Predicting gene function using similarity learning

Expression of NMU, PPBP and GNG4 in colon cancer and their influences on prognosis

BACKGROUND

This study aims to identify the core genes that influence the prognosis of colon cancer (CC) and analyze their relationships with clinical characteristics.

METHODS

The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified. The top ten core genes were selected by bioinformatics tools and screened through the Oncomine database. The expression of core genes in CC tissues and cells was validated by immunohistochemistry, immunoblotting and quantitative real-time polymerase chain reaction. Spearman correlation was used to analyze the relationship between different parameters. Overall survival was assessed by the Kaplan-Meier method. The area under the curve (AUC) and the receiver operating curve (ROC) were applied to assess the accuracy of genes for predicting prognosis.

RESULTS

There were 1,665 DEGs that were identified from TCGA database. Bioinformatics analysis found that GNGT1, NMU, PPBP, AGT, and GNG4 were differentially expressed in CC tissue. Overexpression of NMU, PPBP, AGT, and GNG4 in CC was associated with shortened survival time (P<0.05). In the validation studies, the high expression levels of NMU, PPBP and GNG4 in CC cells and tissues were confirmed compared to the control groups (P<0.05) and were adverse prognostic biomarkers (P<0.01). The combination prognostic model of the three core genes predicted the 1-, 3-, and 5-year survival of CC with AUCs of 0.868, 0.635 and 0.770, respectively.

CONCLUSIONS

High levels of NMU, PPBP, and GNG4 were associated with poor prognosis in CC. The combination prognostic model of these three genes could be a new option.

Identification of genes associated with cancer progression and prognosis in lung adenocarcinoma: Analyses based on microarray from Oncomine and The Cancer Genome Atlas databases

Background

Lung adenocarcinoma (LUAD) accounts for approximately 40% of all lung cancer patients. There is an urgent need to understand the mechanisms of cancer progression in LUAD and to identify useful biomarkers to predict prognosis.

Methods

In this study, Oncomine database was used to identify potential genes contributed to cancer progression. Bioinformatics analysis including pathway enrichment and text mining was used to explain the potential roles of identified genes in LUAD. The Cancer Genome Atlas database was used to analyze the association of gene expression with survival result.

Results

Our results indicated that 80 genes were significantly dysregulated in LUAD according to four microarrays covering 356 cases of LUAD and 164 cases of normal lung tissues. Twenty genes were consistently and stably dysregulated by more than twofold. Ten of 20 genes had a relationship with overall survival or disease‐free survival in a cohort of 516 LUAD patients, and 19 genes were associated with tumor stage, gender, age, lymph node, or smoking. Low expression of AGER and high expression of CCNB1 were specifically associated with poor survival.

Conclusion

Our findings implicate AGER and CCNB1 might be potential biomarkers for diagnosis and prognosis targets for LUAD.

Downregulation of transient receptor potential cation channel, subfamily C, member 1 contributes to drug resistance and high histological grade in ovarian cancer

Transient receptor potential cation channel, subfamily C, member 1 (TRPC1) participates in many physiological functions but has also been implicated in cancer development. However, little is known about the role of TRPC1 in ovarian cancer (OC), including the drug resistance of these tumors. In the present study, a significant and consistent downregulation of TRPC1 in drug-resistant OC tissues/cells was determined using real-time quantitative polymerase chain reaction assays and the microarrays deposited in Oncomine and Gene Expression Omnibus (GEO) profiles. Protein/gene-protein/gene and protein-chemical interactions indicated that TRPC1 interacts with 14 proteins/genes and 6 chemicals, all of which are involved in the regulation of drug resistance in OC. Biological process annotation of TRPC1, OC, and drug resistance indicated a role for TRPC1 in drug-resistance-related functions in OC, mainly via the cell cycle, gene expression and cell growth and cell death. Analysis of mRNA-microRNA interactions showed that 8 out of 11 major pathways enriched from 38 predominant microRNAs targeting TRPC1 were involved in the regulation of drug resistance in OC, and 8 out of these top 10 microRNAs were implicated in the drug resistance in ovarian and other cancers. In a clinical analysis using data obtained from The Cancer Genome Atlas project (TCGA) cohort on 341 OC patients, TRPC1 expression was found to differ significantly between grade 2 and grade 3 tumors, with low-level expression correlating with higher tumor grade. This is the first report to show a potential association between the downregulation of TRPC1 and both drug resistance and high histological tumor grade in OC. Our results provide the basis for further investigations of the drug-resistance-related functions of TRPC1 in OC and other forms of cancer.

Discovery of microarray-identified genes associated with ovarian cancer progression

Ovarian cancer is the most lethal cancer of female reproductive system. There is a consistent and urgent need to better understand its mechanism. In this study, we retrieved 186 genes that were dysregulated by at least 4-fold in 594 ovarian serous cystadenocarcinomas in comparison with eight normal ovaries, according to The Cancer Genome Atlas Ovarian Statistics data deposited in Oncomine database. DAVID analysis of these genes enriched two biological processes indicating that the cell cycle and microtubules might play critical roles in ovarian cancer progression. Among these 186 genes, 46 were dysregulated by at least 10-fold and their expression was further confirmed by the Bonome Ovarian Statistics data deposited in Oncomine, which covered 185 cases of ovarian carcinomas and 10 cases of normal ovarian surface epithelium. Six genes, including aldehyde dehydrogenase 1 family, member A2 (ALDH1A2), alcohol dehydrogenase 1B (class I), β polypeptide (ADH1B), NEL-like 2 (chicken) (NELL2), hemoglobin, β (HBB), ATP-binding cassette, sub-family A (ABC1), member 8 (ABCA8) and hemoglobin, α1 (HBA1) were identified to be downregulated by at least 10-fold in 779 ovarian cancers compared with 18 normal controls. Using mRNA expression profiles retrieved from microarrays deposited in the Gene Expression Omnibus Profiles database, RT-qPCR measurement and bioinformatics analysis, we further indicated that high expression of HBB might predict a poorer 5-year survival, high expression of ALDH1A2 and ABCA8 might predict a poor outcome; while ALDH1A2, ADH1B, HBB and ABCA8, in particular the former two genes, might be associated with drug resistance, and ALDH1A2 and NELL2 might contribute to invasiveness and metastasis in ovarian cancer. This study thus contributes to our understanding of the mechanism of ovarian cancer progression and development, and the six identified genes may be potential therapeutic targets and biomarkers for diagnosis and prognosis.

Downregulation of HNF1 homeobox B is associated with drug resistance in ovarian cancer

The expression of HNF1 homeobox B (HNF1B) is associated with cancer risk in several tumors, including ovarian cancer, and its decreased expression play roles in cancer development. However, the study of HNF1B and cancer is limited, and its association with drug resistance in cancer has never been reported. On the basis of array data retrieved from Oncomine and Gene Expression Omnibus (GEO) online database, we found that the mRNA expression of HNF1B in 586 ovarian serous cystadenocarcinomas and in platinum-resistant A2780 epithelial ovarian cancer cells was significantly decreased, indicating a potential role of HNF1B in drug resistance in ovarian cancer. Based on this finding, comprehensive bioinformatics analyses, including protein/gene interaction, protein-small molecule/chemical interaction, biological process annotation, gene co-occurrence and pathway enrichment analysis and microRNA-mRNA interaction, were performed to illustrate the association of HNF1B with drug resistance in ovarian cancer. We found that among the proteins/genes, small molecules/chemicals and microRNAs which directly interacted with HNF1B, the majority was associated with drug resistance in cancer, particularly in ovarian cancer. Biological process annotation revealed that HNF1B closely related to 24 biological processes which were all notably associated with ovarian cancer and drug resistance. These results indicated that the downregulation of HNF1B may contribute to drug resistance in ovarian cancer, via its direct interactions with these drug resistance-related proteins/genes, small molecules/chemicals and microRNAs, and via its regulations on the drug resistance-related biological processes. Pathway enrichment analysis of 36 genes which co-occurred with HNF1B, ovarian cancer and drug resistance indicated that the HNF1B may perform its drug resistance-related functions through 4 pathways including ErbB signaling, focal adhesion, apoptosis and p53 signaling. Collectively, in this study, we illustrated for the first time that HNF1B may contribute to drug resistance in ovarian cancer, potentially through the 4 pathways. The present study may pave the way for further investigation of the drug resistance-related functions of HNF1B in ovarian cancer.

Downregulation of NEK11 is associated with drug resistance in ovarian cancer

NEKs [NIMA (never in mitosis gene A)-related expressed kinase] are involved in ovarian cancer development and progression, while their association with drug resistance is limited, especially NEK11, and its relationship with drug resistance has never been reported. In this study, on the basis of comprehensive bioinformatic analyses, including mRNA expression according to microarray data, protein/gene interaction, protein-small molecule interaction, annotation of biological process and microRNA-mRNA interaction analysis, we revealed that the NEK11 mRNA was significantly downregulated in 586 cases of ovarian serous cystadenocarcinomas and cisplatin-resistant A2780 ovarian cancer cells, and interacted with 22 proteins and 4 small molecules which all were contributed to drug resistance in ovarian cancer. Furthermore, seven cell cycle-related biological processes were annotated with NEK11, drug resistance and ovarian cancer, suggesting that NEK11 potentially was involved in the drug resistance in ovarian cancer via its regulatory roles in the cell cycle. In addition, among the eight microRNAs predicted to be most strongly targeting NEK11, the majority were involved in drug resistance in ovarian and other cancers. All those results provide a very strong possibility that the notable downregulation of NEK11 in cisplatin-resistant ovarian cancer cells was involved in drug resistance, via its interactions with drug resistance-related genes, proteins, small molecules, microRNAs and biological processes, particularly the cell cycle-related processes. To our knowledge, this is the first report of the association of NEK11 with drug resistance in cancer, and it would pave the way for further investigation of the drug resistance-related functions of this gene.