Integrated analysis reveals key genes with prognostic value in lung adenocarcinoma

KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

KIF2C/MCAK (KIF2C) is the most well-characterized member of the kinesin-13 family, which is critical in the regulation of microtubule (MT) dynamics during mitosis, as well as interphase. This systematic review briefly describes the important structural elements of KIF2C, its regulation by multiple molecular mechanisms, and its broad cellular functions. Furthermore, it systematically summarizes its oncogenic potential in malignant progression and performs a meta-analysis of its prognostic value in cancer patients. KIF2C was shown to be involved in multiple crucial cellular processes including cell migration and invasion, DNA repair, senescence induction and immune modulation, which are all known to be critical during the development of malignant tumors. Indeed, an increasing number of publications indicate that KIF2C is aberrantly expressed in multiple cancer entities. Consequently, we have highlighted its involvement in at least five hallmarks of cancer, namely: genome instability, resisting cell death, activating invasion and metastasis, avoiding immune destruction and cellular senescence. This was followed by a systematic search of KIF2C/MCAK's expression in various malignant tumor entities and its correlation with clinicopathologic features. Available data were pooled into multiple weighted meta-analyses for the correlation between KIF2Chigh protein or gene expression and the overall survival in breast cancer, non-small cell lung cancer and hepatocellular carcinoma patients. Furthermore, high expression of KIF2C was correlated to disease-free survival of hepatocellular carcinoma. All meta-analyses showed poor prognosis for cancer patients with KIF2Chigh expression, associated with a decreased overall survival and reduced disease-free survival, indicating KIF2C's oncogenic potential in malignant progression and as a prognostic marker. This work delineated the promising research perspective of KIF2C with modern in vivo and in vitro technologies to further decipher the function of KIF2C in malignant tumor development and progression. This might help to establish KIF2C as a biomarker for the diagnosis or evaluation of at least three cancer entities.

The role of lncRNA HCG18 in human diseases

A substantial number of long noncoding RNAs (lncRNAs) have been identified as potent regulators of human disease. Human leukocyte antigen complex group 18 (HCG18) is a new type of lncRNA that has recently been proven to play an important role in the occurrence and development of various diseases. Studies have found that abnormal expression of HCG18 is closely related to the clinicopathological characteristics of many diseases. More importantly, HCG18 was also found to promote disease progression by affecting a series of cell biological processes. This article mainly discusses the expression characteristics, clinical characteristics, biological effects and related regulatory mechanisms of HCG18 in different human diseases, providing a scientific theoretical basis for its early clinical application.

Bone sialoprotein promotes lung cancer osteolytic bone metastasis via MMP14-dependent mechanisms

Bone metastases during lung cancer are common. Bone sialoprotein (BSP), a non-collagenous bone matrix protein, plays important functions in bone mineralization processes and in integrin-mediated cell-matrix interactions. Importantly, BSP induces bone metastasis in lung cancer, but the underlying mechanisms remain unclear. This study therefore sought to determine the intracellular signaling pathways responsible for BSP-induced migration and invasion of lung cancer cells to bone. Analyses of the Kaplan-Meier, TCGA, GEPIA and GENT2 databases revealed that high levels of BSP expression in lung tissue samples were associated with significantly decreased overall survival (hazard ratio = 1.17; p=0.014) and with a more advanced clinical disease stage (F-value = 2.38, p<0.05). We also observed that BSP-induced stimulation of matrix metalloproteinase (MMP)-14 promoted lung cancer cell migration and invasion via the PI3K/AKT/AP-1 signaling pathway. Notably, BSP promoted osteoclastogenesis in RAW 264.7 cells exposed to RANKL and BSP neutralizing antibody reduced osteoclast formation in conditioned medium (CM) from lung cancer cell lines. Finally, at 8 weeks after mice were injected with A549 cells or A549 BSP shRNA cells, the findings revealed that the knockdown of BSP expression significantly reduced metastasis to bone. These findings suggest that BSP signaling promotes lung bone metastasis via its direct downstream target gene MMP14, which reveals a novel potential therapeutic target for lung cancer bone metastases.

5-mRNA-based prognostic signature of survival in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis.

AIM

To construct effective predictive models to evaluate the prognosis of LUAD patients.

METHODS

In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.

RESULTS

A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively.

CONCLUSION

A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches

Several studies have been done to identify comorbidities of COVID-19. In this work, we developed an analytical bioinformatics framework to reveal COVID-19 comorbidities, their genomic associations, and molecular mechanisms accomplishing transcriptomic analyses of the RNA-seq datasets provided by the Gene Expression Omnibus (GEO) database, where normal and infected tissues were evaluated. Using the framework, we identified 27 COVID-19 correlated diseases out of 7,092 collected diseases. Analyzing clinical and epidemiological research, we noticed that our identified 27 diseases are associated with COVID-19, where hypertension, diabetes, obesity, and lung cancer are observed several times in COVID-19 patients. Therefore, we selected the above four diseases and performed assorted analyses to demonstrate the association between COVID-19 and hypertension, diabetes, obesity, and lung cancer as comorbidities. We investigated genomic associations with the cross-comparative analysis and Jaccard's similarity index, identifying shared differentially expressed genes (DEGs) and linking DEGs of COVID-19 and the comorbidities, in which we identified hypertension as the most associated illness. We also revealed molecular mechanisms by identifying statistically significant ten pathways and ten ontologies. Moreover, to understand cellular physiology, we did protein-protein interaction (PPI) analyses among the comorbidities and COVID-19. We also used the degree centrality method and identified ten biomarker hub proteins (IL1B, CXCL8, FN1, MMP9, CXCL10, IL1A, IRF7, VWF, CXCL9, and ISG15) that associate COVID-19 with the comorbidities. Finally, we validated our findings by searching the published literature. Thus, our analytical approach elicited interconnections between COVID-19 and the aforementioned comorbidities in terms of remarkable DEGs, pathways, ontologies, PPI, and biomarker hub proteins.

Lung Cancer Gene Regulatory Network of Transcription Factors Related to the Hallmarks of Cancer

The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan-Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients' survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer.

Tumor-promoting roles of HMMR in lung adenocarcinoma

Searching for differential genes in lung adenocarcinoma (LUAD) is vital for research. Hyaluronan mediated motility receptor (HMMR) promotes malignant progression of cancer patients. However, the molecular regulators of HMMR-mediated LUAD onset are unknown. This work aimed to study the relevance of HMMR to proliferation, migration and invasion of LUAD cells. Let-7c-5p and HMMR levels in LUAD cells and HLF-a cells were assessed, and their correlation was also detected. Their interaction was determined by dual-luciferase experiments and qRT-PCR. Cell proliferation, migration and invasion potentials in vitro were validated through cell counting kit-8 (CCK-8), colony formation, scratch healing, and transwell assays. The expression of HMMR was examined by qRT-PCR and western blot and the expression of let-7c-5p was assayed by qRT-PCR. It was found that HMMR level was increased in LUAD and negatively correlated with let-7c-5p level. Let-7c-5p directly targeted HMMR to repress LUAD cell proliferation, migration and invasion. The above data illustrated that the let-7c-5p/HMMR axis may provide certain therapeutic value for LUAD patients.

Identification of BRIP1, NSMCE2, ANAPC7, RAD18 and TTL from chromosome segregation gene set associated with hepatocellular carcinoma

INTRODUCTION

Hepatocellular carcinoma is one of the most frequent cancers with high mortality rate worldwide.

METHODS

TCGA LIHC HTseq counts were analyzed. GSEA was performed with GO BP gene sets. GO analysis was performed with differentially expressed genes. The subset of genes contributing most of the enrichment result of GO_BP_CHROMOSOME_SEGREGATION of GSEA were identified. Five genes have been selected in this subset of genes for further analysis. A microarray data set, GSE112790, was analyzed as a validation data set. Survival analysis was performed.

RESULTS

According to GSEA and GO analysis several gene sets and processes related to chromosome segregation were enriched in LIHC. GO_BP_CHROMOSOME_SEGREGATION gene set from GSEA had the highest size of the genes contributing most of the enrichment. Five genes in this gene set; BRIP1, NSMCE2, ANAPC7, RAD18 and TTL, whose expressions and prognostic values have not been studied in hepatocellular carcinoma in detail, have been selected for further analyses. Expression of these five genes were identified as significantly upregulated in LIHC RNA-seq and HCC microarray data set. Survival analysis showed that high expression of the five genes was associated with poor overall survival in HCC patients.

CONCLUSION

Selected genes were upregulated and had prognostic value in HCC.

The role of RHAMM in cancer: Exposing novel therapeutic vulnerabilities

Receptor for hyaluronic acid-mediated motility (RHAMM) is a cell surface receptor for hyaluronic acid that is critical for cell migration and a cell cycle protein involved in microtubule assembly and stability. These functions of RHAMM are required for cellular stress responses and cell cycle progression but are also exploited by tumor cells for malignant progression and metastasis. RHAMM is often overexpressed in tumors and is an independent adverse prognostic factor for a number of cancers such as breast and prostate. Interestingly, pharmacological or genetic inhibition of RHAMM in vitro and in vivo ablates tumor invasiveness and metastatic spread, implicating RHAMM as a potential therapeutic target to restrict tumor growth and improve patient survival. However, RHAMM’s pro-tumor activity is dependent on its subcellular distribution, which complicates the design of RHAMM-directed therapies. An alternative approach is to identify downstream signaling pathways that mediate RHAMM-promoted tumor aggressiveness. Herein, we discuss the pro-tumoral roles of RHAMM and elucidate the corresponding regulators and signaling pathways mediating RHAMM downstream events, with a specific focus on strategies to target the RHAMM signaling network in cancer cells.

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.

A novel prognostic signature of metastasis-associated genes and personalized therapeutic strategy for lung adenocarcinoma patients

Lung adenocarcinoma (LUAD) is a highly invasive and metastatic malignant tumor with high morbidity and mortality. This study aimed to construct a prognostic signature for LUAD patients based on metastasis-associated genes (MAGs). RNA expression profiles were downloaded from the Cancer Genome Atlas (TCGA) database. RRA method was applied to identify differentially expressed MAGs. A total of 192 significantly robust MAGs were determined among seven GEO datasets. MAGs were initially selected through the Lasso Cox regression analysis and 6 MAGs were included to construct a prognostic signature model. Transcriptome profile, patient prognosis, correlation between the risk score and clinicopathological features, immune cell infiltration characteristics, immunotherapy sensitivity and chemotherapy sensitivity differed between low- and high-risk groups after grouping according to median risk score. The reliability and applicability of the signature were further validated in the GSE31210, GSE50081 and GSE68465 cohort. CMap predicted 62 small molecule drugs on the base of the prognostic MAGs. Targeted drug staurosporine had hydrogen bonding with Gln-172 of SLC2A1, which is one of MAGs. Staurosporine could inhibit cell migration in A549 and H1299. We further verified mRNA and protein expression of 6 MAGs in A549 and H1299. The signature can serve as a promising prognostic tool and may provide a novel personalized therapeutic strategy for LUAD patients.

LINC01234 regulates microRNA-27b-5p to induce the migration, invasion and self-renewal of ovarian cancer stem cells through targeting SIRT5

LINC01234 has been suggested to correlate with the survival of ovarian cancer (OS), but its role in the properties of OC stem cells (OCSCs) has been rarely described. We aim to investigate the effect of LINC01234 on the differentiation and self-renewal of OCSCs through adsorption of microRNA (miR)-27b-5p to target sirtuins 5 (SIRT5). Expression of LINC01234 and SIRT5 in OC and normal samples included in TCGA and GTEx was searched through the GEPIA2 database. Bioinformatics analysis was conducted to predict the relation of LINC01234, miR-27b-5p and SIRT5. Expression of LINC01234, miR-27b-5p and SIRT5 in OC tissues and cells was detected. OCSCs were cultured and identified. CD133⁺ OCSCs were introduced with related oligonucleotides or vectors of LINC01234 or miR-27b-5p and SIRT5 to figure out their roles in OCSCs progression and tumorigenesis in vivo. The interaction of miR-27b-5p with LINC01234 or SIRT5 was analyzed. Bioinformatics analysis suggested that LINC01234 was very likely to influence SIRT5 and regulate the development of OC through miR-27b-5p. Up-regulated LINC01234 exhibited in OC tissues and cells. Down-regulated LINC01234 or elevated miR-27b-5p suppressed OCSCs progression and tumorigenesis in vivo. LINC01234 could restore SIRT5 expression by binding to miR-27b-5p. Down-regulated miR-27b-5p reversed the effect of silenced LINC01234 on OCSCs development and tumorigenesis in vivo. Up-regulation of SIRT5 reduced the effects of elevated miR-27b-5p on OCSCs progression and tumorigenesis in vivo. LINC01234 regulates miR-27b-5p to induce the migration, invasion and self-renewal of OCSCs through targeting SIRT5.

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

Validating HMMR Expression and Its Prognostic Significance in Lung Adenocarcinoma Based on Data Mining and Bioinformatics Methods

Hyaluronic acid-mediated motility receptor (HMMR), a tumor-related gene, plays a vital role in the occurrence and progression of various cancers. This research is aimed to reveal the effect of HMMR in lung adenocarcinoma (LUAD). We first obtained the gene expression profiles and clinical data of patients with LUAD from The Cancer Genome Atlas (TCGA) database. Then, based on the TCGA cohort, the HMMR expression difference between LUAD tissues and nontumor tissues was detected and verified with public tissue microarrays (TMAs), clinical LUAD specimen cohort, and Gene Expression Omnibus (GEO) cohort. Logistic regression analysis and chi-square test were adopted to study the correlation between HMMR expression and clinicopathological parameters. The effect of HMMR expression on survival was evaluated by Kaplan–Meier survival analysis and using the Cox regression model. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to screen out signaling pathways related to LUAD and the co-expression analysis was employed to build the protein–protein interaction (PPI) network. The HMMR expression level in LUAD tissues was dramatically higher than that in nontumor tissues. Logistic regression analysis and chi-square test demonstrated that the high HMMR expression in LUAD has relation with gender, pathological stage, T classification, lymph node metastasis, and distant metastasis. The Kaplan–Meier curve suggested a poor prognosis for LUAD patients with high HMMR expression. Multivariate analysis implied that the high HMMR expression was a vital independent predictor of poor overall survival (OS). GSEA indicated that a total of 15 signaling pathways were enriched in samples with the high HMMR expression phenotype. The PPI network gave 10 genes co-expressed with HMMR. HMMR may be an oncogene in LUAD and is expected to become a potential prognostic indicator and therapeutic target for LUAD.

Expression and clinical significance of AURKB gene in lung adenocarcinoma: Analysis based on the data-mining of bioinformatic database

This study aimed to investigate the expression and clinical significance of aurora B kinase (AURKB) gene in lung adenocarcinoma (LUAD) by collecting relevant data in Oncomine database.Firstly, mRNA expression level of AURKB in LUAD was systematically analyzed using the ONCOMINE and the cancer genome atlas databases. Then, the association between AURKB expression and clinical parameters was investigated by UALCAN. The Kaplan-Meier Plotter was used to assess the prognostic significance of AURKB.Pooled analysis showed that AURKB was frequently up-regulated expression in LUAD. In addition, immunohistochemistry showed that AURKB was highly expressed in lung adenocarcinoma tissues, while it was weakly expressed in normal tissues. Subsequently, AURKB expression was identified to be negatively associated with Overall survival (P < 1e-16), post-progression survival (P = .017), first progression (P = 9.8e-09).This study confirms that increased expression of AURKB in LUAD is associated with poor prognosis, suggesting that AURKB might be used as a promising prognostic biomarker and novel therapeutic target for LUAD.

Development of a gene signature associated with iron metabolism in lung adenocarcinoma

There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Therefore, our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1), and patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and low-risk groups was significantly different (TCGA: P < 0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P < 0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. In addition, immune-related pathways were highlighted in the functional enrichment analysis. In conclusion, we developed and verified a 12-gene prognostic signature, which may be help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.

Bioinformatic analysis of potential hub genes in gastric adenocarcinoma

Gastric adenocarcinoma is the most common histologic type of gastric cancer; however, the pathogenic mechanisms remain unclear. To improve mechanistic understanding and identify new treatment targets or diagnostic biomarkers, we used bioinformatic tools to predict the hub genes related to the process of gastric adenocarcinoma development from public datasets, and explored their prognostic significance. We screened differentially expressed genes between gastric adenocarcinoma and normal gastric tissues in Gene Expression Omnibus datasets (GSE79973, GSE118916, and GSE29998) using the GEO2R tool, and their functions were annotated with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses in the DAVID database. Hub genes were identified based on the protein-protein network constructed in the STRING database with Cytoscape software. A total of 10 hub genes were selected for further analysis, and their expression patterns in gastric adenocarcinoma patients were investigated using the Oncomine GEPIA database. The expression levels of ATP4A, CA9, FGA, ALDH1A1, and GHRL were reduced, whereas those of TIMP1, SPP1, CXCL8, THY1, and COL1A1 were increased in gastric adenocarcinoma. The Kaplan-Meier online plotter tool showed associations of all hub genes except for CA9 with prognosis in gastric adenocarcinoma patients; CXCL8 and ALDH1A1 were positively correlated with survival, and the other genes were negatively correlated with survival. These 10 hub genes may be involved in important processes in gastric adenocarcinoma development, providing new directions for research to clarify the role of these genes and offer insight for improved treatment.

Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis

Colorectal cancer (CRC) is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients. In this study, mRNA microarray datasets GSE113513, GSE21510, GSE44076, and GSE32323 were obtained from the Gene Expression Omnibus (GEO) and analyzed with bioinformatics to identify hub genes in CRC development. Differentially expressed genes (DEGs) were analyzed using the GEO2R tool. Gene ontology (GO) and KEGG analyses were performed through the DAVID database. STRING database and Cytoscape software were used to construct a protein-protein interaction (PPI) network and identify key modules and hub genes. Survival analyses of the DEGs were performed on GEPIA database. The Connectivity Map database was used to screen potential drugs. A total of 865 DEGs were identified, including 374 upregulated and 491 downregulated genes. These DEGs were mainly associated with metabolic pathways, pathways in cancer, cell cycle and so on. The PPI network was identified with 863 nodes and 5817 edges. Survival analysis revealed that HMMR, PAICS, ETFDH, and SCG2 were significantly associated with overall survival of CRC patients. And blebbistatin and sulconazole were identified as candidate drugs. In conclusion, our study found four hub genes involved in CRC, which may provide novel potential biomarkers for CRC prognosis, and two potential candidate drugs for CRC.

Integration of gene profile to explore the hub genes of lung adenocarcinoma: A quasi-experimental study

BACKGROUND

Lung cancer is a leading cause of morbidity diseases worldwide, but the key mechanisms of lung cancer remain elusive. This study aims to integrate of GSE 118370 and GSE 32863 profile and identify the key genes and pathway involved in human lung adenocarcinoma.

METHODS

R software (RStudio, Version info: R 3.2.3, Forrester, USA) were utilized to find the differentially expressed genes. All the differentially expressed genes were analyzed by gene ontology, kyoto encyclopedia of genes and genomes. Protein-protein interaction networks were constructed by STRING database and analyzed by Cytohubber and Module. The cancer genome atlas database was used to verification the expression of hub genes. Quantitative reverse transcription-PCR was used to verify the bio-information results.

RESULTS

Sixty-four lung adenocarcinoma and 64 adjacent normal tissues were used for integration analysis. Five hundred ninety-nine co-expression genes were locked. Biological processes mainly enriched in angiogenesis. Cellular component focused on extracellular exosome and molecular function aimed on protein disulfide isomerase activity. Cytohubber analysis showed that GNG11, FPR2, P4HB, PIK3R1, CDC20, ADCY4, TIMP1, IL6, CXC chemokine ligand (CXCL)12, and GAS6 acted as the hub genes during lung adenocarcinoma. Module analysis presented Chemokine signaling pathway was a key pathway. Quantitative reverse transcription-PCR showed that the expression level of GNG11, FPR2, PIK3R1, ADCY4, IL6, CXCL12, and GAS6 were significantly decreased and P4HB, CDC20 and TIMP1 were increased in human adenocarcinoma tissues (P < .05). The cancer genome atlas online analysis showed GNG11 was not associated with survival.

CONCLUSIONS

This study firstly reported GNG11 acting as a hub gene in adenocarcinoma. GNG11 could be used as a biomarker for human adenocarcinoma. Chemokine signaling pathway might play important roles in lung adenocarcinoma.

Identification of Potential Hub Genes and Signal Pathways Promoting the Distinct Biological Features of Cord Blood-Derived Endothelial Progenitor Cells Via Bioinformatics

Background: Numerous studies, ranging from the alleviation of tissue ischemia to the assessment of cancer prognosis, have demonstrated the fundamental biological differences between human umbilical cord blood-derived endothelial progenitor cells (CB-EPCs) and adult peripheral blood-derived endothelial progenitor cells (PB-EPCs). However, the underlying molecular mechanisms that produce these differences are not clear.The purpose of this study was to identify potential hub genes, key protein interactive networks, and correlated signal pathways unique to CB-EPC biology via bioinformatic methods. Materials and Methods: We selected the microarray dataset GSE39763 and identified the differentially expressed genes (DEGs) using the "limma" package in the RStudio software. These DEGs were annotated by gene ontology enrichment analyses and signal pathway analyses. A protein-protein interaction (PPI) analysis was then performed to construct PPI networks and identify a hub protein module. We further validated candidate DEGs from the selected module in the gene expression profiling interactive analysis (GEPIA) database because the DEGs were enriched in cancer pathways. Results: Setting an adjusted p-value <0.01 and |Log₂ fold change (FC)| ≥ 2 as cutoff criteria, a total of 346 DEGs, including 314 upregulated genes and 32 downregulated genes in CB-EPCs, were identified. Expression of the genes encoding the AT-Hook Containing Transcription Factor 1 (AHCTF1), the Cancer Susceptibility Candidate 5 (CASC5), the Centromere Protein C (CENPC), the Centromere Protein E (CENPE), the Centromere Protein F (CENPF), the NUF2 Component of NDC80 Kinetochore Complex (NUF2), the RAN-Binding Protein 2 (RANBP2), the Shugoshin-like 2 (SGOL2), the Structural Maintenance of Chromosomes 3 (SMC3), and the Spindle Apparatus Coiled-Coil Protein 1 (SPDL1) proteins were specifically associated with CB-EPCs. Except for CENPC, the other nine genes' expression are all associated with a poorer overall survival rate in cancers. The expression levels of the CENPF and NUF2 genes in tumor patients were significantly higher than those in the controls. Conclusion: The CB-EPCs express genes with greater potential for proliferation and increased migration compared to PB-EPCs; in this regard they are similar to cancer cells.

Identification of 17 mRNAs and a miRNA as an integrated prognostic signature for lung squamous cell carcinoma

BACKGROUND

Gene signatures for predicting the outcome of lung squamous cell carcinoma (LUSC) have been employed for many years. However, various signatures have been applied in clinical practice. Therefore, in the present study, we aimed to filter out an effective LUSC prognostic gene signature by simultaneously integrating mRNA and microRNA (miRNA).

METHODS

First, based on data from the Cancer Genome Atlas (TCGA) (https://www.cancer.gov/tcga), mRNAs and miRNAs that were related to overall survival of LUSC were obtained by the least absolute shrinkage and selection operator method. Subsequently, the predicting effect was tested by time-dependent receiver operating characteristic curve analysis and Kaplan-Meier survival analysis. Next, related clinical indices were added to evaluate the efficiency of the selected gene signatures. Finally, validation and comparison using three independent gene signatures were performed using data from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo).

RESULTS

Our data showed that the prognostic index (PI) contained 17 mRNAs and one miRNA. According to the best normalized cut-off of PI (0.0247), the hazard ratio of the PI was 3.40 (95% confidence interval = 2.33-4.96). Moreover, when clinical factors were introduced, the PI was still the most significant index. In addition, only two Gene Ontology terms with p < 0.05 were reported. Furthermore, validation implied that, using our 18-gene signature, only hazard ratio = 1.36 (95% confidence interval = 1.01-1.83) was significant compared to the other three groups of gene biomarkers.

CONCLUSIONS

The 18-gene signature selected based on data from the TCGA database had an effective prognostic value for LUSC patients.

Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data

PURPOSE

Intra-tumor heterogeneity and high mortality among patients with non-small-cell lung carcinoma (NSCLC) emphasize the need to identify reliable prognostic markers unique to each subtype.

METHODS

In this study, univariate cox regression and prognostic index (PI)-based approaches were used to develop models for predicting NSCLC patients' subtype-specific survival.

RESULTS

Prognostic analysis of TCGA dataset identified 1334 and 2129 survival-specific genes for LUSC (488 samples) and LUAD (497 samples), respectively. Individually, 32 and 271 prognostic genes were found and validated in GSE study exclusively for LUSC and LUAD. Nearly, 9-10% of the validated genes in each subtype were already reported in multiple studies thus highlighting their importance as prognostic biomarkers. Strong literature evidence against these prognostic genes like "ELANE" (LUSC) and "AHSG" (LUAD) instigates further investigation for their therapeutic and diagnostic roles in the corresponding cohorts. Prognostic models built on five and four genes were validated for LUSC [HR = 2.10, p value = 1.86 × 10^-5] and LUAD [HR = 2.70, p value = 3.31 × 10^-7], respectively. The model based on the combination of age and tumor stage performed well in both NSCLC subtypes, suggesting that despite having distinctive histological features and treatment paradigms, some clinical features can be good prognostic predictors in both.

CONCLUSION

This study advocates that investigating the survival-specific biomarkers restricted to respective cohorts can advance subtype-specific prognosis, diagnosis, and treatment for NSCLC patients. Prognostic models and markers described for each subtype may provide insight into the heterogeneity of disease etiology and help in the development of new therapeutic approaches for the treatment of NSCLC patients.

Identification of candidate genes and prognostic value analysis in patients with PDL1-positive and PDL1-negative lung adenocarcinoma

Background

Increasing bodies of evidence reveal that targeting a programmed cell death protein 1 (PD-1) monoclonal antibody is a promising immunotherapy for lung adenocarcinoma. Although PD receptor ligand 1 (PDL1) expression is widely recognized as the most powerful predictive biomarker for anti-PD-1 therapy, its regulatory mechanisms in lung adenocarcinoma remain unclear. Therefore, we conducted this study to explore differentially expressed genes (DEGs) and elucidate the regulatory mechanism of PDL1 in lung adenocarcinoma.

Methods

The GSE99995 data set was obtained from the Gene Expression Omnibus (GEO) database. Patients with and without PDL1 expression were divided into PDL1-positive and PDL1-negative groups, respectively. DEGs were screened using R. The Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) networks of DEGs was visualized using Cytoscape, and the MNC algorithm was applied to screen hub genes. A survival analysis involving Gene Expression Profiling Interactive Analysis was used to verify the GEO results. Mutation characteristics of the hub genes were further analyzed in a combined study of five datasets in The Cancer Genome Atlas (TCGA) database.

Results

In total, 869 DEGs were identified, 387 in the PDL1-positive group and 482 in the PDL1-negative group. GO and KEGG analysis results of the PDL1-positive group mainly exhibited enrichment of biological processes and pathways related to cell adhesion and the peroxisome proliferators-activated receptors (PPAR) signaling pathway, whereas biological process and pathways associated with cell division and repair were mainly enriched in the PDL1-negative group. The top 10 hub genes were screened during the PPI network analysis. Notably, survival analysis revealed BRCA1, mainly involved in cell cycle and DNA damage responses, to be a novel prognostic indicator in lung adenocarcinoma. Moreover, the prognosis of patients with different forms of lung adenocarcinoma was associated with differences in mutations and pathways in potential hub genes.

Conclusions

PDL1-positive lung adenocarcinoma and PDL1-negative lung adenocarcinoma might be different subtypes of lung adenocarcinoma. The hub genes might play an important role in PDL1 regulatory pathways. Further studies on hub genes are warranted to reveal new mechanisms underlying the regulation of PDL1 expression. These results are crucial for understanding and applying precision immunotherapy for lung adenocarcinoma.

HCG18/miR-34a-5p/HMMR axis accelerates the progression of lung adenocarcinoma

As the most common subtype of lung cancer, lung adenocarcinoma (LUAD) is the frequently occurred cancers in human. Therefore, thorough investigation is necessary for understanding the progression of LUAD. HMMR has functioned as a regulator in some cancers, whereas its biological role still needs to be investigated in LUAD. By bioinformatics analysis, we found that HMMR was highly expressed in LUAD tissues and associated with patients' poor prognosis. Further, qRT-PCR demonstrated that HMMR was up-regulated in LUAD tissues and cells. Loss-of-function assays manifested that HMMR knockdown refrained cell proliferation, migration and invasion and enhanced cell apoptosis in LUAD. Later, HMMR was identified as a target gene of miR-34a-5p, which expressed at a low level in LUAD cell and played an anti-oncogenic role in LUAD. Simultaneously, we discovered that miR-34a-5p could directly bind to HCG18. Subsequent assays revealed that HCG18 mediated HMMR expression by sequestering miR-34a-5p. At last, rescue assays proved the carcinogenic role of HCG18/miR-34a-5p/HMMR axis in LUAD cells growth. Importantly, HCG18 was found to facilitate tumor growth in LUAD. Conclusively, HCG18 acted an oncogene in LUAD and enhanced LUAD progression by targeting miR-34a-5p/HMMR axis.

Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

Purposes

Cervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes.

Results

The mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues.

Conclusion

We have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

Identification of prognostic markers of lung cancer through bioinformatics analysis and in vitro experiments

Lung cancer is one of the most common types of cancer worldwide. Understanding the molecular mechanisms underlying the development and progression of lung cancer may improve early diagnosis, treatment and prognosis. The aim of the present study was to examine the pathogenesis of lung cancer and to identify potentially novel biomarkers. Gene expression datasets of patients with lung cancer were obtained from the Gene Expression Omnibus. Genes which were most closely associated with lung cancer (core genes) were screened by weighted gene co‑expression network analysis. In vitro cell based experiments were further utilized to verify the effects of the core genes on the proliferation of lung cancer cells, adhesion between cells and the matrix, and the associated metabolic pathways. Based on WGCNA screening, two gene modules and five core genes closely associated with lung cancer, including immunoglobulin superfamily member 10 (IGSF10) from the turquoise module, and ribonucleotide reductase regulatory subunit M2, protein regulator of cytokinesis 1, kinesin family member (KIF)14 and KIF2C from the brown module were identified as relevant. Survival analysis and differential gene expression analysis showed that there were significant differences in IGSF10 expression levels between the healthy controls and patients with lung cancer. In patients with lung cancer, IGSF10 expression was decreased, and the overall survival time of patients with lung cancer was significantly shortened. An MTT and colony formation assay showed that IGSF10‑knockout significantly increased proliferation of lung cancer cells, and Transwell assays and adhesion experiments further suggested that the adhesion between cells and the matrix was significantly increased in IGSF10‑knockout cells. Gene Set Enrichment Analysis showed that the expression level of IGSF10 was significantly associated with the activation of the integrin‑β1/focal adhesion kinase (FAK) pathway. Western blotting revealed that knockout of IGSF10 resulted in the activation of the integrin‑β1/FAK pathway, as the protein expression levels of integrin‑β1, phosphorylated (p)‑FAK and p‑AKT were significantly upregulated. Activation of the integrin‑β1/FAK pathway, following knockout of IGSF10, affected the proliferation and adhesion of lung cancer cells. Therefore, IGSF10 my serve as a potential prognostic marker of lung cancer.

Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases

Background: Epidemiological and clinical evidence points cancer comorbidity with pulmonary chronic disease. The acquisition of some hallmarks of cancer by cells affected with lung pathologies as a cell adaptive mechanism to a shear stress, suggests that could be associated with the establishment of tumoral processes. Objective: To propose a bioinformatic pipeline for the identification of all deregulated genes and the transcriptional regulators (TFs) that are coexpressed during lung cancer establishment, and therefore could be important for the acquisition of the hallmarks of cancer. Methods: Ten microarray datasets (six of lung cancer, four of lung diseases) comparing normal and diseases-related lung tissue were selected to identify hub differentiated expressed genes (DEGs) in common between lung pathologies and lung cancer, along with transcriptional regulators through the utilization of specialized libraries from R language. DAVID bioinformatics tool for gene enrichment analyses was used to identify genes with experimental evidence associated to tumoral processes and signaling pathways. Coexpression networks of DEGs and TFs in lung cancer establishment were created with Coexnet library, and a survival analysis of the main hub genes was made. Results: Two hundred ten DEGs were identified in common between lung cancer and other lung diseases related to the acquisition of tumoral characteristics, which are coexpressed in a lung cancer network with TFs, suggesting that could be related to the establishment of the tumoral pathology in lung. The comparison of the coexpression networks of lung cancer and other lung diseases allowed the identification of common connectivity patterns (CCPs) with DEGs and TFs correlated to important tumoral processes and signaling pathways, that haven´t been studied to experimentally validate their role in the early stages of lung cancer. Some of the TFs identified showed a correlation between its expression levels and the survival of lung cancer patients. Conclusion: Our findings indicate that lung diseases share genes with lung cancer which are coexpressed in lung cancer, and might be able to explain the epidemiological observations that point to direct and inverse comorbid associations between some chronic lung diseases and lung cancer and represent a complex transcriptomic scenario.

Identification of key genes and evaluation of clinical outcomes in lung squamous cell carcinoma using integrated bioinformatics analysis

Lung cancer is the leading cause of cancer-related mortality worldwide. Despite progress in the treatment of non-small-cell lung cancer, there are limited treatment options for lung squamous cell carcinoma (LUSC), compared with lung adenocarcinoma. The present study investigated the disease mechanism of LUSC in order to identify key candidate genes for diagnosis and therapy. A total of three gene expression profiles (GSE19188, GSE21933 and GSE74706) were analyzed using GEO2R to identify common differentially expressed genes (DEGs). The DEGs were then investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes/Proteins, and visualized using Cytoscape software. The expression levels of the hub genes identified using CytoHubba were validated using the University of California, Santa Cruz (UCSC) database and the Human Protein Atlas. A Kaplan-Meier curve and Gene Expression Profiling Interactive Analysis were then employed to evaluate the associated prognosis and clinical pathological stage of the hub genes. Furthermore, non-coding RNA regulatory networks were constructed using the Gene-Cloud Biotechnology information website. A total of 359 common DEGs (155 upregulated and 204 downregulated) were identified, which were predominantly enriched in 'mitotic nuclear division', 'cell division', 'cell cycle' and 'p53 signaling pathway'. The PPI network consisted of 257 nodes and 2,772 edges, and the most significant module consisted of 66 upregulated genes. A total of 19 hub genes exhibited elevated RNA levels, and 10 hub genes had elevated protein levels compared with normal lung tissues. The upregulation of five hub genes (CCNB1, CEP55, FOXM1, MKI67 and TYMS; defined in Table I) were significantly associated with poor overall survival and unfavorable clinical pathological stages. Various ncRNAs, such as C1orf220, LINC01561 and MGC39584, may also play important roles in hub-gene regulation. In conclusion, the present study provides further understanding of the pathogenesis of LUSC, and reveals CCNB1, CEP55, FOXM1, MKI67 and TYMS as potential biomarkers or therapeutic targets.

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.

Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC. Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p<0.05, |log2fold change| >1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages. Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR]=2.673, 95% CI=1.871-3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction. Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.

miRNA-99b-5p targets FZD8 to inhibit non-small cell lung cancer proliferation, migration and invasion

BACKGROUND

miRNAs were found to play crucial roles in regulating cellular behaviors. The aim of this study was to investigate the biological function of miRNA-99b-5p (miR-99b-5p) in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

miR-99b-5p expression level in NSCLC cell lines was detected by quantitative real-time PCR (qRT-PCR). Cell proliferation, migration and invasion were examined by cell counting kit-8 (CCK-8) assay, wound-healing assay and Transwell invasion assay, respectively. Dual-luciferase activity reporter assay and Western blot assay were conducted to validate the target of miR-99b-5p.

RESULTS

The expression of miR-99b-5p was decreased in NSCLC cell lines compared with normal cell line. Overexpression of miR-99b-5p inhibits cell proliferation, migration and invasion in vitro. FZD8 was validated as a direct target of miR-99b-5p. Overexpression of FZD8 partially abolished the effects of miR-99b-5p mimic on NSCLC cell behaviors.

CONCLUSION

Collectively, our results demonstrated that miR-99b-5p inhibits NSCLC cell proliferation, migration and invasion through targeting FZD8. This newly identified miR-99b-5p/FZD8 axis provided novel insights into the mechanisms underlying NSCLC progression.