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. 2016 Mar 3;7(15):19863–19883. doi: 10.18632/oncotarget.7887

Prognostic role of STAT3 in solid tumors: a systematic review and meta-analysis

Pin Wu 1,4,#, Dang Wu 2,4,#, Lufeng Zhao 1, Lijian Huang 1, Gang Shen 1, Jian Huang 3,4, Ying Chai 1
PMCID: PMC4991424  PMID: 26959884

Abstract

Accumulated studies have provided controversial evidences of the association between signal transducer and activator of transcription proteins 3 (STAT3) expression and survival of human solid tumors. To address this inconsistency, we performed a meta-analysis with 63 studies identified from PubMed, Medline and EBSCO. We found STAT3 overexpression was significantly associated with worse 3-year overall survival (OS) (OR = 2.06, 95% CI = 1.57 to 2.71, P < 0.00001) and 5-year OS (OR = 2.00, 95% CI = 1.53 to 2.63, P < 0.00001) of human solid tumors. Similar results were observed when disease free survival (DFS) were analyzed. Subgroup analysis showed that elevated STAT3 expression was associated with poor prognosis of gastric cancer, lung cancer, gliomas, hepatic cancer, osteosarcoma, prostate cancer, pancreatic cancer but better prognosis of breast cancer. The correlation between STAT3 and survival of solid tumors was related to its phosphorylated state. High expression level of STAT3 was also associated with advanced tumor stage. In conclusion, elevated STAT3 expression is associated with poor survival in most solid tumors. STAT3 is a valuable biomarker for prognosis prediction and a promising therapeutic target in human solid tumors.

Keywords: STAT3, solid tumors, prognosis, overall survival, disease free survival

INTRODUCTION

Signal transducer and activator of transcription proteins 3 (STAT3) is well demonstrated to play a crucial role in tumor development and cancer-related inflammation [1]. STAT3 is also linked to inflammation-related oncogenesis initiated by genetic alterations and environmental factors [2–4], and is constitutively activated in various cancers [5, 6]. Persistent activation of STAT3 is involved in promoting tumor cell proliferation, survival, tumor invasion, angiogenesis and immunosuppression, inducing and maintaining a pro-carcinogenic inflammatory microenvironment [7]. Growing studies identified novel tumor-promoting functions of STAT3 in mitochondria metabolism [8], drug resistance [9, 10], epigenetic regulation [11], cancer stem cells [12, 13] and pre-metastatic niches [14, 15]. Given the pivotal role in tumor development, STAT3 represents an attractive therapeutic target for solid tumors. Recently, accumulating studies have demonstrated STAT3-targeted therapy could effectively restrain tumor development in various solid tumors [16–21]. However, the prognostic value of STAT3 overexpression in human solid tumors is still controversial.

A plenty of studies showed that elevated STAT3 expression in tumor tissue was correlated with poor survival of patients with various solid tumors such as gastric cancer [22–29], lung cancer [30–37], gliomas [38–42], colorectal cancer [43], ovarian cancer [44], cervical cancer [45], hepatocellular carcinoma [46, 47], melanoma [48], esophageal cancer [49], osteosarcoma [50, 51], pancreatic cancer [52, 53], thymic epithelial tumor [54], astrocytomas [55], lingual squamous cell carcinoma [56], nasopharyngeal carcinoma [57], prostate cancer [58], renal cell carcinoma [59] and Wilms' tumor [60]. However, other studies reported that overexpression of STAT3 was correlated with favorable outcome of patients with breast cancer [61–65], gastric cancer [66], lung cancer [67–69], colorectal cancer [70, 71] and melanoma [72].

Therefore, we carried out a meta-analysis combining available evidences to evaluate the prognostic value of STAT3 expression in solid tumors. We also evaluated whether the clinical outcome of patients with solid tumors differed between STAT3 phosphorylation state and between different tumor types. This meta-analysis intended to assess the role of STAT3 in relation to survival in solid tumors, thereby supporting more rational development of therapeutic strategies against STAT3.

RESULTS

Search results and study characteristics

Sixty-three studies with a total of 9449 patients were included (Figure 1). Characteristics of included studies are shown in Table 1. Eleven studies evaluated lung cancer [30–37, 67–69], nine evaluated gastric cancer [22–29, 66], five evaluated breast cancer [61–65], five evaluated gliomas [38–42], four evaluated colorectal cancer [43, 70, 71, 73], three evaluated ovarian cancer [44, 74, 75], three evaluated cervical cancer [45, 76, 77], two evaluated hepatocellular carcinoma [46, 47], two evaluated melanoma [48, 72], two evaluated esophageal cancer [49, 78], two evaluated osteosarcoma [50, 51], two evaluated pancreatic cancer [52, 53], two evaluated thymic epithelial tumours [54, 79], two oral cancer [80, 81], and one each evaluated astrocytomas [55], chordoma [82], head and neck squamous cell carcinoma [83], lingual squamous cell carcinoma [56], nasopharyngeal carcinoma [84], pharyngeal cancer [57], prostate cancer [58], renal cell carcinoma [59], and Wilms' tumor [60]. Of these 63 studies, 20 studies evaluated STAT3, 37 studies evaluated p-STAT3, and 6 studies evaluated both STAT3 and p-STAT3. As for the region, 39 studies were conducted in Asia, 13 studies in America, 10 studies in Europe, and 1 study in Austria.

Figure 1. Flow diagram of study selection.

Figure 1

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STAT3: Signal transducer and activator of transcription protein 3; OS: overall survival; DFS: disease-free survival.

Table 1. Characteristics of studies included in the meta-analysis.

References Country Type of cancer Patient No. Age, median (range) Male/Female Stage Follow-Up,months (Range) STAT3 (+/–) NO. 3-yearOS (+/–)% 5-yearOS (+/–)% NOS Score
Studies including OS
Abou-Ghazal, M., et al. (2008) USA Gliomas 128 44 (4–91) NR II-IV NR 65/63 36.7/48.9 34.7/41.7 7
Ai, T., et al. (2012) China NSCLC 65 NR 50/15 I-IV 29.9 ± 15.7 47/18 49.8/77.9 NR 8
Birner, P., etal. (2010) Bulgaria Gliomas 111 58.0 ± 11.6 57/54 NR 11.1 ± 0.8 65/46 0/17.9 NR 7
Chang, K. C., et al. (2006) China TET 118 52.7 (25–77) 65/53 I-IV NR 38/80 58.7/56.7 32.9/30 7
Chatterjee, Devasis., et al. (2008) USA GC 143 71.1 (31–96) 75/68 IA-IV 34 (12–180) 40/103 45.1/70.1 0/60.7 8
Chen, C. C., et al. (2010) China NC 95 NR NR I-IV 112.8 (31.2–240) 34/61 39/61.2 31.3/53 6
Chen, H. H., et al. (2012) China CC 165 69 (29–89) 0/165 I-IV NR 36/129 53.8/60 47.3/48.5 8
Cortas, T., et al. (2007) USA NSCLC 145 70 (40–88) 64/81 I-III 35 (4–85) 50/84 70/60.7 55.5/52 7
Deng, J. Y., et al. (2010) China GC 53 55 (31–78) 37/16 I-IV 38 (2–108) 26/27 11.5/85.2 3.8/85.2 8
Deng, J., et al. (2013) China GC 114 NR 76/38 NR NR 89/25 24.6/80 9.9/50.6 7
Denley, S. M., et al. (2013)- Tyr705 UK PDA 86 NR 43/43 NR 22 29/57 9/21.9 0/11 7
Denley, S. M., et al. (2013)- Ser727 UK PDA 86 NR 43/43 NR 22 30/56 83/22.2 0/11.2 7
Dolled-F. M., et al. (2003)-M1-C USA BC 286 NR 0/286 NR NR 198/88 94.6/87.3 85.5/80 6
Dolled-F. M., et al. (2003)-M1-N USA BC 286 NR 0/286 NR NR 66/220 94.6/92.1 93.1/81.2 6
Dolled-F. M., et al. (2003)-M2-C USA BC 286 NR 0/286 NR NR 56/229 92.5/92.5 86.7/83.7 6
Dolled-F. M., et al. (2003)-M2-N USA BC 286 NR 0/286 NR NR 124/161 95.7/89.9 91.3/78.7 6
Galleges Ruiz, M. I., et al. (2009) USA NSCLC 178 NR 127/51 I-III NR 51/111 54.5/45 48.8/36.5 7
Gordziel, C., et al. (2013)-C Germany CRC 414 NR NR I-III 37 (0–146) 132/282 82.8/70.6 74/61 6
Gordziel, C., et al. (2013)-N Germany CRC 414 NR NR I-III 37 (0–146) 124/290 78.6/72 71.2/62.1 6
Haura, Eric B., et al. (2005) USA NSCLC 176 69 (45–84) 97/79 I 72 (36–108) 94/82 77.4/74.3 57.8/52.2 8
Hbibi, A. Tadlaoui., et al. (2008)-M1 France CRC 126 68.1 NR I-IV NR 62/38 NR 61.3/49.2 7
Hbibi, A. Tadlaoui., et al. (2008)-M2 France CRC 126 68.1 NR I-IV NR 27/73 NR 59.1/55.6 7
Horiguchi, Akio., et al. (2002) Japan RCC 48 63 (24–85) 39/9 I-IV 15.9 (1–101 24/24 53.5/89.7 53.5/89.7 7
Huang, C., et al. (2012) China PDA 71 67 (40–80) 50/21 I-IV 33.7 (3–60) 39/32 0/24.3 0/14.2 8
Jia, Yanfei., et al. (2013) China GC 48 66 (45–83) 34/14 I-IV NR 19/29 54.4/86.7 12.7/47.6 7
Kim, D. Y., et al. (2009) Korea GC 71 NR 48/23 I-IV 30 (11–83) 27/44 59/86.4 40/81.8 6
Kim, Yeon-Joo., et al. (2011) Korea NC 38 48 (25–74) 30/8 I-IV 43.7 (0.71–60) 10/28 NR 41/77 8
Kusaba, T., et al. (2006) Japan CRC 108 65.6 (44–86) 66/42 I-IV NR 62/46 61.6/90.1 48.8/90.1 8
Lee, I., et al. (2012) USA Melanoma 299 56 (13–85) 212/87 IV NR 236/63 44.1/41.8 22.3/25.4 7
Lee, J., et al. (2009) China GC 303 NR 206/97 II-III 61.5 (12–134) 79/224 68.4/78.4 59.5/70.5 7
Li, Chao., et al. (2013) China TET 80 46.5 (19–70) 47/33 I-IV NR 36/44 70.1/100 46.8/97.6 7
Lin, G. S., et al. (2014) China Gliomas 90 55 (18–79). 54/36 NR 46.4 (1.2–109.6) 73/17 14/31.4 NR 8
Mano, Y., et al. (2013) Japan HC 101 NR 81/20 NR NR 36/65 71.3/84.9 60.7/84.7 8
Min, Hao., et al. (2009)-M1 China OC 50 50.6 (22–73) 0/50 I-IV NR 44/6 53.5/75.1 29/0 6
Min, Hao., et al. (2009)-M2 China OC 50 50.6 (22–73) 0/50 I-IV NR 29/21 35/88.1 0/58.7 6
Monnien, F., et al. (2010) France CRC 104 66 (37–80) 76/28 NR 13 (8–48) 39/65 82.2/78 71.8/65.5 7
Pectasides, Eirini., et al. (2010)-1 Greece HNSC 107 NR 87/20 I-IV 64 (1–120) 23/47 90.4/45.9 72.4/38.3 7
Pectasides, Eirini., et al. (2010)-2 Greece HNSC 107 NR 87/20 I-IV 64 (1–120) 12/25 NR 68.8/51.4 7
Piperi, Christina., et al. (2011) Greece Gliomas 97 59 (19–82) 60/37 II-IV 63 (3–180) 89/8 0/14 NR 6
Rosen, D. G., et al. (2006) USA OC 303 58.2 (20–86) 0/303 I-IV 52 215/88 49.9/60 32.1/49.1 8
Ryu, Keinosuke., et al. (2010) USA Osteosarcoma 51 20.5 (5–61) 38/13 NR NR 31/20 48.5/75 35.2/75 8
Schoppmann, S. F., et al. (2012) Austria EC 324 63 252/72 NR NR 144/180 32.6/57.6 24.9/53 7
Sheen-Chen, et al. (2008) China BC 102 48.2 (26–76) 0/102 I-III NR 27/75 NR 59/77.2 6
Slinger, E., et al. (2010) Sweden Gliomas 21 NR NR NR NR 7/14 0/13.6 NR 8
Sonnenblick, A., et al. (2012) Israel BC 125 NR 0/125 NR 50 35/90 100/91 94.4/76.6 6
Sonnenblick, A., et al. (2013)-1 Israel BC 375 50 0/375 NR NR 47/82 97.9/96.3 94/94 7
Sonnenblick, A., et al. (2013)-2 Israel BC 375 50 0/375 NR NR 184/150 99/91.9 94.1/80.2 7
Takemoto, S., et al. (2009) Japan CC 125 47 (19–77) 0/125 I-II NR 71/54 80.9/96.8 78.5/94.5 7
Tam, L., et al. (2007)-N UK PC 50 70 (64–73) 50/0 NR 29.5 (15–54) 22/28 72.8/92.5 57.9/84.2 8
Tam, L., et al. (2007)-C UK PC 50 70 (64–73) 50/0 NR 29.5 (15–54) 19/31 58.1/100 42.5/92.7 8
van Cruijsen, H., et al. (2009) USA NSCLC 164 64.5 NR I-III NR 116/48 49/58.7 33.1/50.3 7
Wang, M., et al. (2011) China NSCLC 208 59.8 (35–76) NR I-III 67 (1–78.2) 128/80 53.9/73.2 24.7/39.8
Wang, Y. C., et al. (2011) China Osteosarcoma 76 NR 25/51 NR 37 36/40 25.8/60.4 25.8/60.4 6
Wang, Y., et al. (2011) China Gliomas 68 45 (15–68) 41/27 NR 51 (1–72) 47/21 0/15.4 NR 7
Woo, S., et al. (2011) Korea GC 285 54.4 193/92 I-IV 39.7 (4–84) 101/179 79/61.6 74.9/54.5 7
Wu, Z.S., et al. (2011) China Melanoma 90 NR 52/38 I-IV NR 51/39 80.5/97.6 50.8/76.7 8
Xiong, Hua., et al. (2012)-M1 China GC 262 59.3 (23–79) 176/86 I-IV 90 (2–273) 248/14 44/64.3 28.4/42.9 8
Xiong, Hua., et al. (2012)-M2 China GC 262 59.3 (23–79) 176/86 I-IV 90 (2–273) 136/126 25.3/65.5 11.5/47.3 8
Yakata, Yuichi., et al. (2007) Japan GC 111 68.9 (38–89) 63/48 NR 120 55/56 37.7/83.4 37.7/78.6 8
Yamashita, H., et al. (2006) Japan BC 506 NR (22–91) 0/506 NR NR 206/300 92/87.9 86/81.6 7
Yang, C., et al. (2013) USA OC 49 61 (41–87) 0/49 I-IV NR 25/24 70.6/73.4 33.5/57.6 7
Yang, Cao., et al. (2009) USA Chordoma 70 59.5 (29–88) 51/19 NR 16.8 (0.8–69.2) 35/35 82.5/90.2 73.1/90.2 8
Yin, Z., et al. (2012) China NSCLC 76 NR 48/28 I-IV NR 42/34 49.6/59.3 41.8/57.9 7
Yu, Y., et al. (2015) China NSCLC 82 NR 48/34 I-IV NR 76/24 28.7/76.2 20.3/48.3 8
Zhang, C. H., et al. (2012)-M1 China HC 100 55.1 (28–77) 80/20 I-IV 15.4 72/28 53.5/57.8 14/32.2 8
Zhang, C. H., et al. (2012)-M2 China HC 100 55.1 (28–77) 80/20 I-IV 15.4 58/42 35.5/81.2 19/25.7 8
Zhang, L. J., et al. (2013) China Wilms’ tumor 58 31 (3–132) 38/20 I-IV ≥ 78 17/41 45.6/72.1 45.6/72.1 7
Zhao, X., et al. (2012)-M1 China SCLC 128 NR 66/62 I-IV 67 (1–78.2) 71/57 29.7/81.6 0/9.9 7
Zhao, X., et al. (2012)-M2 China SCLC 128 NR 66/62 I-IV 67 (1–78.2) 62/66 43.4/62.3 0/3.9 7
Zhao, Yan., et al. (2012) China LSCC 163 NR NR I-IV NR 100/63 75/86.7 41.8/78.6 8
Studies including DFS
Choi, Chel Hun., et al. (2010) Korea CC 29 NR 0/29 I-II NR 20/9 49.9/84.6 49.9/84.6 8
Lee, J., et al. (2009) China GC 303 NR 206/97 II-III 61.5 (12–134) 79/224 61.7/73.7 58.3/67.7 7
Li, X., et al. (2015) China NSCLC 164 NR 115/40 I-III NR 107/57 57.4/87.6 NR 8
Macha, Muzafar A., et al. (2011) Canada Oral cancer 94 NR 70/24 I-IV NR 63/31 18.6/53.9 7.1/53.9 7
Mano, Y., et al. (2013) Japan HC 101 NR 81/20 NR NR 36/65 12.5/55.6 12.5/31.5 8
Schoppmann, S. F., et al. (2012) Austria EC 324 63 252/72 NR NR 144/180 25.8/48.3 20.2/47.2 7
Takemoto, S., et al. (2009) Japan CC 125 47 (19–77) 0/125 I-II NR 71/54 82/97.8 78/95.3 7
Wang, Y. C., et al. (2011) China Osteosarcoma 76 NR 25/51 NR 37 36/40 33.8/67.1 24.9/56.3 6
Yamashita, H., et al. (2006) Japan BC 506 NR (22–91) 0/506 NR NR 206/300 83.2/74.7 72.7/65.3 7
Zhang, L. J., et al. (2013) China Wilms’ tumor 58 31 (3–132) 38/20 I-IV ≥ 78 17/41 31.4/74.1 31.4/74.1 7
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M1: Marker1, STAT3; M2: Marker 2, p-STAT3; N: nuclear expression; C: cytoplasmic expression; 1: Cohort 1; 2: Cohort 2; NSCLC: Non-Small Cell Lung Cancer; GC: Gastric cancer; CRC: Colorectal Cancer; BC: Breast cancer; CC: Cervical Carcinoma; OC: Ovarian Carcinoma; PDA: Pancreatic Ductal Adenocarcinoma; TET: Thymic Epithelial Tumours; RCC: Renal Cell Carcinoma; HC: Hepatocellular Carcinoma; HNSCC: Head and Neck Squamous Cell Carcinoma; EC: Esophageal Cancer; OSCC: Oral Squamous Cell Carcinoma; SCLC: Small Cell Lung Cancer; NC: Nasopharyngeal carcinoma; LSCC: Lingual Squamous Cell Carcinoma; PC: Prostate Cancer; NR: Not Reported; DFS: disease-free survival, STAT3: Signal transducer and activator of transcription protein 3; NOS: newcastle–Ottawa Scale; OS: overall survival.

Evaluation and expression of STAT3

Antibodies, detection and definition method, and cut-off values of STAT3 expression used in the included studies is summarized in Table 2. Diverse antibodies were used for the assessment of STAT3 expression by IHC. For anti-STAT3 antibody, three studies used clone sc-8019, one study each used clone RB-9237, F-2, sc-7179, 79D7, 124H6, and sixteen studies did not report the antibody clone. For anti-p-STAT3 antibody, eight studies used clone D3A7, four studies used clone sc-7993, two studies used clone 9131, one study each used sc-483, sc-8001, sc-8059, ZP-0647, and twenty studies did not report the antibody clone. The median expression of STAT3 in solid tumors was 47.79%, range from 19.65% to 94.66%.

Table 2. Evaluation of human STAT3/p-STAT3 by IHC in the selected studies.

References Type of cancer Marker Cutoff Antibody (Clone)
Abou-Ghazal, M., et al. (2008) Gliomas p-STAT3 NR anti-p-STAT3 (Tyr705), Cell Signaling Technology
Ai, T., et al. (2012) NSCLC STAT3 IHC > 51% anti-STAT3, Cell Signaling Technology
Birner, P., etal. (2010) Gliomas p-STAT3 IHC ≥ 5% anti-p-STAT3 (Tyr705), clone D3A7, Cell Signaling
Chang, K. C., et al. (2006) TET STAT3 IHC ≥ 10% anti-Stat3 F-2: sc-8019, Santa Cruz Biotechnology, Inc.
Chatterjee, Devasis., et al. (2008) GC STAT3-nuclear IHC scores ≥ 4 anti-STAT3, Santa Cruz Biotechnology, Inc.
Chen, H. H., et al. (2012) CC STAT3 IHC ≥ 20% anti-STAT3, Santa Cruz Biotechnology, Inc.
Chen, C. C., et al. (2010) NC p-STAT3 IHC > 10% NR
Choi, Chel Hun., et al. (2010) CC p-STAT3 IHC > 51% anti-p-STAT3 (ser727), Santa Cruz Biotechnology
Cortas, T., et al. (2007) NSCLC p-STAT3 IHC ≥ 5% anti-p-STAT3 (sc-8059), Santa Cruz Biotechnology
Deng, J. Y., et al. (2010) GC p-STAT3 ≥ 10% anti-p-STAT3 (sc-483)
Deng, J., et al. (2013) GC p-STAT3 IHC > 25% anti-p-STAT3, Santa, sc-8001-R
Denley, S. M., et al. (2013) PDA p-STAT3 IHC ≥ 2% anti-pStat3 Tyr 705, 9131, Cell Signaling Technology
anti-pStat3 (Ser 727), 9134, Cell Signaling Technology
Dobi, E., et al. (2013) CRC p-STAT3 IHC > 15% anti-p-STAT3, sc-7993, Santa Cruz Biotechnology
Dolled-Filhart, M., et al. (2003) BC STAT3-cytoplasmic IHC score ≥ 1 anti-STAT3, Cell Signaling Technology
STAT3-nuclear anti-STAT3, Cell Signaling Technology
p-STAT3-cytoplasmic anti-p-STAT3 (Tyr 705), Cell Signaling Technology
p-STAT3-nuclear anti-p-STAT3 (Tyr 705), Cell Signaling Technology
Galleges Ruiz, M. I., et al. (2009) NSCLC p-STAT3-nuclear IHC score > 210 anti–p-STAT3
Gordziel, C., et al. (2013) CRC STAT3-cytoplasmic IHC score ≥ 2 anti-STAT3: Stat3 (79D7), Cell Signaling Technology
STAT3-nuclear
Haura, Eric B., et al. (2005) NSCLC p-STAT3-nuclear IHC score ≥ 1 anti-p-Stat3 (Tyr 705), Cell Signaling Technology
Hbibi, A. Tadlaoui., et al. (2008) CRC p-STAT3 IHC score ≥ 6 anti-P-STAT3 (Tyr 705), Cell Signaling Technology
STAT3 anti-STAT3, Cell Signaling
Horiguchi, Akio., et al. (2002) RCC p-STAT3 IHC ≥ 10% anti-p-STAT3, (Tyr 705), Cell Signaling Technology
Huang, C., et al. (2012) PDA p-STAT3 IHC ≥ 25% anti-p-STAT3, Cell Signaling Technology
Jia, Yanfei., et al. (2013) GC STAT3 NR anti-STAT3, Santa Cruz Biotechnology
Kim, D. Y., et al. (2009) GC STAT3 NR anti-STAT3, Chemicon International
Kim, Yeon-Joo., et al. (2011) NC STAT3 IHC ≥ 10% anti-STAT3, Epitomics
Kusaba, T., et al. (2006) CRC p-STAT3 IHC > 15% anti-p-STAT3 (Tyr705), Santa Cruz Biotechnology
Lee, I., et al. (2012) Melanoma p-STAT3 IHC ≥ 1% anti-p-STAT3 (Tyr705), Santa Cruz Biotechnology
Lee, J., et al. (2009) GC p-STAT3 IHC ≥ 1% anti-p-STAT3 (Tyr705), Cell Signaling Technology
Li, Chao., et al. (2013) TET STAT3 IHC > 10% anti-STAT3, Santa Cruz Biotechnology
Li, X., et al. (2015) NSCLC STAT3 IHC score ≥ 4 anti-STAT3, Santa Cruz Biotechnology
Lin, G. S., et al. (2014) Gliomas p-STAT3 IHC > 5% anti-p-STAT3 (Tyr705), D3A7, Cell Signaling
Macha, Muzafar A., et al. (2011) Oral cancer p-STAT3 NR anti-p-STAT3 (Tyr 705), Cell Signaling
Mano, Y., et al. (2013) HC p-STAT3 NR anti-p-STAT3 (Tyr 705), D3A7, Cell Signaling
Min, Hao., et al. (2009) OC STAT3 IHC ≥ 10% anti-Stat3, (SC-8019), Santa Cruz Biotechnology
p-STAT3 IHC ≥ 10% anti-p-Stat3 (Tyr 705), ZP-0647, Abzoom Biotechnology
Monnien, F., et al. (2010) CRC p-STAT3 IHC > 15% anti-p-Stat3 (Tyr 705), sc-7993, Santa Cruz
Pectasides, Eirini., et al. (2010) HNSCC STAT3-nuclear NR anti-Stat3, clone 124H6; Cell Signaling Technology
Piperi, Christina., et al. (2011) Gliomas p-STAT3 IHC ≥ 6% anti-p-STAT3 (Tyr 705), D3A7 XP, Cell Signaling
Rosen, D. G., et al. (2006) OC p-STAT3 IHC > 10% anti-p-Stat3, (SC-7993-R), Santa Cruz Biotechnology
Ryu, Keinosuke., et al. (2010) Osteosarcoma p-STAT3 IHC > 51% anti-p-STAT, Cell Signaling Technology
Schoppmann, Sebastian F., et al. (2012) EC p-STAT3 IHC > 10% anti-p-STAT3 (Tyr 705), D3A7, Cell Signaling
Shah, N. G., et al. (2006) OSCC STAT3-nuclear IHC > 10% anti-STAT3, Santa Cruz Biotechnology
Slinger, E., et al. (2010) Gliomas p-STAT3 IHC > 30% anti-p-STAT3, (Tyr 705), Cell Signaling
Sheen-Chen, Shyr-Ming., et al. (2008) BC STAT3 IHC score ≥ 3 anti-STAT3 (RB-9237), NeoMarkers
Sonnenblick, A., et al. (2012) BC p-STAT3 IHC ≥ 25% anti-p-STAT3, (Tyr 705), Cell Signaling
Sonnenblick, A., et al. (2013) BC p-STAT3 IHC ≥ 10% anti-p-STAT3, (Tyr 705), Cell Signaling
Takemoto, S., et al. (2009) CC p-STAT3 IHC ≥ 5% anti-p-Stat3 (Tyr 705), sc-7993, Santa Cruz Biotechnology
Tam, L., et al. (2007) PC p-STAT3-cytoplasmic ICCC > 0.7 anti-p-STAT3 (Tyr 705), 9131, Cell Signaling
p-STAT3-nuclear
van Cruijsen, H., et al. (2009) NSCLC p-STAT3 NR anti-p-STAT3 (Tyr 705), clone D3A7, Cell Signaling
Wang, M., et al. (2011) NSCLC p-STAT3 IHC > 25% anti-p-STAT3, Cell Signaling Technology
Wang, Y., et al. (2011) Gliomas p-STAT3 IHC score > 4 anti-p-STAT3 (Tyr 705), clone D3A7, Cell Signaling
Wang, Y. C., et al. (2011) Osteosarcoma STAT3 IHC > 5% anti-STAT3, Santa Cruz Biotechnology
Wu, Zheng-Sheng., et al. (2011) Melanoma p-STAT3 NR anti-p-STAT3, Santa Cruz Biotechnology
Woo, S., et al. (2011) GC p-STAT3 IHC ≥ 1% anti-p-STAT3, (Tyr 705), Cell Signaling
Xiong, Hua., et al. (2012) GC STAT3 IHC > 15% anti-STAT3
p-STAT3 anti-p-STAT3 (Tyr 705)
Yakata, Yuichi., et al. (2007) GC p-STAT3 IHC > 10% anti-p-STAT3, Santa Cruz Biotechnology
Yamashita, H., et al. (2006) BC STAT3 IHC score ≥ 2 anti-STAT3, (F-2), Santa Cruz Biotechnology
Yang, C., et al. (2013) OC p-STAT3 IHC > 50% anti-p-STAT3, (Tyr 705), Cell Signaling Technology
Yang, Cao., et al. (2009) Chordoma p-STAT3 IHC score ≥ 4 anti-p-STAT3, Cell Signaling Technology
Yin, Z., et al. (2012) NSCLC STAT3 IHC ≥ 50% anti-STAT3, (sc-8019); Santa Cruz
You, Z., et al. (2012) EC p-STAT3 IHC score ≥ 2 anti-p-STAT3, (Tyr 705), Cell Signaling Technology
Yu, Y., et al. (2015) NSCLC pSTAT3 IHC score ≥ 3 NR
Zhang, C. H., et al. (2012) HC STAT3 IHC > 10% anti-STAT3, Santa Cruz Biotechnology
p-STAT3 anti-p-STAT3, (Tyr 705), Cell Signaling Technology
Zhang, L. J., et al. (2013) Wilms' tumor STAT3 IHC > 51% anti-STAT3, (sc-7179), Santa Cruz Biotechnology
Zhao, X., et al. (2012) SCLC STAT3 IHC ≥ 25% anti-STAT3, Wuhan Boster Company
p-STAT3 anti-p-STAT3, clone B-7, Wuhan Boster Company
Zhao, Yan., et al. (2012) LSCC STAT3 IHC ≥ 10% anti-STAT3, Santa Cruz Biotechnology
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NSCLC: Non-Small Cell Lung Cancer; GC: Gastric cancer; CRC: Colorectal Cancer; BC: Breast cancer; CC: Cervical Carcinoma; OC: Ovarian Carcinoma; PDA: Pancreatic Ductal Adenocarcinoma; TET: Thymic Epithelial Tumours; RCC: Renal Cell Carcinoma; HC: Hepatocellular Carcinoma; HNSCC: Head and Neck Squamous Cell Carcinoma; EC: Esophageal Cancer; OSCC: Oral Squamous Cell Carcinoma; SCLC: Small Cell Lung Cancer; NC: Nasopharyngeal carcinoma; LSCC: Lingual Squamous Cell Carcinoma; PC: Prostate Cancer; ICCH: Interclass Correlation Coefficient; NR: Not Reported.

Association of STAT3 with OS

The combined analysis of 54 studies showed that STAT3 overexpression in tumor tissue was associated with worse 3-year OS of solid tumors (OR = 2.06, 95% CI = 1.57 to 2.71, P < 0.00001) (Figure 2). There was significant heterogeneity among studies (Cochran's Q P < 0.00001, I2 = 81%), so we conducted meta-regression analysis and subgroup meta-analysis to investigate the possible source of the heterogeneity among studies.

Figure 2. Three-year overall survival (OS) by STAT3 expression.

Figure 2

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M1: Marker 1, STAT3; M2: Marker 2, p-STAT3; 1: Cohort 1; 2: Cohort 2; N: nuclear expression; C: cytoplasmic expression.

In the stratified analysis by tumor types, STAT3 expression was associated with worse 3-year OS of gastric cancer (OR = 4.06, 95% CI = 1.86 to 8.89, P = 0.0004), lung cancer (OR = 2.22, 95% CI = 1.31 to 3.77, P = 0.003), gliomas (OR = 4.10, 95% CI = 1.50 to 11.23, P = 0.006), hepatic cancer (OR = 3.75, 95% CI = 1.71 to 8.21, P = 0.001), osteosarcoma (OR = 3.94, 95% CI = 1.83 to 8.51, P = 0.0005) and prostate cancer (OR = 11.08, 95% CI = 1.24 to 98.96, P = 0.03) (Figure 3). There was no significant association between STAT3 expression and 3-year OS of colorectal cancer, ovarian cancer, pancreatic cancer, cervical cancer, melanoma and thymic epithelial tumor (Supplementary Figure S1). Interestingly, STAT3 overexpression was associated with favorable 3-year OS of breast cancer (OR = 0.51, 95% CI = 0.35 to 0.74, P = 0.0004) (Supplementary Figure S2).

Figure 3. Subgroup analysis of 3-year OS by STAT3 expression in different tumor types.

Figure 3

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(A) gastric cancer; (B) lung cancer; (C) gliomas; (D) hepatic cancer; (E) osteosarcoma; (F) prostate cancer. M1: Marker 1, STAT3; M2: Marker 2, p-STAT3; 1: Cohort 1; 2: Cohort 2 N: nuclear expression; C: cytoplasmic expression.

Meta-regression analysis showed that publication year, country, gender and NOS score did not contribute to the heterogeneity (data not shown).

Analysis of 49 studies showed STAT3 expression was also associated with worse 5-year OS (OR = 2.00, 95% CI = 1.53 to 2.63, P < 0.00001) (Figure 4) of solid tumors. There was also high heterogeneity among studies for 5-year OS (Cochran's Q P < 0.00001, I2 = 82%), so we conducted subgroup meta-analysis.

Figure 4. Five-year OS by STAT3 expression.

Figure 4

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M1: Marker 1, STAT3; M2: Marker 2, p-STAT3; 1: Cohort 1; 2: Cohort 2 N: nuclear expression; C: cytoplasmic expression.

Subgroup analysis showed that STAT3 expression was associated with worse 5-year OS of gastric cancer (OR = 5.48, 95% CI = 2.14 to 14.01, P = 0.0004), hepatic cancer (OR = 2.48, 95% CI = 1.41 to 4.35, P = 0.002), osteosarcoma (OR = 4.84, 95% CI = 2.23 to 10.50, P < 0.0001), pancreatic cancer (OR = 9.71, 95% CI = 1.80 to 52.41, P = 0.008) and prostate cancer (OR = 8.35, 95% CI = 1.81 to38.51, P = 0.007) (Figure 5). There was no significant association between STAT3 expression and the 5-year OS of colorectal cancer, lung cancer, ovarian cancer, cervical cancer, melanoma and thymic epithelial tumor (Supplementary Figure S3). STAT3 overexpression was associated with favorable 5-year OS of breast cancer (OR = 0.57, 95% CI = 0.37 to 0.89, P = 0.01) (Supplementary Figure S4).

Figure 5. Subgroup analysis of 5-year OS by STAT3 expression in different tumor types.

Figure 5

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(A) gastric cancer; (B) hepatic cancer; (C) osteosarcoma; (D) pancreatic cancer; (E) prostate cancer. M1: Marker 1, STAT3; M2: Marker 2, p-STAT3; 1: Cohort 1; 2: Cohort 2 N: nuclear expression; C: cytoplasmic expression.

Twenty studies evaluated STAT3, 38 studies evaluated p-STAT3 and 5 studies evaluated both STAT3 and p-STAT3. Our result showed that both STAT3 and p-STAT3 overexpression were associated with worse OS of solid tumors. However, elevated p-STAT3 (OR = 2.45, 95% CI = 1.73 to 3.46, P < 0.00001) expression in tumor tissue seemed to be more significantly associated with worse 3-year OS than STAT3 expression (OR = 1.72, 95% CI = 1.10 to 2.70, P = 0.02) (Supplementary Figure S5). Similar result was observed for 5-year OS analysis (Supplementary Figure S6). A subgroup meta-analysis of studies evaluated both STAT3 and p-STAT3 shown that p-STAT3 expression was associated with worse 3-year and 5-year OS of solid tumor, but not STAT3 (Supplementary Figure S7).

We also evaluated the correlation between STAT3 overexpression and the TNM stage of tumor. High expression level of STAT3 was significantly associated with advanced TNM stage (OR = 0.42, 95% CI = 0.31 to 0.58, P < 0.00001) (Figure 6).

Figure 6. Subgroup analysis the correlation of STAT3 expression and tumor stage.

Figure 6

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M1: Marker 1, STAT3; M2: Marker 2, p-STAT3.

Next, we conducted subgroup analysis according to STAT3 expression level. Results showed STAT3 expression was associated with poor 3-year OS in the studies using cutoff values of 10%–30% (OR = 3.61, 95% CI = 2.42 to 5.39, P < 0.00001) and 50% (OR = 2.14, 95% CI = 1.29 to 3.57, P = 0.003) (Figure 7) to determine STAT3 positivity. Similar result was observed in 5-year OS (Supplementary Figure S6). However, the studies used cutoff value of STAT3 overexpression as more than 1%-6% tumor cells positive was not associated with 3-year and 5-year OS of solid tumors.

Figure 7. Subgroup analysis the correlation between STAT3 overexpression and 3-year OS of solid tumors according to cut-off values determining STAT3 positivity.

Figure 7

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M1: Marker 1, STAT3; M2: Marker 2, p-STAT3.

Association of STAT3 with DFS

Meta-analysis of 10 studies showed that STAT3 expression was associated with statistically significant poor 3-year DFS (OR = 3.52, 95% CI = 1.85 to 6.71, P = 0.0001) (Figure 8A) and poor 5-year DFS (OR = 3.37, 95% CI = 1.67 to 6.80, P = 0.0007) (Figure 8B).

Figure 8. Three and five-year DFS by STAT3 expression.

Figure 8

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(A) 3-year DFS; (B) 5-year DFS. M1: Marker 1, STAT3; M2: Marker 2, p-STAT3.

Sensitivity analyses

Removal of the studies that was an outlier (score, IRS, > 50% vs 1%–6% for other studies) or no report (NR) with regard to the cutoff of STAT3 overexpression by IHC did not influence results for 3- or 5-year OS (OR = 2.45, 95% CI = 1.73 to 3.48, p < 0.00001; OR = 2.08, 95% CI = 1.46 to 2.96, p < 0.0001; respectively). Exclusion of these studies did not reduce heterogeneity for 3- or 5-year OS (Cochran's Q P < 0.00001, I2 = 82%; Cochran's Q P < 0.00001, I2 = 84%, respectively).

Removal of studies with NOS score 6 did not influence results for 3- or 5-year OS (OR = 2.49, 95% CI = 1.86 to 3.34, p < 0.00001; OR = 2.33, 95% CI = 1.74 to 3.12, p < 0.00001, respectively). Exclusion of these studies did not reduce heterogeneity for 3- or 5-year OS (Cochran's Q P < 0.00001, I2 = 80%; Cochran's Q P < 0.00001, I2 = 82%, respectively).

Publication bias

Funnel plot analysis showed that there was no statistical evidence of publication bias in our meta-analysis (data not shown).

DISCUSSION

This meta-analysis is the most comprehensive assessment of the literatures regarding STAT3 expression and tumor prognosis to date. We systematically evaluated survival data for 9449 solid tumor patients included in 63 different studies. Our study demonstrated that the expression of STAT3 is a marker of poor prognosis in solid tumors, with consistent results of OS at 3 and 5 years. Regarding to the tumor types, elevated STAT3 expression in tumor tissues were associated with worse OS of gastric cancer, lung cancer, gliomas, hepatic cancer, osteosarcoma, prostate cancer and pancreatic cancer. However, elevated STAT3 expression was associated with better prognosis of breast cancer. In addition, expression level of phosphorylated STAT3 was more significantly associated with worse outcome of solid tumors than unphosphorylated STAT3.

Our study found there is no significant correlation between STAT3 overexpression and OS of colorectal cancer and ovarian cancer. And STAT3 overexpression in breast cancer tissue is associated with favorable OS. However, recent studies demonstrated that STAT3-targeted inhibitor could restrain tumor development in various solid tumor models including breast cancer [16, 19, 85, 86], melanoma [87] and ovarian cancer [16, 88]. These divergences suggest that further study is needed to shed more light on the underling mechanism of STAT3 signal pathway in pro-tumor microenvironment in different tumor types.

There are several important implications in this meta-analysis. First, it shows that STAT3 expression is related to adverse outcome of most solid tumors. Second, it identifies a subgroup of tumors with unfavorable outcome in gastric cancer, lung cancer, hepatic cancer, prostate cancer and glioblastoma, but with favorable outcome in breast cancer. Finally, it emphasizes the potential of STAT3 to developing a valuable therapeutic target and prognostic biomarker for solid tumor.

This study also has some limitations. First, from the literature we could only extract summarized population-level data rather than individual patient-level data. Second, the method for assessing STAT3 expression and definition of STAT3 positivity are inconsistent. Finally, substantial heterogeneity observed across included studies cannot be fully accounted for by our use of appropriate meta-analytic techniques with random-effects modeling.

In summary, STAT3 expression in solid tumor tissues is associated with poor survival in most solid tumors, which suggests that STAT3 is a valuable prognostic biomarker and a promising therapeutic target for solid tumors.

MATERIALS AND METHODS

This meta-analysis was conducted according to the statement for reporting systematic reviews and meta-analyses [89]. This study summarized and analyzed the results of previous studies, so the ethical approval was not necessary.

Search strategy and study selection

An electronic search of Pubmed, Web of Science and EBSCO were undertaken for studies evaluating STAT3 or p-STAT3 expression and clinical outcome in solid tumors from 1994 to August 2015. The search was performed with subject heading terms including “signal transducer and activator of transcription 3” or “STAT3 transcription factor” or “STAT3” or “phosphorylated signal transducer and activator of transcription 3” or “phosphorylated STAT3 transcription factor” or “phospho-STAT3” and “neoplasms” and the results were limited to human studies of solid tumors. In addition, the entry “signal transducer and activator of transcription 3” or “STAT3 transcription factor” or “STAT3” or “phosphorylated signal transducer and activator of transcription 3” or “phosphorylated STAT3 transcription factor” or “phospho-STAT3” and the name of each specific solid tumor were used for additional studies. A total of 3547, 3542 and 2914 entries were identified, respectively. Inclusion criteria were the measurement of STAT3 and (or) p-STAT3 by immunohistochemistry (IHC), availability of survival data for at least 3 years, and original article written in English. Exclusion criteria were studies evaluating gene expression of STAT3 measured by polymerase chain reaction (PCR) and STAT3 expression in lymph node and myeloid cells. Citation lists of retrieved articles were manually screened to ensure sensitivity of the search strategy. Study selection was based on the association of STAT3 and survival. Two reviewers (Pin Wu and Dang Wu) evaluated independently all of the full articles for study eligibility. Inter-reviewer agreement was assessed using Cohen's kappa coefficient. Disagreement was resolved by consensus.

Data extraction

Overall survival (OS) and disease free survival (DFS) were the primary endpoints of interest. Data were extracted using predefined abstraction forms. The following details were extracted by two authors (Pin Wu and Dang Wu): name of first author, year of publication, country of publication, tumor type, patient number, tumor stage, antibodies used for the evaluation, method and score for STAT3 assessment, and cut-off values to determine STAT3 positivity. Data for 3 and 5 year of OS and DFS were extracted from tables or Kaplan–Meier curves for both STAT3 negative and STAT3 positive group.

The studies included in our meta-analysis were all cohort studies. Two independent authors evaluated the quality of each included study using Newcastle-Ottawa Scale (NOS) [90]. The studies with 6 scores or more were considered as high quality studies. A consensus NOS score for each item was achieved finally.

Data synthesis

The relative frequency of OS and DFS at 3 and 5 years between STAT3 negative and STAT3 positive group was presented as an odds ratio (OR) and its 95% confidence interval (CI). Sensitivity analyses were carried out for different analytical methods and cut-offs for defining STAT3 expression and NOS scores for quality assessment of included studies. Publication bias was assessed by visual inspection of the funnel plot.

Statistical analysis

Data were extracted from the primary publications and combined into a meta-analysis using RevMan 5.3 analysis software (Cochrane Collaboration, Copenhagen, Denmark). Estimates of ORs were weighted and pooled using the Mantel–Haenszel random effect model. Statistical heterogeneity was assessed using the Cochran's Q and I2 statistics. Differences between subgroups were assessed using methods as previous described by Deeks et al. [91]. Meta-regression analysis was conducted using Stata 12.0 software (StataCorp LP, College Station, TX). All statistical tests were two-sided, and statistical significance was defined as P less than 0.05. No correction was made for multiple statistical testing.

SUPPLEMENTARY MATERIALS FIGURES

ACKNOWLEDGMENTS AND FUNDING

We thank all the group members for helpful discussions. This work was supported by grants from the National Natural Science Foundation of China (81572800, PW), the Science and Technology Department of Zhejiang Province (2013c03044–7, YC), and Natural Science Foundation of Zhejiang Province (LY13H160016, YC; Y15H160094, GS and LY15H160041, PW).

Footnotes

CONFLICTS OF INTEREST

The authors declare no competing financial interest.

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