32 ]. Such contradictions can be assigned to the bias as the result of a limited sample size. MMP-12 is involved in the pro-tumorigenesis process through inhibiting cancer cell apoptosis and promoting cancer cell invasion and migration [ 33 ]. As SNP of MMP-12-82 A>G can influence the MMP-12 expression and enhance the cancer risk, the correlation between MMP-12 promoter gene polymorphism and the cancer risk has been extensively addressed in recent years. Obesity, overweight, smoking, and the use of perineal talc could be mentioned as other factors associated with OC risk. The biological mechanisms underlying the relation of overweight and obesity with OC are not clarified and consistent. Based on a study by Kuper et al. [ 34 ], progesterone and leptin could be possible endocrine mediators of the weight effect on OC risk. Such an impact could be assigned to elevated insulin levels, androgens, and free IGF-I due to obesity [ 35 ]. Regarding disassociation of BMI with OC risk among postmenopausal women, Reeves et al. [ 36 ] express that association of BMI with OC risk is under the mediation of hormones, as its impact on OC risk remarkably differs in premenopausal and postmenopausal subjects. BMI shows an inverse association with sex hormone-binding globulin and progesterone, while it is positively correlated with free testosterone in premenopausal women [ 37 ]. The mentioned hormone factors seem to be independently or cooperatively involved in the carcinogenic process. Concerning biological mechanisms, the direct correlation of smoking with mucinous tumors can be assigned to the similarity of this neoplasm with cervical adenocarcinoma and colorectal cancers [ 38 ], both of which have exhibited direct association with tobacco exposure. Similarly, endometriois and clear cell cancers have some biological similarities with endometrial cancer, which is inversely related to tobacco smoking due to the possible anti-estrogenic influence of smoking. The tobacco smoking could exert strong impacts in the early stages of (ovarian) carcinogenesis. Thus, the more powerful tobacco-associated risk for mucinous could be explained by the fact that for the mucinous histotype, there is a continuum from benign to borderline and invasive disease, while serous OCs are often high grade and not originated from the borderline tumors [ 39 ]. Furthermore, the smoking-induced mutation in the somatic KRAS gene is more common in mucinous rather than serous borderline ovarian tumors [ 40 ], and also in borderline tumors than invasive cancer [ 41 ]. The ovarian carcinogenesis mechanism of perineal talc use has remained unclear. Based on a hypothesis, however, as an external stimulus, talc can ascend from the vagina to the uterine tubes and trigger a chronic inflammatory response, further promoting the OC development. Cellular injuries, oxidative stresses, and local elevation of inflammatory mediators (e.g., cytokines and prostaglandins) could be mutagenic, thus encouraging carcinogenesis [ 42 ]. Supporting this hypothesis, hysterectomy or bilateral tubal ligation, which may dramatically decline the ovarian exposure to inflammatory mediators, is related to a decreased OC risk [ 43 – 45 ].

Additional file 1. Additional file 1.