Novel prognostic genes of diffuse large B-cell lymphoma revealed by survival analysis of gene expression data

Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis

To perform bioinformatics analysis on the papillary thyroid carcinoma (PTC) gene chip dataset to explore new biological markers for PTC. The gene expression profiles of GSE3467 and GSE6004 chip data were collected by GEO2R, and the differentially expressed genes (DEGs) were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-protein interaction (PPI) relationship analysis was achieved using STRING, and the hub genes were obtained using the Cytoscape software. GEPIA was used to validate the expressions of the hub genes in the normal and tumor tissues and to conduct survival analyses. Pertinent genetic pathology results were fetched using the HPA database. Finally, the key genes were clinically verified by reverse transcription-polymerase chain reaction. 97 genes were jointly up-regulated and 107 genes were jointly down-regulated in GSE3467 and GSE6004. GO function enrichment analysis revealed that the DEGs were involved in the regulation of calcium ion transport into cytosol, integrin binding, and cell adhesion molecule binding. KEGG pathway enrichment analysis indicated that the DEGs were chiefly associated with thyroid cancer and non-small cell lung cancer. According to the PPI network, 30 key target genes were identified. Only the expressions of ANK2, TLE1, and TCF4 matched between the normal and tumor tissues, and were associated with disease prognosis. When compared with the normal thyroid tissues, the protein and mRNA expressions of ANK2, TLE1, and TCF4 were down-regulated in PTC. Significant differences exist in overall gene expression between the thyroid tissues of patients with PTC and those of healthy people. Furthermore, the differential genes ANK2, TLE1, and TCF4 are expected to be reliable molecular markers for the mechanism study and diagnosis of PTC.

High Expression of VAV Gene Family Predicts Poor Prognosis of Acute Myeloid Leukemia

Objectives: VAV family genes (VAV1, VAV2, and VAV3) are associated with prognosis in various cancers; however, they have not been evaluated in acute myeloid leukemia (AML). In this study, the prognostic value of VAV expression in AML was evaluated by a single-center study in combination with bioinformatics analyses. Methods: The expression and prognostic value of VAVs in patients with AML were investigated using various databases, including GEPIA, CCLE, EMBL-EBI, UALCAN, cBioPortal, STRING, and DAVID. Blood samples from 35 patients with AML (non-M3 subtype) and 13 benigh individuals were collected at our center. VAV expression levels were detected by real-time quantitative PCR (RT-qPCR) and western blotting. Clinical data were derived from medical records. Results: Based on data from multiple databases, the expression levels of VAV1, VAV2, and VAV3 were significantly higher in AML than in control tissues (P < 0.05). RT-qPCR and western blotting results showed that VAV expression in mRNA and protein levels were higher in patients with AML that in the control group (P < 0.05). Complete remission rates were lower and risks were higher in patients with AML with high VAV1 expression than with low VAV1 expression (P < 0.05). High levels of VAV2, VAV3, and VAV1 were related to a poor overall survival, and this relationship was significant for VAV1 (P < 0.05). High expression levels of genes correlated with VAV1, such as SIPA1, SH2D3C, and HMHA1 were also related to a poor prognosis in AML. Functional and pathways enrichment analyses indicated that the contribution of the VAV family to AML may be mediated by the NF-κB, cAMP, and other pathways. Conclusion: VAVs were highly expressed in AML. In particular, VAV1 has prognostic value and is a promising therapeutic target for AML.

Microenvironment-related prognostic genes in esophageal cancer

Background

Esophageal cancer is one of the most common malignant tumors. The role of tumor microenvironment in esophageal cancer is unclear.

Methods

The gene expression profiles and clinical data of 158 patients with esophageal cancer were extracted from The Cancer Genome Atlas database. Immune scores and stromal scores were calculated based on ESTIMATE algorithm. According to different immune/stromal scores, differentially expressed genes (DEGs) were identified. The function enrichment, protein interactions of shared DEGs and their associations with overall survival were analyzed.

Results

In regard to the association of the immune/stromal scores and disease stage, pathological type and overall survival, only the stromal scores among the different stages were significantly different (P=0.015). In the high immune and stromal score groups, 603 shared up-regulated genes were found. The related function and pathways included regulation of lymphocyte activation, cytokine binding and chemokine signaling pathway. Protein-protein interaction analysis showed that ITGAM had the most connections, followed by CXCL10 and CCR2. High expression of 11 genes, including MS4A7, TMIGD3, MS4A4A, EVI2A, MS4A6A, FCER1G, AIF1, GNGT2, LCP2, DNAJC5B and RNASE6, were found to be associated with shorter overall survival.

Conclusions

Microenvironment-associated functions and pathways were analyzed in esophageal cancer, and 11 microenvironment-associated genes were correlated to poor prognoses. Further studies on these genes may be helpful to understand the tumor microenvironment and provide new therapies for esophageal cancer.

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

BACKGROUND

Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data.

METHODS

Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set.

RESULTS

Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942).

CONCLUSION

Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma

Background

Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown.

Methods

Energy metabolism-related genes were obtained from the TARGET database. We applied the “NFM” algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model.

Results

Two subtypes associated with energy metabolism was determined using the “NFM” algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis.

Conclusions

The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS.

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Diffuse Large B-cell Lymphoma (DLBCL), a heterogeneous disease, is influenced by complex network of gene interactions. Most previous studies focused on individual genes, but ignored the importance of intergenic correlations. In current study, we aimed to explore the association between gene networks and overall survival (OS) of DLBCL patients treated with CHOP-based chemotherapy (cyclophosphamide combination with doxorubicin, vincristine and prednisone). Weighted gene co-expression network analysis was conducted to obtain insights into the molecular characteristics of DLBCL. Ten co-expression gene networks (modules) were identified in training dataset (n = 470), and their associations with patients' OS after chemotherapy were tested. The results were validated in four independent datasets (n = 802). Gene ontology (GO) biological function enrichment analysis was conducted with Metascape. Three modules (purple, brown and red), which were enriched in T-cell immune, cell-cell adhesion and extracellular matrix (ECM), respectively, were found to be related to longer OS. Higher expression of several hub genes within these three co-expression modules, for example, LCP2 (HR = 0.77, p = 5.40 × 10^-2), CD2 (HR = 0.87, p = 6.31 × 10^-2), CD3D (HR = 0.83, p = 6.94 × 10^-3), FYB (HR = 0.82, p = 1.40 × 10^-2), GZMK (HR = 0.92, p = 1.19 × 10^-1), FN1 (HR = 0.88, p = 7.06 × 10^-2), SPARC (HR = 0.82, p = 2.06 × 10^-2), were found to be associated with favourable survival. Moreover, the associations of the modules and hub genes with OS in different molecular subtypes and different chemotherapy groups were also revealed. In general, our research revealed the key gene modules and several hub genes were upregulated correlated with good survival of DLBCL patients, which might provide potential therapeutic targets for future clinical research.

Identifying autophagy gene-associated module biomarkers through construction and analysis of an autophagy-mediated ceRNA‑ceRNA interaction network in colorectal cancer

Autophagy is crucial in cellular homeostasis and has been implicated in the development of malignant tumors. However, the regulatory function of autophagy in cancer remains to be fully elucidated. In the present study, the autophagy-mediated competing endogenous RNA (ceRNA)-ceRNA interaction networks in colorectal cancer (CRC) were constructed by integrating systematically expression profiles of long non-coding RNAs and mRNAs. It was found that a large proportion of autophagy genes were inclined to target hub nodes, including a fraction of autophagy genes, by comparing with other genes within ceRNA networks, and showed preferential interaction with themselves. The present study also revealed that autophagy genes may be used as prognostic markers for cancer therapy. A risk score model based on multivariable Cox regression analysis was then used to capture novel biomarkers in connection with lncRNA for the prognosis of CRC. These biomarkers were confirmed in the test dataset and an additional independent dataset. Furthermore, the prognostic value of biomarkers is independent of conventional clinical factors. These results provide improved understanding of autophagy-mediated ceRNA regulatory mechanisms in CRC and provide novel potential molecular therapeutic targets for the diagnosis and treatment of CRC.

Identification of new progestogen-associated networks in mammalian ovulation using bioinformatics

Background

Progesterone plays an essential role in mammalian ovulation. Although much is known about this process, the gene networks involved in ovulation have yet to be established. When analyze the mechanisms of ovulation, we often need to determine key genes or pathways to investigate the reproduction features. However, traditional experimental methods have a number of limitations.

Results

Data, in this study, were acquired from GSE41836 and GSE54584 which provided different samples. They were analyzed with the GEO2R and 546 differentially expressed genes were obtained from two data sets using bioinformatics (absolute log₂ FC > 1, P < 0.05). This study identified four genes (PGR, RELN, PDE10A and PLA2G4A) by protein-protein interaction networks and pathway analysis, and their functional enrichments were associated with ovulation. Then, the top 25 statistical pathway enrichments related to hCG treatment were analyzed. Furthermore, gene network analysis identified certain interconnected genes and pathways involved in progestogenic mechanisms, including progesterone-mediated oocyte maturation, the MAPK signaling pathway, the GnRH signaling pathway and focal adhesion, etc. Moreover, we explored the four target gene pathways. q-PCR analysis following hCG and RU486 treatments confirmed the certain novel progestogenic-associated genes (GNAI1, PRKCA, CAV1, EGFR, RHOA, ZYX, VCL, GRB2 and RAP1A).

Conclusions

The results suggested four key genes, nine predicted genes and eight pathways to be involved in progestogenic networks. These networks provide important regulatory genes and signaling pathways which are involved in ovulation. This study provides a fundamental basis for subsequent functional studies to investigate the regulation of mammalian ovulation.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0577-7) contains supplementary material, which is available to authorized users.

TLE1 function and therapeutic potential in cancer

Groucho (Gro)/Transducin-like enhancer of split (TLE) family proteins act as co-repressors of many transcription factors, and are involved in key signaling pathways. TLE1 negatively regulates inflammation and has potential roles in various diseases, including cancer. Previous studies suggest TLE1 could be used as a diagnostic marker and is a possible therapeutic target in various malignancies. It is therefore important to elucidate the mechanisms underlying TLE1 function during cancer initiation and metastasis. In this review, we highlight the functions of TLE1 in cancer and explore targeted approaches for cancer diagnosis and treatment. In particular, we discuss the TLE1 function in pancreatic cancer.