The prognostic factors and multiple biomarkers in young patients with colorectal cancer

The incidence of colorectal cancer (CRC) in young patients (≤50 years of age) appears to be increasing. However, their clinicopathological characteristics and survival are controversial. Likewise, the biomarkers are unclear. We used the West China (2008-2013, China), Surveillance, Epidemiology, and End Results program (1973-2011, United States) and Linköping Cancer (1972-2009, Sweden) databases to analyse clinicopathological characteristics, survival and multiple biomarkers of young CRC patients. A total of 509,934 CRC patients were included from the three databases. The young CRC patients tended to have more distal location tumours, fewer tumour numbers, later stage, more mucinous carcinoma and poorer differentiation. The cancer-specific survival (CSS) of young patients was significantly better. The PRL (HR = 12.341, 95% CI = 1.615-94.276, P = 0.010), RBM3 (HR = 0.093, 95% CI = 0.012-0.712, P = 0.018), Wrap53 (HR = 1.952, 95% CI = 0.452-6.342, P = 0.031), p53 (HR = 5.549, 95% CI = 1.176-26.178, P = 0.045) and DNA status (HR = 17.602, 95% CI = 2.551-121.448, P = 0.001) were associated with CSS of the young patients. In conclusion, this study suggests that young CRC patients present advanced tumours and more malignant pathological features, while they have a better prognosis. The PRL, RBM3, Wrap53, p53 and DNA status are potential prognostic biomarkers for the young CRC patients.

was performed by matching all patients against the Swedish Cancer Register and the Cause of Death Register until July 2013. Survival data including local/distant recurrence, disease-free time, survival time and death causes was collected. All patients gave the required informed consent. The surgical specimens once obtained, were snap-frozen immediately in dry ice and stored at -80 • C freezer until further biomarker analysis.

Histopathological characteristics analysis
The histopathological characteristics, inflammatory infiltration, necrosis and fibrosis were included in this study, according to our published data. Two investigators including one pathologist independently read the slides in a blinded fashion without any knowledge of the clinicopathological and biological information. The characteristics were determined in 10-20 areas (depending on the size of the section) at x400 magnification. For each tumor/biopsy, 1-5 sections were analyzed, and a mean score was reached. In the cases with discrepant results in the staining score, a consensus score was reached after re-examination 25 .
In all runs, negative and positive controls were included.
All immunohistochemical slides for each biomarker were independently reviewed scored by two investigators (including one pathologist) without knowledge of clinicopathological and biological information In the case of discrepancy in individual scores, both investigators re-evaluated the slides together and reached a consensus before combining the individual scores. To avoid an artificial effect, the cells on the margins of the sections and in areas with poor morphology were not counted.

Microsatellite testing and analysis
The microsatellite status (MSS and MSI) was determined using mononucleotide marker BAT-26 as an instability marker, by PCR based assays as previous describing 40 . The BAT-26 locus was amplified by a primary and a secondary PCR using the forward primers: 5′-TGACTACTTTTGACTT CAGCC-3´ and the reverse primer 5´-AACCATTCAACATTTTTAACCC-3′ (Life Technologies, Carlsbad, CA). The primary PCR was carried out in a mixture containing 20 ng DNA, 1 × magnesiumfree buffer, 1.5 mM MgCl2, 0.2 mM dNTP, 2 μM of each primer, 0.5 units Taq polymerase (Promega, Madison, WI)) and water to a final volume of 19.5 μl. The PCR was carried out with initiating denaturation at 94 • C for 4 min, 40 cycles of 94 • C for 1 min, 52 • C for 45 s and 72 • C for 45 s, extension at 72 • C for 10 min. A negative control was included in each run. To incorporate [α-33 P]dATP (Amersham Pharmacia Biotech, Bucks, UK) into the samples a secondary PCR was carried out. The secondary PCR was carried out under the same conditions as the primary PCR except that the cycles were reduced to 15. After the PCR the DNA products were denatured by adding 15 μl Blue Juice (containing formamide, xylene cyanol FF, bromophenol blue and EDTA) and incubated at 90 • C for 5 min. The DNA products were separated on a denaturing 6% polyacrylamide gel containing 8 M urea by electrophoresis. The gel was dried and detection was carried out by autoradiography.

TUNEL assay
Apoptotic cells were detected by the terminal deoxynucleotidy transferase-mediated dUTP-biotin nick end-labelling (TUNEL) assay 41 . Five μm-thick sections were cut from the paraffin blocks of the surgical specimen. The sections were deparaffinized in xylene, rehydrated, and incubated with 20 μg/mL proteinase K (Boehringer-Mannheim Biochemicals, Indianapolis, IN) for 15 minutes and rinsed in distilled water. Endogenous peroxidase activity was inhibited with 2% hydrogen peroxide. . The Apop-Tag in situ apoptosis detection kit (Oncor, Gaithersburg, MD) was used to detected apoptosis.
The sections were then incubated with equilibration buffer for 10-15 seconds and TdT enzyme in humidified atmosphere at 37 • C for 90 minutes. They subsequently were put into prewarmed working strength stop/wash buffer at room temperature for 10 minutes and incubated with antidigoxigenin-peroxidase for 45 minutes. Staining was done with 0.05% 3, 3-diaminobenzidine tetrahydrochloride (Sigma Chemical Co. St. Louis, MO), and counter staining was performed in methyl green. A section from rat mammary gland (Oncor) was included in each run as a positive control. To produce DNA fragments, we treated the control sections with 2 μg/mL DNAase at 37 • C for 30 minutes before we labeled the control sections by TUNEL assay.

Flow cytometry
DNA content and S-phase fraction (SPF) were measured by flow cytometry. The details were described previously 23 . Fifty μm-thick sections from the paraffin blocks of the surgical specimen were prepared for measurements in a FACScan flow cytometer (Becton-Dickinson, San Jose, CA).
Normal diploid cells from the same specimens were used as internal controls. Diploid tumors were defined as having a single G0/1 peak. Tumors were considered non-diploid if there was evidence of more than one distinct G0/1 peak. SPF was estimated using a rectangular model. The number of Sphase cells was calculated by multiplying the number of channels between the G0/1 and G2/M peaks by the mean number of cells per channel in a part of the S-phase interval judged by the operator to be representative. Small disturbing peaks in the S-phase region could be excluded when the SPF, which was divided into two categories: <10% and ≥10%, was calculated. Background correction was made by subtracting a constant estimated in each of the histograms. The mean number of registrations per channel in an area to the right of the G2/M peak was calculated and subtracted from the mean number of registrations per channel in the S-phase interval.

Functional analysis
To further analyze the function of the significant biomarkers, the STRING resource was utilized for PPI network analysis. The corresponding gene symbol of the protein was used for functional analysis. The STRING resource (version 9.1) contained the PPI information from numerous sources including experimental data, computational prediction methods (neighborhood, gene fusion, cooccurrence, and co-expression) and public text collections 42 . All the data from STRING was weighted and integrated for PPI network analysis. A confidence score was assigned to each protein interaction.
The score was calculated by benchmarking the performance of the predictions against a common reference set of trusted associations, which was the functional grouping of proteins maintained at KEGG database. The WebGestalt was utilized for comprehensive gene functional enrichment analysis, including GO enrichment and KEGG pathway enrichment 43, 44 . Human protein coding Entrez genes was set as the reference list for enrichment analysis in the WebGestalt, while hypergeometric distribution algorithm was set as statistical method and Benjamini-Hochberg (BH) method as the multiple test adjustment. A P value of less than 0.05 was considered as the cut-off criterion.