Serum protein profiling to identify biomarkers for small renal cell carcinoma.

Diagnostic biomarkers for early detection of renal cell carcinoma (RCC) are in great need. In the present study, we compared the serum protein profiles of patients with small RCC to those of healthy individuals to identify the differentially expressed proteins with potential to serve as biomarkers. Serum samples were collected from 10 patients with small RCC and 10 healthy individuals. The serum protein expression profiles were analyzed by two-dimensional (2-D) gel electrophoresis. Twenty-seven proteins with differences in expression levels between RCC patients and healthy volunteers were identified. Of these, 19 were expressed at different levels and eight were expressed in serum from the RCC group, but not from the control group. Six differentially expressed proteins identified by using mass spectrometry included coagulation factor XIII B, complement C3 and its precursor, misato homolog 1 (isoform CRA_b), hemopexin, and alpha-1-B-glycoprotein. Some of these serum proteins are known regulators of tumor progression in human malignancies. In conclusion, we successfully applied 2-D gel electrophoresis and identified six serum proteins differentially expressed between patients with small RCC and healthy volunteers. These proteins may provide novel biomarkers for early detection and diagnosis of human RCC.

[Proteomic analysis in combination with CT diagnosis to distinguish renal cell carcinoma from renal benign masses].

OBJECTIVE: To identify the proteomic differences between renal cell carcinoma (RCC) and renal benign masses and to evaluate the diagnostic value of parallel and serial test combining with CT and surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS). METHODS: Serum samples were collected from 96 patients with renal tumors, 62 RCC cases and 34 renal benign mass cases, all of which had been evaluated by CT before surgery. The sera were analyzed using IMAC-Cu2+ ProteinChip system by SELDI-TOF-MS. The decision tree was generated by Biomark Pattern based on the sera of 42 RCC cases and 22 renal benign mass cases and was blind-tested by the rest sera samples. The parallel method and serial method combining CT and SELDI were also used to distinguish RCC and renal benign masses. RESULTS: The sensitivity and specificity of the decision tree were 85.7% (36/42) and 90.9% (20/22) respectively. The sensitivity and specificity of the double-blind test were 75.0% (15/20) and 83.3% (10/12) respectively. CT showed higher sensitivity but lower specificity in detecting RCC. While combining CT with SELDI-TOF-MS, the sensitivity and specificity could be improved. CONCLUSION: Three peaks with the molecular weights of 4657.56, 2955.95, and 3278.00 were detected which are potentially useful for differentiating RCC and renal benign masses. Using serial method combining CT and the decision tree based on these three proteins improves the sensitivity and positive predictive values of diagnosing RCC to 100%.

Detection of mutant p53 protein in workers occupationally exposed to benzidine.

To investigate the expression of mutant p53 protein in workers occupationally exposed to benzidine, we detected mutant p53 protein by immuno-PCR assay in the serum of 331 benzidine-exposed healthy workers, while we classified exfoliated urothelial cells in urine samples with Papanicoloau's grading (PG). The Papanicoloau's grading classified exfoliated urothelial cells of the subjects from grade I (normal cells) to grade III (suspicious malignant cells). The subjects were also divided into high, medium and low exposure groups according to the exposure intensity index. The results revealed that mutant p53 protein in the medium and high exposure groups were significantly higher than the in low exposure group (p<0.05), and in PG II and III were significantly higher than in the PG I (p<0.05). There was no significant differences among Papanicoloau's gradings strata in the low exposure group on the incidence and quantity of mutant p53 protein. In the medium and high exposure groups, the incidence and/or quantity of mutant p53 protein in the stratum of PG II and/or III were significantly higher than that of PG I (p<0.05). Detection of mutant p53 protein in conjunction with benzidine exposure level and Papanicoloau's gradings of exfoliated urothelial cells could provide more information to help us elevate surveillance efficiency and diagnose bladder cancer in the early period.

[Serum proteomic patterns may assist the diagnosis of solitary pulmonary nodules].

OBJECTIVE: To screen relatively specific biomarkers in serum from lung adenocarcinoma patients by surface-enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOFMS), and to investigate the clinical value of SELDI-TOF-MS in differentiation of benign from malignant solitary pulmonary nodules (SPN). METHODS: Serum samples from 71 lung adenocarcinoma patients and 71 healthy volunteers with matched gender, age and history of smoking were analyzed using WCX2 ProteinChip to screen potential biomarkers. 28 patients received surgical treatment among total 53 patients with SPN. The clinical value of SELDI-TOF-MS in differentiation of benign from malignant solitary pulmonary nodules was evaluated by pathological diagnosis. RESULTS: Five highly expressed potential biomarkers were identified with the relative molecular weights of 4047.79 Da, 4203.99 Da, 4959. 81 Da, 5329. 30 Da and 7760.12 Da. The postoperative pathologic diagnosis was lung adenocarcinoma in 24 patients with SPN, validating the clinical value of the 5 potential biomarkers. CONCLUSION: SELDI-TOF-MS technology is a quick, easy, convenient, and high-throughput analyzing method capable of screening several relatively specific potential biomarkers from the serum of lung adenocarcinoma patients and may have attractive clinic value in differentiation of solitary pulmonary nodules.

[Preliminary study of serum proteome biomarkers of hypopharyngeal squamous cell carcinoma].

OBJECTIVE: To screen the serum proteome biomarkers of hypopharyngeal squamous cell carcinoma (HSCC) and to establish a predictive model for early detection of HSCC. METHODS: Serum samples were collected from 48 HSCC patients before surgery and 52 age and sex-matched individuals without cancer used as controls. The samples were divided into 2 sets: training set (including 36 HSCC patients and 36 controls) and blind testing set (including 12 HSCC patients and 16 controls). With WCX2 and IMAC3 protein chips, surface-enhanced laser desorption/ionization (SELDI) was used to analyze the serum protein profiling. 72 samples of the training set were analyzed by a decision tree algorithm to be able to differentiate HSCC patients from controls. Double-blind test was used to determine the sensitivity and specificity of the classification model. RESULTS: Ranging from 2000 - 50000 (M/Z), 11 potential biomarkers on WCX2 and 19 biomarkers on IMAC3 protein chip could differentiate HSCC patients from the control set (P < 10(-5)). Among them 4 candidate protein peaks with the m/z values of 7796, 4216, 5927, and 5361 were selected to be used to establish a predictive model by Biomarker Pattern Software. The model separated effectively the HSCC samples from the control samples, achieving a sensitivity of 94.44%, and a specificity of 88.89%. An accuracy of 85.71% (24/28), sensitivity of 91.67% (11/12), specificity of 81.25% (13/16), positive predictive value of 78.57%% (11/14), and negative predictive value of 92.85% (13/14) were validated in the double-blind testing set. CONCLUSION: The SELDI-TOF-MS Protein Chip combined with artificial intelligence classification algorithm helps find serum proteome biomarkers and establish predictive model for early diagnosis of HSCC. This technique has potential for the development of a screening test for the detection of HSCC.

[Expression of protein p53 in workers occupationally exposed to benzidine and bladder cancer patients.].

OBJECTIVE: To study expression of mutant p53 protein in workers occupationally exposed to benzidine and bladder cancer patients. METHODS: Mutant p53 protein in serum from the workers occupationally exposed to benzidine and bladder cancer patients were determined with Immuno-PCR, while exfoliated urothelial cells in the urine samples were classified with Papanicolau grading. RESULTS: Positive rate of mutant p53 protein increased with the exposed intensity index in workers occupationally exposed to benzidine. The positive rate of mutant p53 protein in bladder cancer patients (83.3%) was significantly higher than that in the group 1 of exposed intensity index. The average scanning integrals of PCR amplified band in the group of bladder cancer patients and group 2 of exposed intensity index were both higher than that in the group 1 significantly. Workers in the groups of different exposed intensity indices were further stratified according to Papanicolau grades. In the group 2 of exposed intensity index, the average scanning integrals of PCR amplified band in the stratum of Papanicolau grade II and III were significantly higher than that in the strata of Papanicolau grade I. And in the group 3 of exposed intensity index, the positive rate of mutant p53 protein in the strata of Papanicolau grade III was higher than that in the strata of Papanicolau grade I significantly. CONCLUSION: The increase of exposed intensity may not only result in the positive rate of mutant p53 protein, but also the quantity of mutant p53 protein in serum within the low range of benzidine exposure. Once the exposed intensity was beyond that spectrum, the positive rate of mutant p53 protein in serum and the average scanning integrals of PCR amplified band were no longer enhanced with the increase of exposed intensity. There was tight correlation between Papanicolau grade of exfoliated urothelial cells and the positive rate or the quantity of mutant p53 protein for the higher benzidine exposure intensity.

Application of SELDI-TOF-MS to identify serum biomarkers for renal cell carcinoma.

PURPOSE: Early diagnosis is critical for improving the outcome of patients with renal cell carcinoma (RCC). In this study, we applied a proteomic approach to identify serum biomarkers associated with different stages of renal tumor development. MATERIALS AND METHODS: The protein expression profiles in patient serum samples were analyzed using surface enhanced laser desorption/ionization time-of-flight mass spectroscopy (SELDI-TOF-MS). The subjects included 65 patients with renal cell carcinomas, 34 with benign renal tumors, and 69 normal controls. A diagnostic decision tree was developed and validated based on the differentially expressed proteins between the serum of patients with small (

[Serum proteomic spectra of esophageal squamous cell carcinoma patients analyzed with IMAC3 protein chip].

BACKGROUND & OBJECTIVE: Serum protein fingerprinting technology can help to identify the molecular changes related to esophageal carcinogenesis. This study was to screen serum markers and establish the predictive models that may be of help to serologic diagnosis of esophageal squamous cell carcinoma (ESCC). METHODS: Serum samples were collected from 68 ESCC patients and 44 age-and sex-matched healthy subjects, and randomized into a training set (55 ESCC patients and 35 healthy subjects) and a blind testing set (13 ESCC patients and 9 healthy subjects). Serum samples were applied to immobilized metal affinity capture (IMAC3) proteinchip surfaces and tested by surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS). The data were analyzed by Biomarker Wizard software to screen serum proteomic biomarkers. Decision classification tree models were established by bioinformatics. Double-blind test was used to determine the sensitivity and specificity of the classification tree models. RESULTS: A total of 78 effective protein peaks were detected at the molecular range of 1.5 to 20 ku, among which 25 were significantly different between ESCC patients and healthy subjects (P<0.001). All the peptide pattern data were sampled randomly for 1,000 times using 3-cross validation approach, and 1,000 decision tree models were obtained. Twenty decision trees with the highest cross validation rate were chosen to construct the classification models which can differentiate ESCC patients from healthy subjects. With these decision trees, 18 samples were correctly forecasted from 22 blind testing samples, with a sensitivity of 92.31% and a specificity of 66.67%. CONCLUSION: SELDI-TOF-MS technique combined with decision tree model can help to identify serum proteomic biomarkers related to ESCC and the predictive models can discriminate ESCC patients from healthy people effectively.