Squamous cell carcinoma antigen,platelet distribution width,and prealbumin collectively as a marker of squamous cell cervical carcinoma

Abstract

BACKGROUND:

Squamous cell cervical carcinoma is the most common gynecological malignant disorder worldwide. Early detection of squamous cell cervical carcinoma is needed for proper clinical management. Squamous cell carcinoma antigen (SCCA) is commonly used as a tumor marker for squamous cell cervical carcinoma. Platelet distribution width (PDW) is an indicator of platelet activation. Prealbumin is a negative acute-phase protein.

OBJECTIVE:

The aim of this study was to investigate the ability of SCCA, PDW, and prealbumin individually or in combination, to distinguish between cervical carcinoma and control subjects.

MEHTODS:

Two hundred and twenty patients with squamous cell cervical carcinoma and 211 control subjects were included in the study. Patients’ characteristics and hematologic tests data at initial diagnosis were collected.

RESULTS:

Our results showed that SCCA and PDW were higher, and prealbumin was lower in cervical carcinoma patients than in control subjects. Single biomarker had AUC value ranging from 0.753 for SCCA to 0.845 for PDW. The combination of SCCA and PDW increased the AUC to 0.900 ( $p<$ 0.0001). In addition, the combination of SCCA, PDW and prealbumin exhibited a significantly larger AUC of 0.917 (0.887–0.942), significantly higher than those of any single marker.

CONCLUSIONS:

The combined use of SCCA, PDW and prealbumin can accurately distinguish squamous cell cervical carcinoma from control subjects. This promising approach could be helpful in early detection of squamous cell cervical carcinoma.

Keywords

Cervical carcinoma platelet distribution width prealbumin squamous cell carcinoma antigen

1. Introduction

Cervical carcinoma is the third most common gynecological malignant disorder and represents the fourth leading cause of cancer-related death in females worldwide [1]. Squamous cell cervical carcinoma comprises 80–90% of cervical cancers. Since the occurrence of cervical carcinoma is a multigenic disease, there are no good serum biomarkers that can be used to screen cervical carcinoma. Although cytology (papanicolaou test) screening has been very successful in lowering cervical carcinoma incidence and mortality, false-positives are common [2]. Therefore, identification of novel markers to find cervical carcinoma in early stage is warranted.

Squamous cell carcinoma antigen (SCCA) is commonly used as a tumor marker for squamous cell cervical carcinoma. SCCA is a 45-kDa glycoprotein with two types: SCCA-1 and SCCA-2. Increased SCCA levels were observed not only in squamous cell carcinoma of the esophagus, lungs, head and neck, and cervix, but also in benign diseases such as skin disorders, cystitis, and pelvic inflammatory disease [3, 4].

Activated platelets play a key role in cancer progression and metastases [5, 6]. A meta-analysis showed that pretreatment thrombocytosis is an independent prognosis predictor in cervical cancer patients [7]. Mean platelet volume (MPV) is an indicator of activated platelets and is associated with gastric cancer, ovarian cancer, lung cancer, colon cancer, and breast cancer [8, 9, 10, 11, 12]. Platelet distribution width (PDW), another platelet parameter, indicates variation in platelet size and differentially diagnoses thrombocytopenia [13]. Previous studies showed that PDW is associated with poor prognosis in gastric cancer, laryngeal cancer, non-small cell lung cancer, and melanoma [14, 15, 16, 17].

Serum prealbumin is a 55-kDa negative acute-phase reactive protein and is synthesized mainly in the liver. It is generally used to determine nutritional status. Recent studies revealed that reduced serum prealbumin came from decreased synthesis following reprioritization of the synthesis of acute-phase proteins and was tightly related to inflammation [18, 19, 20]. Emerging evidence showed that decreased prealbumin is associated with impaired functional status, malignancies, poor outcome, and mortality [21, 22, 23].

Combination of several biomarkers for early detection of cancer may result in enhanced sensitivities and specificities. The aim of the present study was to investigate the ability of SCCA, PDW, and prealbumin, individually or in combination, to distinguish squamous cell cervical carcinoma from normal cervix.

2. Methods

2.1 Study population

The present study included 220 patients with stage Ia-Ib squamous cell cervical carcinoma (mean age 46.9 $\pm$ 8.3 years, range 25–70 years). All patients were admitted to Harbin Medical University Cancer Hospital between January 2015 and December 2015. Clinical staging was according to the criteria of Federation of International Gynecologists and Obstetricians (FIGO). Inclusion criteria included: (1) complete surgical resection and diagnosis of squamous cell cervical carcinoma confirmed by histology; (2) stage Ia-Ib squamous cell cervical carcinoma; (3) untreated before diagnosis. Exclusion criteria included: hematological disorders, coronary artery disease, hypertension, diabetes mellitus, and medical treatment with anticoagulant, statins, and acetylic salicylic acid. Two hundred and eleven control subjects (mean age 47.4 $\pm$ 4.8 years, range 27–64 years) were recruited from Harbin Medical University Cancer Hospital. They were matched for age and body mass index (BMI).

Table 1
The characteristics of the participants according to cervical carcinoma status

Variables	Without cervical	With cervical	P value
	carcinoma	carcinoma
N	211	220
Age (years)	47.4 (4.8)	46.9 (8.3)	0.514
BMI (kg/m ${}^{2}$ )	23.7 (3.3)	23.6 (2.6)	0.824
Prealbumin (mg/L)	310.7 (63.0)	256.8 (54.4)	$<$ 0.001
WBC ( $\times$ 10 ${}^{9}$ /L)	6.1 (1.5)	5.9 (1.7)	0.323
Haemoglobin (g/dl)	138.6 (13.4)	126.0 (17.3)	$<$ 0.001
Platelet ( $\times$ 10 ${}^{9}$ /L)	240.5 (59.3)	229.2 (67.7)	0.065
MPV (fL)	9.8 (1.3)	8.5 (1.2)	$<$ 0.001
PDW (%)	14.8 (2.2)	17.3 (1.1)	$<$ 0.001
SCCA (ng/ml)	0.7 (0.6)	2.5 (3.6)	$<$ 0.001

Values are shown as mean (standard deviation). BMI, body mass index; WBC, white blood cell; MPV, mean platelet volume; PDW, platelet distribution width; SCCA, squamous cell carcinoma antigen.

The study protocol was approved by the Ethics Committee of Harbin Medical University Cancer Hospital of Harbin Medical University. Written informed consents were obtained from all participants.

Table 2

Correlations between clinicopathological features and pre-operative SCCA, prealbumin and PDW in cervical carcinoma

Variables	N (%)	SCCA (ng/ml)	P value	Prealbumin (mg/L)	P value	PDW (%)	P value
Differentiation			0.699		0.572		0.151
Well-differentiated	49 (22.3)	2.7 (3.6)		259.6 (58.5)		17.6 (1.3)
Moderately-differentiated	113 (51.4)	2.6 (4.0)		253.1 (51.5)		17.2 (0.9)
Poorly/undifferentiated	58 (26.4)	2.1 (2.8)		261.8 (56.7)		17.4 (1.3)
FIGO stage			0.632		0.661		0.582
Ia	74 (33.6)	2.3 (3.0)		259.1 (57.8)		17.4 (1.3)
Ib	146 (66.4)	2.6 (3.9)		255.7 (52.7)		17.3 (1.0)
Size of primary tumor			0.018		0.537		0.956
$\leqslant$ 4 cm	170 (68.2)	2.0 (2.5)		255.6 (56.4)		17.3 (1.1)
$>$ 4 cm	50 (22.7)	4.1 (5.9)		261.0 (47.0)		17.3 (1.2)

Table 3

Receiver operating characteristic curve analyses showing the utility of alone or combined markers for differentiating of control subjects and cervical carcinoma

Tumor marker	Sensitivity	Specificity	PPV	NPV	AUC (95%CI)
SCCA (ng/ml)	56.8	84.4	79.1	65.2	0.753 (0.710–0.793)
Prealbumin (mg/L)	60.0	80.6	76.3	65.9	0.756 (0.712–0.795)
PDW (%)	70.0	83.4	81.5	72.7	0.845 (0.808–0.878)
SCCA $+$ PDW	79.1	83.4	76.6	88.2	0.900 (0.867–0.926)
SCCA $+$ Prealbumin	69.6	84.4	82.3	72.7	0.825 (0.786–0.860)
SCCA $+$ PDW $+$ Prealbumin	86.8	80.6	82.3	85.4	0.917 (0.887–0.942)

SCCA, squamous cell carcinoma antigen; PDW, platelet distribution width; PPV, positive predictive value; NPV, negative predictive value; AUC, area under curve.

2.2 Clinical examination and biochemical measurements

All the subjects underwent physical examination. BMI was calculated as weight (kg) divided by height squared (m ${}^{2}$ ). Clinical data included smoking status, medical history and medication use. The whole blood samples were drawn in EDTA-containing tubes after an 8-h overnight fasting and all samples were processed within 30 min after blood collection with an autoanalyzer (Sysmex XE-2100, Kobe, Japan). The inter- and intra-assays coefficients of variation (CVs) of all these assays were below 5%.

2.3 Statistical analyses

All data were expressed as means $\pm$ SD. When the characteristics between two groups were compared, continuous variables were compared with the Student’s $t$ test. When the characteristics among three groups were compared, continuous variables were compared with the one-way ANOVA. The Chi-square test was used for categorical variables. The data were analyzed using SPSS Statistics version 22.0 (SPSS Inc., Chicago, IL, USA). Receiver operating characteristic (ROC) curves were constructed, and the area under the curve (AUC) was calculated to evaluate the specificity and sensitivity using MedCalc version 15.0. A two-tailed $p$ -value less than 0.05 was considered significant in all tests.

Figure 1.

Receiver-operator characteristics (ROC) curve for serum SCCA, PDW, and prealbumin combined showing sensitivity and 1-specificity of differential diagnosis of cervical carcinoma versus normal cervix.

3. Results

The characteristics of cervical carcinoma patients and control subjects are shown in Table 1. The mean age of cervical carcinoma patients and control subjects was 46.9 (8.3) years and 47.4 (4.8) years, respectively. Prealbumin, MPV and haemoglobin levels were significantly reduced, and PDW and SCCA were significantly increased in cervical carcinoma patients compared with the control subjects ( $p<$ 0.001). However, there were no significant differences in white blood cell (WBC) and platelet count between two groups.

Correlations between clinicopathological features and pre-operative SCCA, PDW and prealbumin in cervical carcinoma are demonstrated in Table 2. There were no significant differences in SCCA, prealbumin and PDW between the differentiation stage and the FIGO stage. SCCA showed a markedly difference in different tumor size group ( $p=$ 0.018). However, prealbumin and PDW showed no difference in different tumor size group.

To determine the predictive accuracy of each of the independent biomarkers based on the optimal cutoff value, we used ROC analysis to assess the AUC for single biomarker and the combination of three (Table 3). When used to analyze control subjects versus cervical carcinoma, prealbumin, SCCA, and PDW alone had the high specificity (80.6%–84.4%), but at the cost of unsatisfactory low sensitivity (56.8%–70.0%). The sensitivity increased and specificity did not changed when the combination of SCCA, PDW and prealbumin was applied. Single biomarker had AUC value ranging from 0.753 for SCCA to 0.845 for PDW; the combination of SCCA and PDW increased the AUC to 0.900 ( $p<$ 0.0001). In addition, the combination of SCCA, PDW and prealbumin exhibited a significantly larger AUC of 0.917 (0.887–0.942) compared with the combination of SCCA and PDW ( $p=$ 0.0008).

4. Discussion

Cervical carcinoma ranks as the fourth most common cause of death in women. Good tumor markers in early screening of cervical carcinoma are lacking. In this study, we provided evidence that the combined use of SCCA, PDW and prealbumin could accurately distinguish squamous cell cervical carcinoma from control subjects.

Activated platelet is a paramount mediator in tumor development, tumor cell growth, angiogenesis, and metastasis. MPV and PDW are commonly used indicators of activated platelet. Reduced MPV is regarded as an enhanced consumption of large platelets in inflammatory states [24]. At present, the mechanism of increased PDW in cervical carcinoma has not been determined. Bone marrow cells (including megakaryocytes) dys-regulation may contribute to changed MPV and PDW.

Prealbumin was generally used to assess the nutritional status in various types of cancers, including head and neck cancer, and ovarian cancer [25, 26]. Recent studies showed that prealbumin was associated with response to treatment and outcomes in patients with non-small cell lung cancer and epithelial ovarian carcinoma [21, 27]. Moreover, hypoalbuminemia was a poor prognostic indicator in esophageal cancer and advanced colorectal cancer [28, 29]. Several studies have confirmed that inflammation is associated with reduced prealbumin [20, 30, 31, 32]. However, the association between prealbumin and cervical carcinoma has not been investigated in previous research. Our results showed that prealbumin was lower in cervical carcinoma patients than in control subjects. In addition, prealbumin plays a complementary role along with SCCA and PDW in cervical carcinoma evaluation.

Increased SCCA levels are commonly found in squamous cell carcinoma of the esophagus, lung, head and neck, and cervix. Furthermore, SCCA at initial diagnosis is correlated with the tumor stage, size, depth of invasion, and lymph node metastasis [33]. However, SCCA may also increase in benign diseases such as pelvic inflammatory disease and cystitis, suggesting the need for other promising biomarkers in the primary screen to detect early disease and renal failure [3, 4].

For the diagnosis of squamous cell cervical cancer, few tumor markers are highly sensitive or specific. SCCA has low specificity and sensitivity. Our study demonstrated that the AUC value for discriminating cervical carcinoma patients from healthy controls using this three biomarkers was 0.917, significantly higher than any single index. In addition, SCCA, PDW, and prealbumin levels are routinely recorded in the clinical setting and can be easily estimated prior to treatment. Thus, a combination of three serum markers is a more comprehensive indicator for cervical detection than single biomarker.

This study has limitations: (1) the study was carried out in a single center; (2) because prealbumin levels are affected by changes in transcapillary escape, further study is needed to evaluate transcapillary escape rate of prealbumin; (3) the results cannot be generalized because our study includes only Chinese participants.

In conclusion, the study showed that the combined use of SCCA, PDW and prealbumin might be useful in the distinction of squamous cell cervical carcinoma and control subjects. Further studies are needed to validate these results before using these markers in routine practice.

Footnotes

Acknowledgments

This work was supported by the Scientific Research Foundation for Post-Doctoral Scholars settled in Heilongjiang (RC2016XK004068).

Conflict of interest

The authors declare no conflict of interest.

References

Jemal

et al., Global cancer statistics, CA Cancer J Clin 61(2) (2011), 69–90.

Saslow

et al., American Cancer Society, American Society for Colposcopy and Cervical Pathology, and American Society for Clinical Pathology screening guidelines for the prevention and early detection of cervical cancer, Am J Clin Pathol 137(4) (2012), 516–542.

Gadducci

et al., The serum assay of tumour markers in the prognostic evaluation, treatment monitoring and follow-up of patients with cervical cancer: A review of the literature, Crit Rev Oncol Hematol 66(1) (2008), 10–20.

Jao

M.S.

et al., Long-term follow up of cervical cancer patients with unexplained squamous cell carcinoma antigen elevation after post-therapy surveillance using positron emission tomography, J Obstet Gynaecol Res 36(5) (2010), 1003–1008.

Bambace

N.M.

and Holmes

C.E.

, The platelet contribution to cancer progression, J Thromb Haemost 9(2) (2011), 237–249.

Goubran

H.A.

et al., Platelet-cancer interactions, Semin Thromb Hemost 40(3) (2014), 296–305.

Cheng

et al., The association of pretreatment thrombocytosis with prognosis and clinicopathological significance in cervical cancer: A systematic review and meta-analysis, Oncotarget 8(15) (2017), 24327–24336.

Kilincalp

et al., Mean platelet volume could be possible biomarker in early diagnosis and monitoring of gastric cancer, Platelets 25(8) (2014), 592–594.

Kemal

et al., Mean platelet volume could be a useful biomarker for monitoring epithelial ovarian cancer, J Obstet Gynaecol 34(6) (2014), 515–518.

10.

Kumagai

et al., Prognostic significance of preoperative mean platelet volume in resected non-small-cell lung cancer, Mol Clin Oncol 3(1) (2015), 197–201.

11.

J.Y.

et al., Elevated mean platelet volume is associated with presence of colon cancer, Asian Pac J Cancer Prev 15(23) (2014), 10501–10504.

12.

et al., Pre-treatment mean platelet volume associates with worse clinicopathologic features and prognosis of patients with invasive breast cancer, Breast Cancer 23(5) (2015), 752–760.

13.

Kaito

et al., Platelet size deviation width, platelet large cell ratio, and mean platelet volume have sufficient sensitivity and specificity in the diagnosis of immune thrombocytopenia, Br J Haematol 128(5) (2005), 698–702.

14.

Zhang

et al., Higher platelet distribution width predicts poor prognosis in laryngeal cancer, Oncotarget 8(29) (2017), 48138–48144.

15.

Cui

M.M.

et al., Platelet distribution width correlates with prognosis of non-small cell lung cancer, Sci Rep 7(1) (2017), 3456.

16.

et al., Increased platelet distribution width predicts poor prognosis in melanoma patients, Sci Rep 7(1) (2017), 2970.

17.

Zhang

et al., Platelet distribution width correlates with prognosis of gastric cancer, Oncotarget 8(12) (2017), 20213–20219.

18.

Brãmer

et al., Study protocol: Prediction of stroke associated infections by markers of autonomic control, BMC Neurol 14 (2014), 9.

19.

Gürlek

G.D.

et al., Assessment of nutritional status in children with cancer and effectiveness of oral nutritional supplements, Pediatr Hematol Oncol 32(6) (2015), 423–432.

20.

Liu

et al., The role of perioperative oral nutritional supplementation in elderly patients after hip surgery, Clin Interv Aging 10 (2015), 849–858.

21.

Kawai

and Ota

, Low perioperative serum prealbumin predicts early recurrence after curative pulmonary resection for non-small-cell lung cancer, World J Surg 36(12) (2012), 2853–2857.

22.

Yovita

et al., Correlation between anthropometrics measurements, prealbumin level and transferin serum with Child-Pugh classification in evaluating nutritional status of liver cirrhosis patient, Acta Med Indones 36(4) (2004), 197–201.

23.

Chaturvedi

and Kaczmarek

, Mmp-9 inhibition: A therapeutic strategy in ischemic stroke, Mol Neurobiol 49(1) (2014), 563–573.

24.

Gasparyan

A.Y.

et al., Mean platelet volume: A link between thrombosis and inflammation, Curr Pharm Des 17(1) (2011), 47–58.

25.

Unal

et al., Prealbumin is a more sensitive marker than albumin to assess the nutritional status in patients undergoing radiotherapy for head and neck cancer, Contemp Oncol (Pozn) 17(3) (2013), 276–280.

26.

Geisler

J.P.

et al., Nutritional assessment using prealbumin as an objective criterion to determine whom should not undergo primary radical cytoreductive surgery for ovarian cancer, Gynecol Oncol 106(1) (2007), 128–131.

27.

S.Y.

et al., Nutritional predictors of survival in terminally ill cancer patients, J Formos Med Assoc 102(8) (2003), 544–550.

28.

Pan

Tao

and Sun

, Subjective global assessment and prealbumin levels of esophageal cancer patients undergoing concurrent chemoradiotherapy, Nutr Hosp 31(5) (2015), 2167–2173.

29.

Bystrom

et al., Evaluation of predictive markers for patients with advanced colorectal cancer, Acta Oncol 51(7) (2012), 849–859.

30.

Ingenbleek

and Young

V.R.

, Significance of transthyretin in protein metabolism, Clin Chem Lab Med 40(12) (2002), 1281–1291.

31.

Kopple

J.D.

et al., Observations with regard to the National Kidney Foundation K/DOQI clinical practice guidelines concerning serum transthyretin in chronic renal failure, Clin Chem Lab Med 40(12) (2002), 1308–1312.

32.

Rambod

et al., Association of serum prealbumin and its changes over time with clinical outcomes and survival in patients receiving hemodialysis, Am J Clin Nutr 88(6) (2008), 1485–1494.

33.

Reesink-Peters

et al., Preoperative serum squamous cell carcinoma antigen levels in clinical decision making for patients with early-stage cervical cancer, J Clin Oncol 23(7) (2005), 1455–1462.