Introduction

Colorectal cancer (CRC) is a major cause of cancer mortality worldwide, with an estimated one million new cases and a half million deaths each year1. In the United States, although incidence of CRC steadily declined2, it is still the third most common cancer and ranked as third leading cause of cancer-related deaths3. The same phenomenon was observed in Sweden, where CRC is the second most common cancer type in both men and women4. In some Asian countries, such as China, the incidence of CRC has increased 2-4 fold and reached to the level of the Western countries during the past decades5.

Besides the improvement of surgical and adjuvant therapy, these decreases of CRC incidence are partially attributed to population based CRC screening which is generally recommended to begin at 50 years of age. In sharp contrast to overall decreasing trends, the incidence of CRC in young patients (≤50 years of age) appears to be increasing6. Since one of the earliest articles describing young CRC patients published in 19397, a series of investigations reported the clinicopathological features and survival of young CRC patients. However, because of the likely biases associated with single-institution experiences or limit cohort sizes, the data vary markedly. Most afflicted individuals lack any identifiable risk factor for their development or potential biomarker for prognosis prediction. The mechanisms underlying the apparent increase in CRC among young patients are poorly understood.

In the present study, we analysed clinicopathological characteristics, prognostic factors and survival of young CRC patients from the West China (WC), Surveillance, Epidemiology and End Results program (SEER) and Linkö** Cancer (LC) databases. Furthermore, we assessed the molecular features and the prognostic value of these biomarkers in young CRC patients in LC database.

Results

Patient characteristics

We have identified a total of 509,934 eligible patients with CRC in three databases (n = 5,918 in WC, n = 503,002 in SEER and n = 1,014 in LC). Patient demographic and clinicopathological characteristics of each database are shown in Supplementary Table 1. We divided the patients into two groups according to age for analysis: young group (≤50 years of age at diagnosis, n = 43,821) and elderly group (>50 years of age at diagnosis, n = 466,113). There were 530 (9.0%), 43,236 (8.6%) and 55 (5.4%) young patients in WC, SEER and LC, respectively.

Clinicopathological differences between two age groups

Compared with elderly group, significant differences in young group had been observed concerning the clinicopathological characteristics as follows (Table 1): gender (fewer males in WC and more males in SEER), tumour location (occurring predominately on left colon and the rectum in WC, SEER and LC), tumour numbers (fewer cases with multiple tumours in WC and SEER), TNM stage (later stage in WC and SEER), tumour growth pattern (more frequent in expansive growth in WC), histological type (more mucinous carcinoma in WC and SEER), differentiation (poorer differentiation in WC and SEER), surgical type (more patients underwent radical surgery in WC) and radiotherapy (more patients received radiotherapy in SEER).

Table 1 Clinicopathological characteristics of colorectal cancer patients according to age groups.
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Survival differences between two age groups

The follow-up information is available in two databases (SEER and LC). The median follow-up period in SEER and LC was 75 months (range, 0-467 months) and 87 months (range, 0-349 months), respectively. In SEER, the 3, 5, 10-year overall cancer-specific survival (CSS) rates were 73.2%, 66.9%, 61.7% in young group and 66.9%, 60.5%, 54.3% in elderly group, respectively. The CSS of young patients was significantly better than elderly patients (P < 0.001, Figure 1a). In LC, the 3, 5, 10-year overall CSS rates were 76.7%, 74.7%, 66.2% in young group and 71.1%, 62.9%, 56.9% in elderly group, respectively. Similarly, the CSS of young patients was better than elderly patients, although the difference was not statistically significant (P = 0.102, Figure 1b). When the survival analyses were stratified by each stage in SEER and LC, the same trend of CSS at stage I (P < 0.001, P = 0.245, Supplementary Figure 1a), II (P < 0.001, P = 0.152, Supplementary Figure 1b), III (P < 0.001, P = 0.524, Supplementary Figure 1c) and IV (P < 0.001, P = 0.132, Supplementary Figure 1d) had been found.

Figure 1
figure 1

The cancer-specific survival of young and elderly CRC patients in (a) SEER, P < 0.001 and (b) LC, P = 0.102.

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In LC, the 3 and 5-year disease-free survival (DFS) rates were 68.4% and 63.2% in young group and 69.6% and 62.3% in elderly group, respectively. The DFS was not significantly different between two age groups (P = 0.690, Fig. 2). Recurrence rate was 27.6% in young group and 34.1% in elderly group (P = 0.606). In consideration of recurrence type, local recurrence rate (15.8% vs. 13.5%, P = 1.000) and distant metastasis rate (26.3% vs. 35.0%, P = 0.611) had no significant difference between young group and elderly group.

Figure 2
figure 2

The disease-free survival (DFS) of young and elderly CRC patients in LC. The DFS was not significantly different between two age groups, P = 0.690.

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Multiple biomarkers differences between two age groups

The differences of multiple biomarkers between young and elderly group in LC are shown in Table 2. Compared with elderly group, there were more young patients with moderate/strong PRL (phosphatase of regenerating liver, P = 0.014), positive Wrap53 (WD40-encoding RNA antisense to p53, P = 0.017), positive RBM3 (RNA-binding motif protein3, P = 0.018), weak TAZ (Tafazzin, P = 0.044) expression and DNA diploid (P = 0.030). The impact of the studied characteristics on prognosis by univariate analyses is presented in Table 3. In young group, TNM stage, tumour growth pattern, surgical type, recurrence, PRL (hazard ratios, HR = 12.341; 95% confidence intervals, CI = 1.615-94.276; P = 0.010; Supplementary Figure 2a), RBM3 (HR = 0.093, 95% CI = 0.012-0.712, P = 0.018; Supplementary Figure 2b), Wrap53 (HR = 1.952, 95% CI = 0.452-6.342, P = 0.031; Supplementary Figure 2c), p53 (HR = 5.549, 95% CI = 1.176-26.178, P = 0.045; Supplementary Figure 2d) and DNA status (HR = 17.602, 95% CI = 2.551-121.448, P = 0.001; Supplementary Figure 2e) were strongly associated with CSS. Nevertheless, TAZ did not have any prognostic value for CSS although its expression was different in two age groups. Taking into consideration the limited number of young group, we did not further analyse the prognostic value of these biomarkers by multivariable modelling.

Table 2 Analysis of multiple biomarkers in colorectal cancer patients according to age groups.
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Table 3 Univariate survival analysis of biomarkers and prognostic factors in colorectal cancer patients.
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Protein-protein interactions (PPIs) network and pathways of biomarkers in young CRC group

All PPIs for each significant biomarker with a confidence score ≥0.4 (medium confidence) were fetched from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) resource (Supplementary Table 2). Then the top 10 confident proteins for each biomarker (total 55 proteins) were used to build the final PPIs network (Supplementary Figure 3) and to do the further gene function enrichment analysis. According to the gene ontcology (GO) enrichment analysis, totally 30 GO terms were enriched with statistically significant raw P value and adjusted p value, as shown in Supplementary Table 3, mainly enriched in metabolic process (6 GO terms) and molecular binding functions (8 GO terms, Supplementary Figure 4). With the strict cut-off criterion (adjusted P < 0.001), the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis showed a total of four pathways enriched including pathways of Jak-STAT signalling, cell cycle and p53 signalling, as well as pathways in cancer (Supplementary Table 4).

Discussion

In the present study, we provided large number of CRC patients and extensive clinicopathological data from multiple-institutions in China, U.S. and Sweden. For the first time, the integrated analysis of multiple biomarkers and prognostic factors was performed in young CRC patients compared with elderly patients. Moreover, we utilized the bioinformatics analysis to explore the function of prognostic biomarkers for young CRC patients.

The results described in our study suggested that young CRC patients had distinct clinicopathological characteristics. In accordance with our observations, several investigations showed that CRC in young patients tended to occur predominately on distal location. A literature review of 55 articles concerning young CRC patients exhibited that the sigmoid colon and rectum were the frequent sites (54%)8. Similarly, You YN et alsupplementary data). The histopathological characteristics, inflammatory infiltration, necrosis and fibrosis were included in this study, according to our published data25.

Biomarker analysis

Immunohistochemistry was performed at our laboratory for the following biomarkers: Astrocyte elevated gene-1 (AEG-1)26, CD16327, c-erbB-228, cyclooxygenase-2 (Cox-2)29, D2-4030, FXYD-331, Ki-6732, Meningioma associated protein 3 (Mac30)33, Nuclear factor-kappaB (NFκB)34, p5320, p7335, Particularly interesting new cysteine-histidine-rich protein (PINCH)36, Peroxisome proliferator-activated receptor delta (PPARD)37, PRL17, ras38, RBM3 (unpublished data), TAZ39 and Wrap5319. The microsatellite status (microsatellite stability, MSS; microsatellite instability, MSI) was determined by PCR based assays as previous describing40. Apoptotic cells were detected by the terminal deoxynucleotidy transferase-mediated dUTP-biotin nick end-labelling (TUNEL) assay41. DNA content and S-phase fraction (SPF) were measured by flow cytometry. The details were described previously23.

Functional analysis

To further analyse the function of the significant biomarkers, STRING resource was utilized for PPIs network analysis42 and the WEB-based Gene Set Analysis Toolkit (WebGestalt) was performed for comprehensive gene functional enrichment analysis43, including GO enrichment and KEGG pathway enrichment analysis44

Statistical analysis

The relationships of age groups with clinicopathological characteristics and biomarkers were analysed by Chi-square (χ2) test. Survival curves were generated using Kaplan-Meier estimates, differences between the curves were analysed by log-rank test. The impact of each characteristic on survival was examined by the Cox’s proportional hazard regression models. The data were summarized with HR and their 95% CI. The test was two-sided and a P value of less than 0.05 was considered statistically significant. All statistical analyses were performed using R software ( http://www.R-project.org/). For details, see supplementary data.

Additional Information

How to cite this article: Wang, M.-J. et al. The prognostic factors and multiple biomarkers in young patients with colorectal cancer. Sci. Rep. 5, 10645; doi: 10.1038/srep10645 (2015).