Bioinformatic analysis of prognostic value of ZW10 interacting protein in lung cancer

Enhancing risk stratification models in localized prostate cancer by novel validated tissue biomarkers

BACKGROUND

Localized prostate cancer (PCa) is a largely heterogeneous disease regarding its clinical behavior. Current risk stratification relies on clinicopathological parameters and distinguishing between indolent and aggressive cases remains challenging. To improve risk stratification, we aimed to identify new prognostic markers for PCa.

METHODS

We performed an in silico analysis on publicly available PCa transcriptome datasets. The top 20 prognostic genes were assessed in PCa tissue samples of our institutional cohort (n = 92) using the NanoString nCounter technology. The three most promising candidates were further assessed by immunohistochemistry (IHC) in an institutional (n = 121) and an independent validation cohort from the EMPACT consortium (n = 199). Cancer-specific survival (CSS) and progression-free survival (PFS) were used as endpoints.

RESULTS

Our in silico analysis identified 113 prognostic genes. The prognostic values of seven of the top 20 genes were confirmed in our institutional radical prostatectomy (RPE) cohort. Low CENPO, P2RX5, ABCC5 as well as high ASF1B, NCAPH, UBE2C, and ZWINT gene expressions were associated with shorter CSS. IHC analysis confirmed the significant associations between NCAPH and UBE2C staining and worse CSS. In the external validation cohort, higher NCAPH and ZWINT protein expressions were associated with shorter PFS. The combination of the newly identified tissue protein markers improved standard risk stratification models, such as D'Amico, CAPRA, and Cambridge prognostic groups.

CONCLUSIONS

We identified and validated high tissue levels of NCAPH, UBE2C, and ZWINT as novel prognostic risk factors in clinically localized PCa patients. The use of these markers can improve routinely used risk estimation models.

siRNA-based therapy for gastric adenocarcinoma: what's next step?

Gastric cancer continues to have a high death rate despite advancements in their diagnosis and treatment. Novel treatment techniques are thus desperately needed. This is where double-stranded RNA molecules known as small interfering RNA (siRNA), which may selectively target the mRNA of disease-causing genes, may find use in medicine. For siRNAs to function properly in the human body, they must be shielded from deterioration. Furthermore, in order to maintain organ function, they must only target the tumor and spare normal tissue. siRNAs have been designed using clever delivery mechanisms including polymers and lipids to achieve these objectives. Although siRNA protection is not hard to acquire, it is still challenging to target cancer cells with them. Here, we first discuss the basic characteristics of gastric cancer before describing the properties of siRNA and typical delivery methods created specifically for gastric tumors. Lastly, we provide a succinct overview of research using siRNAs to treat gastric tumors.

Comprehensive analysis of alternative splicing across multiple transcriptomic cohorts reveals prognostic signatures in prostate cancer

BACKGROUND

Alternative splicing (AS) plays a crucial role in transcriptomic diversity and is a hallmark of cancer that profoundly influences the development and progression of prostate cancer (PCa), a prevalent and potentially life-limiting cancer among men. Accumulating evidence has highlighted the association between AS dysregulation and the onset and progression of PCa. However, a comprehensive and integrative analysis of AS profiles at the event level, utilising data from multiple high-throughput cohorts and evaluating the prognosis of PCa progression, remains lacking and calls for thorough exploration.

RESULTS

We identified a differentially expressed retained intron event in ZWINT across three distinct cohorts, encompassing an original array-based dataset profiled by us previously and two RNA sequencing (RNA-seq) datasets. Subsequent in-depth analyses of these RNA-seq datasets revealed  141 altered events, of which 21 demonstrated a significant association with patients' biochemical recurrence-free survival (BCRFS). We formulated an AS event-based prognostic signature, capturing six pivotal events in genes CYP4F12, NFATC4, PIGO, CYP3A5, ALS2CL, and FXYD3. This signature effectively differentiated  high-risk patients diagnosed with PCa, who experienced shorter BCRFS, from their low-risk counterparts. Notably, the signature's predictive power surpassed traditional clinicopathological markers in forecasting 5-year BCRFS, demonstrating robust performance in both internal and external validation sets. Lastly, we constructed a novel nomogram that integrates patients' Gleason scores with pathological tumour stages, demonstrating improved prognostication of BCRFS.

CONCLUSIONS

Prediction of clinical progression remains elusive in PCa. This research uncovers novel splicing events associated with BCRFS, augmenting existing prognostic tools, thus potentially refining clinical decision-making.

Exosomal miR-105-5p derived from bladder cancer stem cells targets for GPR12 to promote the malignancy of bladder cancer

Bladder cancer stem cells (BCSCs) are considered as the root cause of BC initiation and recurrence, and exosomes derived from BCSCs (CSCs-exo) are the vital tool for establishing a stable tumor microenvironment. miR-105-5p has been revealed to promote tumor growth in a variety of cancers, but the effects on BC are still not included.Characteristics of CSCs-exo were examined by transmission electron microscope and nanoparticle tracking analysis. PKH67 dye was used to observe the cellular uptake of exosomes. Cell viability, migration and invasion were detected by CCK-8, wound healing and transwell invasion assays, respectively. The interaction between miR-105-5p and GPR12 was verified by luciferase activity assay. Xenografts were induced in the nude mice, and H&E staining method was applied to analyze the histological changes of xenografts. CSCs-exo efficiently promoted BC cell viability, migration and invasion. miR-105-5p was highly expressed in CSCs and CSCs-exo treatment significantly upregulated the expression of miR-105-5p in BC cells.GPR12 was subsequently verified to be the target gene of miR-105-5p, and overexpression of GPR12 abrogated the effects of miR-105-5p on BC cell growth and metastasis. Reversely, the anti-tumor function of miR-105-5p antagomir was observed in the xenograft mice.CSCs aggravated the malignancy of BC partly through transmitting exosomal miR-105-5p to BC cells to inhibit the expression of GPR12, which developed a novel aspect for CSC-targeted therapies.

Integrative transcriptional characterization of cell cycle checkpoint genes promotes clinical management and precision medicine in bladder carcinoma

Background

The aberrant regulation of cell cycle is significantly correlated with cancer carcinogenesis and progression, in which cell cycle checkpoints control phase transitions, cell cycle entry, progression, and exit. However, the integrative role of cell cycle checkpoint-related genes (CRGs) in bladder carcinoma (BC) remains unknown.

Methods

The transcriptomic data and clinical features of BC patients were downloaded from The Cancer Genome Atlas (TCGA), used to identify CRGs correlated with overall survival (OS) by univariate Cox regression analysis. Then, the multivariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses further developed a prognostic CRG signature, which was validated in three external datasets retrieved from Gene Expression Omnibus (GEO). The receiver operating characteristic curve (ROC) analysis was conducted for evaluating the performance of the CRG signature in prognosis prediction. RNA sequencing (RNA-Seq) was performed to explore the expression difference in the identified CRGs between tumor and normal tissue samples from 11 BC patients in the local cohort. Ultimately, genomic profiles and tumor microenvironment (TME), and the Genomics of Drug Sensitivity in Cancer (GDSC) were investigated to guide precision treatment for BC patients with different CRG features.

Results

The novel constructed 23-CRG prognostic signature could stratify BC patients into high-risk and low-risk groups with significantly different outcomes (median OS: 13.64 vs. 104.65 months). Notably, 19 CRGs were the first to be identified as being associated with BC progression. In three additional validation datasets (GSE13507, GSE31684, and GSE32548), higher CRG scores all indicated inferior survival, demonstrating the robust ability of the CRG signature in prognosis prediction. Moreover, the CRG signature as an independent prognostic factor had a robust and stable risk stratification for BC patients with different histological or clinical features. Then, a CRG signature-based nomogram with a better performance in prognostic prediction [concordance index (C-index): 0.76] was established. Functional enrichment analysis revealed that collagen-containing extracellular matrix (ECM), and ECM-related and MAPK signaling pathways were significantly associated with the signature. Further analysis showed that low-risk patients were characterized by particularly distinctive prevalence of FGFR3 (17.03% vs. 6.67%, p < 0.01) and POLE alterations (7.97% vs. 2.50%, p < 0.05), and enrichment of immune infiltrated cells (including CD8+ T cells, CD4+ naïve T cells, follicular helper T cells, Tregs, and myeloid dendritic cells). RNA-seq data in our local cohort supported the findings in the differentially expressed genes (DEGs) between tumor and normal tissue samples, and the difference in TME between high-risk and low-risk groups. Additionally, CRG signature score plus FGFR3 status divided BC patients into four molecular subtypes, with distinct prognosis, TME, and transcriptomic profiling of immune checkpoint genes. Of note, CRG signature score plus FGFR3 status could successfully distinguish BC patients who have a higher possibility of response to immunotherapy or chemotherapy drugs.

Conclusions

The CRG signature is a potent prognostic model for BC patients, and in combination with FGFR3 alterations, it had more practical capacity in the prediction of chemotherapy and immunotherapy response, helping guide clinical decision-making.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Zw10 negatively regulates the MyD88-mediated NF-κB signaling through autophagy in teleost fish

MyD88 is a typical street protein of the TLRs signaling pathway and is a central player in innate immune signaling, which can regulate the NF-κB signaling pathway and promote downstream inflammatory factors. However, studies on the molecular mechanisms of the MyD88-mediated NF-κB signaling pathway in teleosts have been poorly reported. In this study, we report that Zw10 targets MyD88 to inhibit NF-κB activation. Zw10 inhibits cell proliferation and MyD88-mediated innate immunity in fish. Zw10 interacts with MyD88, and its Δ2 domain is very critical for MyD88 degradation. In addition, we found that Zw10 degrade MyD88 by autophagy, thereby negatively regulating the MyD88-mediated NF-κB signaling pathway. This study not only enriches the research on the innate immunity of teleost fish, but also provides insights for the regulating mechanism for mammals.

Residential surrounding greenness and DNA methylation: An epigenome-wide association study

BACKGROUND

DNA methylation is a potential biological mechanism through which residential greenness affects health, but little is known about its association with greenness and whether the association could be modified by genetic background. We aimed to evaluate the association between surrounding greenness and genome-wide DNA methylation and potential gene-greenness interaction effects on DNA methylation.

METHODS

We measured blood-derived DNA methylation using the HumanMethylation450 BeadChip array (Illumina) for 479 Australian women, including 66 monozygotic, 66 dizygotic twin pairs, and 215 sisters of these twins. Surrounding greenness was represented by Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) within 300, 500, 1000 or 2000 m surrounding participants' home addresses. For each cytosine-guanine dinucleotide (CpG), the associations between its methylation level and NDVI or EVI were evaluated by generalized estimating equations, after adjusting for age, education, marital status, area-level socioeconomic status, smoking behavior, cell-type proportions, and familial clustering. We used comb-p and DMRcate to identify significant differentially methylated regions (DMRs). For each significant CpG, we evaluated the interaction effects of greenness and single-nucleotide polymorphisms (SNPs) within ±1 Mb window on its methylation level.

RESULTS

We found associations between surrounding greenness and blood DNA methylation for one CpG (cg04720477, mapped to the promoter region of CNP gene) with false discovery rate [FDR] < 0.05, and for another 9 CpGs with 0.05 ≤ FDR < 0.10. For two of these CpGs, we found 33 SNPs significantly (FDR < 0.05) modified the greenness-methylation association. There were 35 significant DMRs related to surrounding greenness that were identified by both comb-p (Sidak p-value < 0.01) and DMRcate (FDR < 0.01). Those CpGs and DMRs were mapped to genes related to many human diseases, such as mental health disorders and neoplasms as well as nutritional and metabolic diseases.

CONCLUSIONS

Surrounding greenness was associated with blood DNA methylation of many loci across human genome, and this association could be modified by genetic variations.

Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis

Breast cancer (BC) is a malignancy with high incidence among women in the world. This study aims to screen key genes and potential prognostic biomarkers for BC using bioinformatics analysis. Total 58 normal tissues and 203 cancer tissues were collected from three Gene Expression Omnibus (GEO) gene expression profiles, and then the differential expressed genes (DEGs) were identified. Subsequently, the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway were analyzed to investigate the biological function of DEGs. Additionally, hub genes were screened by constructing a protein–protein interaction (PPI) network. Then, we explored the prognostic value and molecular mechanism of these hub genes using Kaplan–Meier (KM) curve and Gene Set Enrichment Analysis (GSEA). As a result, 42 up-regulated and 82 down-regulated DEGs were screened out from GEO datasets. The DEGs were mainly related to cell cycles and cell proliferation by GO and KEGG pathway analysis. Furthermore, 12 hub genes (FN1, AURKA, CCNB1, BUB1B, PRC1, TPX2, NUSAP1, TOP2A, KIF20A, KIF2C, RRM2, ASPM) with a high degree were identified initially, among which, 11 hub genes were significantly correlated with the prognosis of BC patients based on the Kaplan–Meier-plotter. GSEA reviewed that these hub genes correlated with KEGG_CELL_CYCLE and HALLMARK_P53_PATHWAY. In conclusion, this study identified 11 key genes as BC potential prognosis biomarkers on the basis of integrated bioinformatics analysis. This finding will improve our knowledge of the BC progress and mechanisms.

KIFC1 regulates ZWINT to promote tumor progression and spheroid formation in colorectal cancer

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. Kinesin Family Member C1 (KIFC1) has been proposed as a promising therapeutic target due to its pivotal role in centrosome clustering to mediate cancer cell progression. This study aimed to analyze the expression and biological function of KIFC1 in CRC. Immunohistochemically, 67 (52%) of 129 CRC cases were positive for KIFC1 and statistically associated with poorer overall survival. KIFC1 small interfering RNA (siRNA)-transfected cells demonstrated lower cell proliferation as compared to the negative control cells. A specific KIFC1 inhibitor, kolavenic acid analog (KAA) drastically inhibited CRC cell proliferation. Microarray analysis revealed that KAA-treated CRC cells presented reduced ZW10 interacting kinetochore protein (ZWINT) expression as compared to control cells. Immunohistochemical analysis demonstrated that 61 (47%) of 129 CRC cases were positive for ZWINT and ZWINT expression was significantly correlated with KIFC1 expression. ZWINT-positive cases exhibited significantly worse overall survival. KIFC1 siRNA-transfected cells showed reduced ZWINT expression while ZWINT siRNA-transfected cells decreased cell proliferation. Both KIFC1 and ZWINT knockdown cells attenuated spheroid formation ability. This study provides new insights into KIFC1 regulating ZWINT in CRC progression and its potential as a therapeutic target.

Similarity and difference of pathogenesis among lung cancer subtypes suggested by expression profile data

Lung cancer is difficult to diagnose, has a high mortality rate and a high recurrence rate. By grouping and analyzing the gene expression in lung cancer samples, we selected the differentially expressed genes (DEGs) in total lung cancers or each subgroup, and then searched for the similarities and differences among these. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed, in addition to predictable cell proliferation or immune-related pathways, 'hemostasis', 'coagulation' and 'viral myocarditis' were also enriched in common DEGs, while specific functions or pathways were enriched in different subgroups. This may have implications for the treatment of total lung cancer or different subtypes. Through bioinformatics analysis, hub genes were obtained from total lung cancer and each subgroup respectively. Survival analysis of common hub genes led us to find that ZWINT, A2M, POLR2H and KIF11 are associated with unclassified lung cancer survival. For the construction of miRNA regulatory network, miR-16-5p was related to all of these four genes, and its expression is significantly different between lung cancers and normal samples. Combined with the hub genes of each subtype, it may have the ability of early screening and typing.

[BUB1 gene is highly expressed in gastric cancer:analysis based on Oncomine database and bioinformatics]

OBJECTIVE

To investigate the expression of BUB1 gene in gastric cancer.

METHODS

Oncomine, GEPIA, BioGPS and Kaplan-Meier Plotter databases were used to analyze the difference of BUB1 gene expression between gastric cancer tissue and normal gastric tissue. The association of BUB1 expression level with the prognosis of gastric cancer patients was also analyzed. The Cancer Cell Line Encyclopedia (CCLE) was explored to analyze the expression of BUB1 in T cells and B cells in gastric cancer patients, and the String database was used to generate the network map of BUB1-related proteins and functional annotation of gene ontology (GO). The related pathways of KEGG were analyzed. Tumor immune assessment resource (TIMER) database was used to analyze the expression of BUB1 in immune infiltration and its effect on prognosis of gastric cancer patients. To further verify the results of gene chip analysis in Oncomine database, we collected 30 pairs of surgical specimens of gastric adenocarcinoma and adjacent tissues from patients admitted to the First Affiliated Hospital of Chengdu Medical College from March, 2018 to July, 2019. The results of BUB1 gene expression in Oncomine database were verified by PCR and immunohistochemistry.

RESULTS

Oncomine, GEPIA and BioGPS analyses showed that BUB1 was highly expressed in gastric cancer compared with normal gastric tissue. Kaplan-Meier survival analysis showed that the progression-free survival time (HR=0.52, 95% CI:0.41-0.67, P &lt; 0.05) and the overall survival time (HR=0.67, 95% CI:0.55-0.82, P &lt; 0.05) were prolonged in gastric cancer patients with a high expression of BUB1. Through String data collection, BUB1-related proteins were mainly enriched in 13 cellular components, 4 molecular functions and 12 biological processes, involving 4 signal pathways. TIMER database analysis showed that CD4⁺ T cells and macrophages with high expressions of BUB1 mRNA in the immune microenvironment were associated with a favorable 5-year survival outcome of patients with gastric cancer. In the surgical specimens, real-time quantitative PCR showed that the expression level of BUB1 mRNA was significantly higher in gastric cancer tissues than in the adjacent gastric mucosa tissues, and immunohistochemical results demonstrated positive BUB1 staining in the gastric cancer tissues.

CONCLUSIONS

BUB1 gene is highly expressed in gastric cancer. BUB1 may reduce tumor immunosuppression and helps to evaluate the prognosis of patients with gastric cancer.

Clinical Implications of Chromosomal Instability (CIN) and Kinetochore Abnormalities in Breast Cancers

Genetic instability is a defining property of cancer cells and is the basis of various lesions including point mutations, copy number alterations and translocations. Chromosomal instability (CIN) is part of the genetic instability of cancer and consists of copy number alterations in whole or parts of cancer cell chromosomes. CIN is observed in differing degrees in most cancers. In breast cancer, CIN is commonly part of the genomic landscape of the disease and has a higher incidence in aggressive sub-types. Tumor suppressors that are commonly mutated or disabled in cancer, such as p53 and pRB, play roles in protection against CIN, and as a result, their dysfunction contributes to the establishment or tolerance of CIN. Several structural and regulatory proteins of the centromeres and kinetochore, the complex structure that is responsible for the correct distribution of genetic material in the daughter cells during mitosis, are direct or, mostly, indirect transcription targets of p53 and pRB. Thus, despite the absence of structural defects in genes encoding for centromere and kinetochore components, dysfunction of these tumor suppressors may have profound implications for the correct function of the mitotic apparatus contributing to CIN. CIN and its prognostic and therapeutic implications in breast cancer are discussed in this article.

ZW10 Binding Factor (ZWINT), a Direct Target of Mir-204, Predicts Poor Survival and Promotes Proliferation in Breast Cancer

BACKGROUND ZW10 binding factor (ZWINT) has been reported to be upregulated in various human cancers and predict worse survival. However, the expression profile, clinical significance, and biological role of ZWINT remains unclear in breast cancer. MATERIAL AND METHODS In this study, we investigated messenger RNA (mRNA) and protein expression levels of ZWINT in breast cancer tissues, and the prognostic value of ZWINT protein expression was validated in a cohort of breast cancer patients using immunohistochemistry analysis. Then, different bioinformatic analyses were combined to explore the potential cancer-related hallmark underlying ZWINT in breast cancer, and a series of experiments in vitro were performed to reveal the oncogenic role of ZWINT in breast cancer. RESULTS Significant upregulation of ZWINT was observed in breast cancer tissues compared to normal and para-tumor tissues and upregulation of ZWINT predicts poor prognosis in breast cancer patients. Additionally, ZWINT could promote breast cancer proliferation via cell cycle regulation, especially by influencing the expression of some critical cell cycle regulators involved in G1 phase and G1/S transition. Finally, miR-204 was identified as a tumor suppressor microRNA which directly targets a specific site in 3'-UTR of ZWINT. CONCLUSIONS Overall, our results indicated that miR-204/ZWINT/cell cycle process might play an important role in breast cancer progression.

Common and distinct features of potentially predictive biomarkers in small cell lung carcinoma and large cell neuroendocrine carcinoma of the lung by systematic and integrated analysis

BACKGROUND

Large-cell neuroendocrine carcinoma of the lung (LCNEC) and small-cell lung carcinoma (SCLC) are neuroendocrine neoplasms. However, the underlying mechanisms of common and distinct genetic characteristics between LCNEC and SCLC are currently unclear. Herein, protein expression profiles and possible interactions with miRNAs were provided by integrated bioinformatics analysis, in order to explore core genes associated with tumorigenesis and prognosis in SCLC and LCNEC.

METHODS

GSE1037 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in LCNEC and SCLC, as compared with normal lung tissues, were selected using the GEO2R online analyzer and Venn diagram software. Gene ontology (GO) analysis was performed using Database for Annotation, Visualization and Integrated Discovery. The biological pathway analysis was performed using the FunRich database. Subsequently, a protein-protein interaction (PPI) network of DEGs was generated using Search Tool for the Retrieval of Interacting Genes and displayed via Cytoscape software. The PPI network was analyzed by the Molecular Complex Detection app from Cytoscape, and 16 upregulated hub genes were selected. The Oncomine database was used to detect expression patterns of hub genes for validation. Furthermore, the biological pathways of these 16 hub genes were re-analyzed, and potential interactions between these genes and miRNAs were explored via FunRich.

RESULTS

A total of 384 DEGs were identified. A Venn diagram determined 88 common DEGs. The PPI network was constructed with 48 nodes and 221 protein pairs. Among them, 16 hub genes were extracted, 14 of which were upregulated in SCLC samples, as compared with normal lung specimens, and 10 were correlated with the cell cycle pathway. Furthermore, 57 target miRNAs for 8 hub genes were identified, among which 31 miRNAs were correlated with the progression of carcinoma, drug-resistance, radio-sensitivity, or autophagy in lung cancer.

CONCLUSION

This study provided effective biomarkers and novel therapeutic targets for diagnosis and prognosis of SCLC and LCNEC.

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.

Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis

The pathogenesis of small cell lung cancer (SCLC), a highly metastatic malignant tumor, remains unclear. In the present study, important genes and pathways that are involved in the pathogenesis of SCLC were identified. The following four datasets were downloaded from the Gene Expression Omnibus: GSE60052, GSE43346, GSE15240 and GSE6044. The differentially expressed genes (DEGs) between the SCLC samples and the normal samples were analyzed using R software. The limma package was used for every dataset. The RobustRankAggreg package was used to integrate the DEGs from the four datasets. Functional and pathway enrichment analyses were conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases with FunRich software and R software, respectively. In addition, the protein-protein interaction (PPI) network of the DEGs was constructed using the STRING database and Cytoscape software. Hub genes and significant modules were identified using Molecular Complex Detection in Cytoscape software. Finally, the expression values of hub genes were determined using the Oncomine online database. In total, 412 DEGs were identified following the integration of the four datasets, with 146 upregulated genes and 266 downregulated genes. The upregulated DEGs were primarily enriched in the cell cycle, cell division and microtubule binding. The downregulated DEGs were primarily enriched in the complement and coagulation cascades, the cytokine-mediated signaling pathway and protein binding. Eight hub genes and 1 significant module correlated to the cell cycle pathway were identified based on a subset of the PPI network. Finally, five hub genes were identified as highly expressed in SCLC tissue compared with normal tissue. The cell cycle pathway may be the pathway most closely associated with the pathogenesis of SCLC. NDC80, BUB1B, PLK1, CDC20 and MAD2L1 should be the focus of follow-up studies regarding the diagnosis and treatment of SCLC.

The prognostic value of matrix metalloproteinase-7 and matrix metalloproteinase-15 in acute myeloid leukemia

Matrix metalloproteinases (MMPs), a family of zinc-dependent endopeptidases, are involved in a variety of physiological and pathological processes. We analyzed 11 data sets from Gene Expression Omnibus Database and found that MMP7 and MMP15 were highly expressed in multiple carcinomas. GSE13204 showed that MMP7 and MMP15 were overexpressed in acute myeloid leukemia (AML) patients. The Cancer Genome Atlas data set exhibited that high expression of MMP7 or MMP15 in bone marrow (BM) of AML patients predicted poor overall survival. The χ ² test results indicated that high expression level of MMP7 and MMP15 were correlated with high-risk stratification and high BM blast cell percentage in AML patients. To confirm these findings, we performed reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and found that MMP7 and MMP15 were highly expressed in three AML cell lines. Further study showed that MMP7 and MMP15 were highly expressed both in BM and peripheral blood in collected AML samples compared with healthy individuals. Additionally, long noncoding RNA (lncRNA) microarray of BM samples of AML patients revealed that multiple lncRNAs were correlated with MMP7 and MMP15, suggesting that lncRNAs might be involved in the pathogenesis of AML via modulating MMPs. In conclusion, our study uncovers the potential roles of MMP7 and MMP15 in the prognosis of AML.

Gene set enrichment analysis and meta-analysis to identify six key genes regulating and controlling the prognosis of esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) is a common malignancy with high mortality. Because of the lack of clarity in the relevant genes and mechanisms involved, and the current difficulty for oncotherapy in providing therapeutic solutions, there is an urgent need to study this matter. While gene probe studies have been used to select the most virulent genes and pathways, paucity of case controls during gene screening and lack of conclusive results to expound the etiology and pathogenesis of the disease, have reduced study reliability.

Methods

We chose six datasets from independent studies in the Gene Expression Omnibus (GEO) database and used gene set enrichment analysis and meta-analysis to select key genes and pathways.

Results

We found four down-regulated and four up-regulated pathways through gene set enrichment analysis, and 406 differential genes through meta-analysis. Based on The Cancer Genome Atlas (TCGA), 995 differentially expressed genes were screened out. Comparing the 406 gene set with the 995 gene set, we found 19 common genes, of which 6 had a common pathway and were screened out as key genes regulating and controlling the prognosis of ESCC.

Conclusions

Among the 19 genes, we found three genes that affect the chemotherapy of ESCC: BUB1B, BUB1, and TTK. Another three genes NDC1, NUP107, and NUP155 on the RNA transport pathway were also found. Altogether, these six genes are not only crucial in the development of ESCC, but also determine the prognosis of patients. The key genes and pathways identified in the present study will be used for the next stage in our study, which will involve gene elimination and other experimentation methods.