The Identification of Specific Methylation Patterns across Different Cancers

Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2, and PRRT1) in breast cancer and eight biomarkers (ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8, and SALL1) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that MAP3K14, PTN, ACVR1 and HCK sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.     

Transcriptional landscape of DNA repair genes underpins a pan-cancer prognostic signature associated with cell cycle dysregulation and tumor hypoxia

Overactive DNA repair contributes to therapeutic resistance in cancer. However, pan-cancer comparative studies investigating the contribution of all DNA repair genes in cancer progression employing an integrated approach have remained limited. We performed a multi-cohort retrospective analysis to determine the prognostic significance of 138 DNA repair genes in 16 cancer types (n = 16,225). Cox proportional hazards analyses revealed a significant variation in the number of prognostic genes between cancers; 81 genes were prognostic in clear cell renal cell carcinoma while only two genes were prognostic in glioblastoma. We reasoned that genes that were commonly prognostic in highly correlated cancers revealed by Spearman’s correlation analysis could be harnessed as a molecular signature for risk assessment. A 10-gene signature, uniting prognostic genes that were common in highly correlated cancers, was significantly associated with overall survival in patients with clear cell renal cell (P < 0.0001), papillary renal cell (P = 0.0007), liver (P = 0.002), lung (P = 0.028), pancreas (P = 0.00013) or endometrial (P = 0.00063) cancers. Receiver operating characteristic analyses revealed that a combined model of the 10-gene signature and tumor staging outperformed either classifier when considered alone. Multivariate Cox regression models incorporating additional clinicopathological features showed that the signature was an independent predictor of overall survival. Tumor hypoxia is associated with adverse outcomes. Consistent across all six cancers, patients with high 10-gene and high hypoxia scores had significantly higher mortality rates compared to those with low 10-gene and low hypoxia scores. Functional enrichment analyses revealed that high mortality rates in patients with high 10-gene scores were attributable to an overproliferation phenotype. Death risk in these patients was further exacerbated by concurrent mutations of a cell cycle checkpoint protein, TP53. The 10-gene signature identified tumors with heightened DNA repair ability. This information has the potential to radically change prognosis through the use of adjuvant DNA repair inhibitors with chemotherapeutic drugs.     

Molecular characterization of lung cancer: A two-miRNA prognostic signature based on cancer stem-like cells related genes

Lung cancer is one of the deadliest cancers worldwide. To increase the survival rate of lung cancer, it is necessary to explore specific prognosis markers. More and more evidence finds that noncoding RNA is closely associated with the survival of lung cancer, and cancer stem cells (CSCs) also play a significant role in the progress of lung cancer. The objective of this study is to find CSLCs genes that affect the prognosis of lung cancer. The differential expression of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), messenger RNAs (mRNAs) in the Cancer Genome Atlas (TCGA) database and differential expression data from microarray of CD326⁺ and CD326⁻ A549 cell are intersected to identify stable and consistent expression genes (2 lncRNAs, 15 miRNAs, and 134 mRNAs). The intersection of lncRNAs and miRNAs is analyzed by univariate and multivariate Cox regression to obtained prognostic genes. Two miRNAs (miR-30b-5p and miR-29c-3p) are significantly correlated with the overall survival rate. Then using these two miRNAs to construct a risk score model as a prognosis signature of lung cancer. Subsequently, we analyzed the association between two miRNAs and clinical information of lung cancer patients, of which T stage, Neoplasm cancer and risk score (P < .05) can be used as independent prognostic indicators of lung cancer. Finally, target genes of 2 miRNAs and 134 mRNAs were annotated with Gene Ontology and analyzed with Kyoto Encyclopedia of Genes and Genomes pathway, and verified with the GEO database. In summary, this study illustrates the role of miRNAs in the promotion of lung cancer by CSCs, which is important to find molecular biomarkers of lung cancer.     

Disparities in survival improvement for U.S. childhood and adolescent cancer between 1995 and 2019: An analysis of population-based data

BackgroundAlthough treatment advances have increased childhood and adolescent cancer survival, whether patient subgroups have benefited equally from these improvements is unclear.MethodsData on 42,865 malignant primary cancers diagnosed between 1995 and 2019 in individuals ≤ 19 years were obtained from 12 Surveillance, Epidemiology, and End Results registries. Hazard ratios (HRs) and 95 % confidence intervals (CIs) for cancer-specific mortality by age group (0–14 and 15–19 years), sex, and race/ethnicity were estimated using flexible parametric models with a restricted cubic spline function in each of the periods: 2000–2004, 2005–2009, 2010–2014 and 2015–2019, versus 1995–1999. Interactions between diagnosis period and age group (children 0–14 and adolescents 15–19 years at diagnosis), sex, and race/ethnicity were assessed using likelihood ratio tests. Five-year cancer-specific survival rates for each diagnosis period were further predicted.ResultsCompared with the 1995–1999 cohort, the risk of dying from all cancers combined decreased in subgroups defined by age, sex and race/ethnicity with HRs ranging from 0.50 to 0.68 for the 2015–2019 comparison. HRs were more variable by cancer subtype. There were no statistically significant interactions by age group (P_interaction=0.05) or sex (P_interaction=0.71). Despite non-significant differences in cancer-specific survival improvement across different races and ethnicities (P_interaction=0.33) over the study period, minorities consistently experienced inferior survival compared with non-Hispanic Whites.ConclusionsThe substantial improvements in cancer-specific survival for childhood and adolescent cancer did not differ significantly by different age, sex, and race/ethnicity groups. However, persistent gaps in survival between minorities and non-Hispanic Whites are noteworthy.     

The Influence of the CHIEF Pathway on Colorectal Cancer-Specific Mortality

Many components of the CHIEF (Convergence of Hormones, Inflammation, and Energy Related Factors) pathway could influence survival given their involvement in cell growth, apoptosis, angiogenesis, and tumor invasion stimulation. We used ARTP (Adaptive Rank Truncation Product) to test if genes in the pathway were associated with colorectal cancer-specific mortality. Colon cancer (n = 1555) and rectal cancer (n = 754) cases were followed over five years. Age, center, stage at diagnosis, and tumor molecular phenotype were considered when calculating ARTP p values. A polygenic risk score was used to summarize the magnitude of risk associated with this pathway. The JAK/STAT/SOC was significant for colon cancer survival (P_ARTP = 0.035). Fifteen genes (DUSP2, INFGR1, IL6, IRF2, JAK2, MAP3K10, MMP1, NFkB1A, NOS2A, PIK3CA, SEPX1, SMAD3, TLR2, TYK2, and VDR) were associated with colon cancer mortality (P_ARTP <0.05); JAK2 (P_ARTP  = 0.0086), PIK3CA (P_ARTP = 0.0098), and SMAD3 (P_ARTP = 0.0059) had the strongest associations. Over 40 SNPs were significantly associated with survival within the 15 significant genes (P_ARTP<0.05). SMAD3 had the strongest association with survival (HR_GG 2.46 95% CI 1.44,4.21 P_Ttrnd = 0.0002). Seven genes (IL2RA, IL8RA, IL8RB, IRF2, RAF1, RUNX3, and SEPX1) were significantly associated with rectal cancer (P_ARTP<0.05). The HR for colorectal cancer-specific mortality among colon cancer cases in the upper at-risk alleles group was 11.81 (95% CI 7.07, 19. 74) and was 10.99 (95% CI 5.30, 22.78) for rectal cancer. These results suggest that several genes in the CHIEF pathway are important for colorectal cancer survival; the risk associated with the pathway merits validation in other studies.     

Circulating Concentrations of Vitamin B6 and Kidney Cancer Prognosis: A Prospective Case-Cohort Study

Prospective cohort studies have found that prediagnostic circulating vitamin B6 is inversely associated with both risk of kidney cancer and kidney cancer prognosis. We investigated whether circulating concentrations of vitamin B6 at kidney cancer diagnosis are associated with risk of death using a case-cohort study of 630 renal cell carcinoma (RCC) patients. Blood was collected at the time of diagnosis, and vitamin B6 concentrations were quantified using LC-MS/MS. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using Cox regression models. After adjusting for stage, age, and sex, the hazard was 3 times lower among those in the highest compared to the lowest fourth of B6 concentration (HR_4vs1 0.33, 95% CI [0.18, 0.60]). This inverse association was solely driven by death from RCC (HR_4vs1 0.22, 95% CI [0.11, 0.46]), and not death from other causes (HR_4vs1 0.89, 95% CI [0.35, 2.28], p-interaction = 0.008). These results suggest that circulating vitamin B6 could provide additional prognostic information for kidney cancer patients beyond that afforded by tumour stage.     

Detection of high CD44 expression in oral cancers using the novel monoclonal antibody,C44Mab-5

CD44 is a transmembrane glycoprotein that regulates a variety of genes related to cell-adhesion, migration, proliferation, differentiation, and survival. A large number of alternative splicing isoforms of CD44, containing various combinations of alternative exons, have been reported. CD44 standard (CD44s), which lacks variant exons, is widely expressed on the surface of most tissues and all hematopoietic cells. In contrast, CD44 variant isoforms show tissue-specific expression patterns and have been extensively studied as both prognostic markers and therapeutic targets in cancer and other diseases. In this study, we immunized mice with CHO-K1 cell lines overexpressing CD44v3-10 to obtain novel anti-CD44 mAbs. One of the clones, C₄₄Mab-5 (IgG₁, kappa), recognized both CD44s and CD44v3-10. C₄₄Mab-5 also reacted with oral cancer cells such as Ca9-22, HO-1-u-1, SAS, HSC-2, HSC-3, and HSC-4 using flow cytometry. Moreover, immunohistochemical analysis revealed that C₄₄Mab-5 detected 166/182 (91.2%) of oral cancers. These results suggest that the C₄₄Mab-5 antibody may be useful for investigating the expression and function of CD44 in various cancers.     

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry–based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.     

The additional value of TGFβ1 and IL-7 to predict the course of prostate cancer progression

Background

Given the fact that prostate cancer incidence will increase in the coming years, new prognostic biomarkers are needed with regard to the biological aggressiveness of the prostate cancer diagnosed. Since cytokines have been associated with the biology of cancer and its prognosis, we determined whether transforming growth factor beta 1 (TGFβ1), interleukin-7 (IL-7) receptor and IL-7 levels add additional prognostic information with regard to prostate cancer-specific survival.

Materials and methods

Retrospective survival analysis of forty-four prostate cancer patients, that underwent radical prostatectomy, was performed (1989–2001). Age, Gleason score and pre-treatment PSA levels were collected. IL-7, IL-7 receptor and TGFβ1 levels in prostate cancer tissue were determined by quantitative real-time RT-PCR and their additional prognostic value analyzed with regard to prostate cancer survival. Hazard ratios and their confidence intervals were estimated, and Akaike’s information criterion was calculated for model comparison.

Results

The predictive ability of a model for prostate cancer survival more than doubled when TGFβ1 and IL-7 were added to a model containing only the Gleason score and pre-treatment PSA (AIC: 18.1 and AIC: 6.5, respectively).

Conclusion

IL-7 and TGFβ1 are promising markers to indicate those at risk for poor prostate cancer survival. This additional information may be of interest with regard to the biological aggressiveness of the diagnosed prostate cancer, especially for those patients screened for prostate cancer and their considered therapy.     

Partner status and survival after cancer: A competing risks analysis

ObjectiveThe survival benefits of having a partner for all cancers combined is well recognized, however its prognostic importance for individual cancer types, including competing mortality causes, is less clear. This study was undertaken to quantify the impact of partner status on survival due to cancer-specific and competing mortality causes.MethodsData were obtained from the population-based Queensland Cancer Registry on 176,050 incident cases of ten leading cancers diagnosed in Queensland (Australia) from 1996 to 2012. Flexible parametric competing-risks models were used to estimate cause-specific hazards and cumulative probabilities of death, adjusting for age, stage (breast, colorectal and melanoma only) and stratifying by sex.ResultsBoth unpartnered males and females had higher total cumulative probability of death than their partnered counterparts for each site. For example, the survival disadvantage for unpartnered males ranged from 3% to 30% with higher mortality burden from both the primary cancer and competing mortality causes. The cause-specific age-adjusted hazard ratios were also consistent with patients without a partner having increased mortality risk although the specific effect varied by site, sex and cause of death. For all combined sites, unpartnered males had a 46%, 18% and 44% higher risk of cancer-specific, other cancer and non-cancer mortality respectively with similar patterns for females. The higher mortality risk persisted after adjustment for stage.ConclusionsIt is important to better understand the mechanisms by which having a partner is beneficial following a cancer diagnosis, so that this can inform improvements in cancer management for all people with cancer.     

Biomarkers for detection and prognosis of breast cancer identified by a functional hypermethylome screen

Breast cancer (BC) is a disease with diverse tumor heterogeneity, which challenges conventional approaches to develop biomarkers for early detection and prognosis. To identify effective biomarkers, we performed a genome-wide screen for functional methylation changes in BC, i.e., genes silenced by promoter hypermethylation, using a functionally proven gene expression approach. A subset of candidate hypermethylated genes were validated in primary BCs and tested as markers for detection and prognosis prediction of BC. We identified 33 cancer specific methylated genes and, among these, two categories of genes: (1) highly frequent methylated genes that detect early stages of BC. Within that category, we have identified the combination of NDRG2 and HOXD1 as the most sensitive (94%) and specific (90%) gene combination for detection of BC; (2) genes that show stage dependent methylation frequency pattern, which are candidates to help delineate BC prognostic signatures. For this category, we found that methylation of CDO1, CKM, CRIP1, KL and TAC1 correlated with clinical prognostic variables and was a significant prognosticator for poor overall survival in BC patients. CKM [Hazard ratio (HR) = 2.68] and TAC1 (HR = 7.73) were the strongest single markers and the combination of both (TAC1 and CKM) was associated with poor overall survival independent of age and stage in our training (HR = 1.92) and validation cohort (HR = 2.87). Our study demonstrates an efficient method to utilize functional methylation changes in BC for the development of effective biomarkers for detection and prognosis prediction of BC.     

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening

Background

In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC’s prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA).

Results

With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data.

Conclusions

Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients’ survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.

     

Grade-specific diagnostic and prognostic biomarkers in breast cancer

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.     

Effects of polymorphisms in alcohol metabolism and oxidative stress genes on survival from head and neck cancer

BackgroundHeavy alcohol consumption increases risk of developing squamous cell carcinoma of the head and neck (SCCHN). Alcohol metabolism to cytotoxic and mutagenic intermediates acetaldehyde and reactive oxygen species is critical for alcohol-drinking-associated carcinogenesis. We hypothesized that polymorphisms in alcohol metabolism-related and antioxidant genes influence SCCHN survival.MethodsInterview and genotyping data (64 polymorphisms in 12 genes) were obtained from 1227 white and African-American cases from the Carolina Head and Neck Cancer Epidemiology study, a population-based case–control study of SCCHN conducted in North Carolina from 2002 to 2006. Vital status, date and cause of death through 2009 were obtained from the National Death Index. Kaplan–Meier log-rank tests and adjusted hazard ratios were calculated to identify alleles associated with survival.ResultsMost tested SNPs were not associated with survival, with the exception of the minor alleles of rs3813865 and rs8192772 in CYP2E1. These were associated with poorer cancer-specific survival (HR_rs3813865, 95%CI = 2.00, 1.33–3.01; HR_rs8192772, 95%CI = 1.62, 1.17–2.23). Hazard ratios for 8 additional SNPs in CYP2E1, GPx2, SOD1, and SOD2, though not statistically significant, were suggestive of differences in allele hazards for all-cause and/or cancer death. No consistent associations with survival were found for SNPs in ADH1B, ADH1C, ADH4, ADH7, ALDH2, GPx2, GPx4, and CAT.ConclusionsWe identified some polymorphisms in alcohol and oxidative stress metabolism genes that influence survival in subjects with SCCHN. Previously unreported associations of SNPs in CYP2E1 warrant further investigation.     

Identification of a novel glycolysis-related gene signature that can predict the survival of patients with lung adenocarcinoma

Lung cancer is one of the most malignant cancers worldwide, and lung adenocarcinoma (LUAD) is the most common histologic subtype. Thousands of biomarkers related to the survival and prognosis of patients with this cancer type have been investigated through database mining; however, the prediction effect of a single gene biomarker is not satisfactorily specific or sensitive. Thus, the present study aimed to develop a novel gene signature of prognostic values for patients with LUAD. Using a data-mining method, we performed expression profiling of 1145 mRNAs in large cohorts with LUAD (n = 511) from The Cancer Genome Atlas database. Using the Gene Set Enrichment Analysis, we selected 198 genes related to GLYCOLYSIS, which is the most important enrichment gene set. Moreover, these genes were identified using Cox proportional regression modeling. We established a risk score staging system to predict the outcome of patients with LUAD and subsequently identified four genes (AGRN, AKR1A1, DDIT4, and HMMR) that were closely related to the prognosis of patients with LUAD. The identified genes allowed us to classify patients into the high-risk group (with poor outcome) and low-risk group (with better outcome). Compared with other clinical factors, the risk score has a better performance in predicting the outcome of patients with LUAD, particularly in the early stage of LUAD. In conclusion, we developed a four-gene signature related to glycolysis by utilizing the Cox regression model and a risk staging model for LUAD, which might prove valuable for the clinical management of patients with LUAD.     

A novel survival-based tissue microarray of pancreatic cancer validates MUC1 and mesothelin as biomarkers

Background

One–fifth of patients with seemingly ‘curable’ pancreatic ductal adenocarcinoma (PDA) experience an early recurrence and death, receiving no definable benefit from a major operation. Some patients with advanced stage tumors are deemed ‘unresectable’ by conventional staging criteria (e.g. liver metastasis), yet progress slowly. Effective biomarkers that stratify PDA based on biologic behavior are needed. To help researchers sort through the maze of biomarker data, a compendium of ∼2500 published candidate biomarkers in PDA was compiled (PLoS Med, 2009. 6(4) p. e1000046).

Methods and Findings

Building on this compendium, we constructed a survival tissue microarray (termed s-TMA) comprised of short-term (cancer-specific death <12 months, n = 58) and long-term survivors (>30 months, n = 79) who underwent resection for PDA (total, n = 137). The s-TMA functions as a biological filter to identify bona fide prognostic markers associated with survival group extremes (at least 18 months separate survival groups). Based on a stringent selection process, 13 putative PDA biomarkers were identified from the public biomarker repository. Candidates were tested against the s-TMA by immunohistochemistry to identify the best markers of tumor biology. In a multivariate model, MUC1 (odds ratio, OR = 28.95, 3+ vs. negative expression, p = 0.004) and MSLN (OR = 12.47, 3+ vs. negative expression, p = 0.01) were highly predictive of early cancer-specific death. By comparison, pathologic factors (size, lymph node metastases, resection margin status, and grade) had ORs below three, and none reached statistical significance. ROC curves were used to compare the four pathologic prognostic features (ROC area = 0.70) to three univariate molecular predictors (MUC1, MSLN, MUC2) of survival group (ROC area = 0.80, p = 0.07).

Conclusions

MUC1 and MSLN were superior to pathologic features and other putative biomarkers as predicting survival group. Molecular assays comparing cancers from short and long survivors are an effective strategy to screen biomarkers and prioritize candidate cancer genes for diagnostic and therapeutic studies.     

肝癌预后miRNA风险评分模型的鉴定和分析

肝细胞癌(hepatocellular carcinoma,HCC)是世界上高发病率和高死亡率的恶性肿瘤之一.研究目的是寻找HCC相关的mi RNA预后生物学标志物,预测HCC患者的风险程度和生存时间,为他们提供有效的预后信息.使用4种方法从TCGA中识别差异表达的mi RNAs(DEMs).并用Kaplan-Meier生存曲线、单因素和多因素Cox回归分析从DEMs中筛选肝癌预后相关的mi RNA.最终4个HCC的预后mi RNA生物学标志物(hsa-mi R-132-3p、hsa-mi R-139-5p、hsa-mi R-3677-3p、hsa-mi R-500a-3p)被筛选出来组合成一个风险评分模型.目前还没有实验证据表明组合中的hsa-mir-3677-3p与HCC相关,是本研究新发现的mi RNA.生存曲线、ROC曲线、卡方检验等多种生物信息学方法的评价结果均表明,该模型计算出的风险分值能有效预测患者的风险程度(P<0.000,风险比=2.551,95%置信区间=1.751-3.717).低风险组HCC患者1-5年生存率比高风险组高20%-30%.通过与临床数据分析发现,组合的生物学标志物较其他临床指标相比具有更好的预后效果,也可以作为独立的预后因子.最后,预测了4种mi RNA的靶基因,包括AGO2、FOXO1、ROCK2、RAP1B、CYLD等,并在细胞增殖、迁移、凋亡、免疫应答等生物学过程中富集.     

Concurrent Gene Signatures for Han Chinese Breast Cancers

The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers.We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival.Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. Prognostic comparisons with published gene expression signatures showed a better discerning ability of concurrent genes, many of which were rarely identifiable if expression data were pre-selected by phenotype correlations or variability of individual genes.We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer.