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

High Expression of Heterogeneous Nuclear Ribonucleoprotein A1 Facilitates Hepatocellular Carcinoma Growth

Purpose

Hepatocellular carcinoma (HCC) represents one of the most common tumors in the world. Our study aims to explore new markers and therapeutic targets for HCC. Heterogeneous Nuclear ribonucleoprotein A1 (hnRNPA1) has recently been found to be involved in the progression of several types of cancer, but its role in HCC remains uncovered.

Methods

We performed bioinformatic analysis to preliminarily show the relationship between hnRNPA1 and liver cancer. Then the correlation of the hnRNPA1 gene expression with clinicopathological characteristics of HCC patients was verified by human liver cancer tissue microarrays. The functional role of this gene was evaluated by in vivo and vitro experiments.

Results

Results showed that the expression of hnRNPA1 was upregulated in HCC tissues and was associated with pathological stage of HCC patients. Knockdown of hnRNPA1 gene markedly inhibited tumor growth in vivo, and reversed the effects on proliferation, migration and invasion and promoted apoptosis in vitro. Furthermore, down-regulation of hnRNPA1 gene expression can inhibit the activity of the MEK/ERK pathway.

Conclusion

In our work, we combined bioinformatic analysis with in vivo and in vitro experiments to initially elucidate the function of hnRNPA1 in liver cancer, which may help to explore biomarkers and therapeutic targets for HCC patients.

Landscape and Construction of a Novel N6-methyladenosine-related LncRNAs in Cervical Cancer

Cervical cancer is a crucial clinical problem with high mortality. Despite much research in therapy, the prognosis of patients with cervical cancer is still not ideal. The data on cervical cancer were downloaded from The Cancer Genome Atlas (TCGA) portal. R language was used to screen out the N6-methyladenosine (m6A)-related lncRNAs (long non-coding RNA). A consensus clustering algorithm was performed to identify m6A-related lncRNAs in cervical cancer; 10 m6A-related lncRNAs showing a significant association with survival were filtrated through a gradually univariate Cox regression model, least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression preliminarily. Furthermore, we conducted Kaplan-Meier curves, receiver operating curve (ROC) analyses, and proportional hazards model to quantify the underlying character of the m6A-related model in the prevision of cervical cancer patients. Gene set enrichment analysis (GSEA) was used to explore several pathways significantly. Finally, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was applied to estimate the immune cell infiltration in the profiling. In the present study, 10 m6A-related lncRNAs make up our prediction model. This prediction model can do duty for an independent predictive biomolecular element. Subsequently, we then found that the model was still valid in further validation of the training group and the test group. Our signature was correlated with immune cell infiltration and partial signaling pathway. These lncRNAs played a no negligible biomolecular role in contributing to the prognosis of cervical cancer.

MiR-17-3p Facilitates Aggressive Cell Phenotypes in Colon Cancer by Targeting PLCD1 Through Affecting KIF14

Colon cancer (CC) is a common and lethal cancer to be further elucidated. Accumulating studies elaborated the crucial role of miRNAs differentially expressed in cancer cell growth. In the present study, differentially expressed miRNAs related to CC were screened by the bioinformatics methods on the strength of TCGA database. Highly expressed miR-17-3p was proved to notably influence CC cell proliferative, migratory, invasion, and apoptotic levels. By using TargetScan and miRTarBase databases, phospholipase C delta 1 (PLCD1) was predicted as a target downstream of miR-17-3p, and their binding site was predicted. Through TCGA database, low expression of PLCD1 and its significant negative correlation with miR-17-3p were identified in CC. Dual-luciferase reporter gene analysis ascertained the targeting relationship between miR-17-3p and PLCD1. Cell Counting Kit-8, colony formation, and transwell assays were introduced to detect CC cell malignant progression. Flow cytometry was applied to detect CC cell apoptosis. As result revealed, miR-17-3p was markedly highly expressed, and PLCD1, the target of miR-17-3p, was remarkably lowly expressed in CC cells. Forced expression of miR-17-3p facilitated CC cell proliferation, migration, invasion, and suppressed apoptosis. Biological roles of upregulating miR-17-3p in the colon cancer cells were markedly weakened by over-expressing PLCD1 simultaneously. MiR-17-3p regulated CC cell malignant progression, as well as apoptosis by targeting PLCD1. Moreover, KIF14 was extensively considered as an involved tumor-promoting gene that could be affected by miR-17-3p/PLCD1 axis based on BioGRID analysis and CO-IP assay. Concludingly, this study exhibited that miR-17-3p facilitated CC progression by PLCD1 downregulation.

Transcriptomic analysis predicts the risk of progression of premalignant lesions in human tongue

The 5-year survival rate for patients with oral squamous cell carcinomas (SCC), including tongue SCC, has not significantly improved over the last several decades. Oral potentially malignant disorders (OPMD), including oral dysplasias, are oral epithelial disorders that can develop into oral SCCs. To identify molecular characteristics that might predict conversion of OPMDs to SCCs and guide treatment plans, we performed global transcriptomic analysis of human tongue OPMD (n = 9) and tongue SCC (n = 11) samples with paired normal margin tissue from patients treated at Weill Cornell Medicine. Compared to margin tissue, SCCs showed more transcript changes than OPMDs. OPMDs and SCCs shared some altered transcripts, but these changes were generally greater in SCCs than OPMDs. Both OPMDs and SCCs showed altered signaling pathways related to cell migration, basement membrane disruption, and metastasis. We suggest that OPMDs are on the path toward malignant transformation. Based on patterns of gene expression, both OPMD and tongue SCC samples can be categorized into subclasses (mesenchymal, classical, basal, and atypical) similar to those seen in human head and neck SCC (HNSCC). These subclasses of OPMDs have the potential to be used to stratify patient prognoses and therapeutic options for tongue OPMDs. Lastly, we identified a gene set (ELF5; RPTN; IGSF10; CRMP1; HTR3A) whose transcript changes have the power to classify OPMDs and SCCs and developed a Firth logistic regression model using the changes in these transcripts relative to paired normal tissue to validate pathological diagnosis and potentially predict the likelihood of an OPMD developing into SCC, as data sets become available.

Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis

Background

Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.

Methods

To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.

Results

There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.

Conclusions

Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

Pan-cancer analyses reveal IGSF10 as an immunological and prognostic biomarker

Background: IGSF10 is a member of the immunoglobulin superfamily. Over the previous decade, growing proof has validated definitive correlations between individuals of the immunoglobulin superfamily and human diseases. However, the function of IGSF10 in pan-cancer stays unclear. We aimed to analyze the immunological and prognostic value of IGSF10 in pan-cancer. Methods: We utilized a vary of bioinformatic ways to inspect the function of IGSF10 in pan-cancer, including its correlation with prognosis, immune cell infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), DNA methyltransferases, genetic alteration, drug sensitivity, etc. Results: We noticed low expression of IGSF10 in most cancer types. IGSF10 expression in tumor samples correlates with prognosis in most cancers. In most cancer types, IGSF10 expression was strongly related to immune cells infiltration, immune checkpoints, immune modulators, TMB, MSI, MMR, and DNA methyltransferases, among others. Functional enrichment analyses indicated that IGSF10 expression was involved in lymphocyte differentiation, cell molecules adhesion, etc. Furthermore, low IGSF10 expression could increase the drug sensitivity of many drugs. Conclusion: IGSF10 could serve as a novel prognostic marker and attainable immunotherapy target for several malignancies.

Enhancer-associated regulatory network and gene signature based on transcriptome and methylation data to predict the survival of patients with lung adenocarcinoma

Accumulating evidence has proved that aberrant methylation of enhancers plays regulatory roles in gene expression for various cancers including lung adenocarcinoma (LUAD). In this study, the transcriptome and methylation data of The Cancer Genome Atlas (TCGA)-LUAD cohort were comprehensively analyzed with a five-step Enhancer Linking by Methylation/Expression Relationships (ELMER) process. Step 1: 131,371 distal (2 kb upstream from the transcription start site) probes were obtained. Step 2: 10,665 distal hypomethylated probes were identified in an unsupervised mode with the get.diff.meth function. Step 3: 699 probe-gene pairs with negative correlations were screened using the get.pair function in an unsupervised mode. Step 4: After mapping with probes, 768 motifs were obtained and 24 of them were enriched. Step 5: 127 transcription factors (TFs) with differential expressions and negative correlations with methylation levels were screened, which were corresponding to 21 motifs. After the ELMER process, a prognostic “TFs-motifs-genes” regulatory network was constructed. The Least absolute shrinkage and selection operator (LASSO) and Stepwise regression analyses were further applied to identify variables in the TCGA-LUAD cohort and an eight-gene signature was constructed for calculating the risk score. The risk score was verified in two independent validation cohorts. The area under curve values of receiver operating characteristic curves predicting 1-, 3-, and 5-years survival ranged from 0.633 to 0.764. With the increase of the risk scores, both the survival statuses and clinical traits showed a worse tendency. There were significant differences in the degrees of immune cell infiltration, TMB values, and TIDE scores between the high-risk and low-risk groups. Finally, a better-performing prognostic nomogram was integrated with the risk score and other clinical traits. In short, this multi-omics analysis demonstrated the application of ELMER in analyzing enhancer-associated regulatory network in LUAD, which provided promising strategies for epigenetic therapy and prognostic biomarkers.

MicroRNA-106b-5p inhibits growth and progression of lung adenocarcinoma cells by downregulating IGSF10

In this study, we investigated the mechanistic role and prognostic significance of IGSF10 in lung adenocarcinoma. Oncomine database analysis showed that IGSF10 expression was significantly reduced in most cancer types, including lung adenocarcinoma (LUAD). In the TCGA-LUAD dataset, IGSF10 expression correlated positively with proportions of tumor-infiltrated B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells. Kaplan-Meier survival analysis showed that overall survival of patients with low IGSF10 expression was significantly shorter than those with high IGSF10 expression. MiRWalk2.0 database analysis and dual luciferase reporter assays confirmed that miR-106b-5p suppressed IGSF10 expression by binding to its 3’UTR. MiR-106b-5p levels inversely correlated with IGSF10 expression in the TCGA-LUAD dataset. Moreover, inhibition of miR-106b-5p significantly decreased in vitro proliferation, migration, and invasion by LUAD cells, whereas miR-106b-5p overexpression reversed those effects. These results demonstrate that IGSF10 is an independent prognostic factor for LUAD. Furthermore, miR-106b-5p suppressed IGSF10 expression in LUAD tissues by binding to its 3’UTR, which makes IGSF10 and miR-106b-5p potential prognostic biomarkers and therapeutic targets in LUAD patients.

Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients.

Method

We extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients.

Results

A total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models.

Conclusion

In this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.

MYCT1 Inhibits the Adhesion and Migration of Laryngeal Cancer Cells Potentially Through Repressing Collagen VI

MYCT1, a target of c-Myc, inhibits laryngeal cancer cell migration, but the underlying mechanism remains unclear. In the study, we detected differentially expressed genes (DEGs) from laryngeal cancer cells transfected by MYCT1 using RNA-seq (GSE123275). DEGs from head and neck squamous cell carcinoma (HNSCC) were first screened by comparison of transcription data from the Gene Expression Omnibus (GSE6631) and the Cancer Genome Atlas (TCGA) datasets using weighted gene co-expression network analysis (WGCNA). GO and KEGG pathway analysis explained the functions of the DEGs. The DEGs overlapped between GSE6631and TCGA datasets were then compared with ours to find the key DEGs downstream of MYCT1 related to the adhesion and migration of laryngeal cancer cells. qRT-PCR and Western blot were applied to validate gene expression at mRNA and protein levels, respectively. Finally, the cell adhesion, migration, and wound healing assays were to check cell adhesion and migration abilities, respectively. As results, 39 overlapping genes were enriched in the GSE6631 and TCGA datasets, and most of them revealed adhesion function. Thirteen of 39 genes including COL6 members COL6A1, COL6A2, and COL6A3 were overlapped in GSE6631, TCGA, and GSE123275 datasets. Similar to our RNA-seq results, we confirmed that COL6 is a target of MYCT1 in laryngeal cancer cells. We also found that MYCT1 inhibited the adhesion and migration of laryngeal cancer cells via COL6. These indicate that COL6 is a potential target of MYCT1 and participates the adhesion and migration of laryngeal cancer cells, which provides an important clue for further study on how MYCT1 regulating COL6 in laryngeal cancer progression.

Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis

Background:

Breast cancer is the most commonly diagnosed malignancy and a major cause of cancer-related deaths in women globally. Identification of novel prognostic and pathogenesis biomarkers play a pivotal role in the management of the disease.

Methods:

Three data sets from the GEO database were used to identify differentially expressed genes (DEGs) in breast cancer. Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway analyses were performed to elucidate the functional roles of the DEGs. Besides, we investigated the translational and protein expression levels and survival data of the DEGs in patients with breast cancer from the Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine, Human Protein Atlas, and Kaplan Meier plotter tool databases. The corresponding change in the expression level of microRNAs in the DEGs was also predicted using miRWalk and TargetScan, and the expression profiles were analyzed using OncomiR. Finally, the expression of novel DEGs were validated in Chinese breast cancer tissues by RT-qPCR.

Results:

A total of 46 DEGs were identified, and GO analysis revealed that these genes were mainly associated with biological processes involved in fatty acid, lipid localization, and regulation of lipid metabolism. Two novel biomarkers, ADH1A and IGSF10, and 4 other genes (APOD, KIT, RBP4, and SFRP1) that were implicated in the prognosis and pathogenesis of breast cancer, exhibited low expression levels in breast cancer tissues. Besides, 14/25 microRNAs targeting 6 genes were first predicted to be associated with breast cancer prognosis. RT-qPCR results of ADH1A and IGSF10 expression in Chinese breast cancer tissues were consistent with the database analysis and showed significant down-regulation.

Conclusion:

ADH1A, IGSF10, and the 14 microRNAs were found to be potential novel biomarkers for the diagnosis, treatment, and prognosis of breast cancer.

Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma

Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (TTK, HMMR, ASPM, CDCA8, KIF2C, CCNA2, CCNB2, and MKI67) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD.

Immunoglobulin superfamily member 10 is a novel prognostic biomarker for breast cancer

Background

Immunoglobulin superfamily member 10 (IGSF10) is a member of the immunoglobulin superfamily that is expressed at high levels in both the gallbladder and ovary. Currently, the role and possible mechanism of IGSF10 in breast cancer remain unclear.

Method

By applying real-time quantitative polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC), the expression of IGSF10 in breast cancer cells and tissues was detected. We collected the clinical information from 700 patients with breast cancer in The Cancer Genome Atlas (TCGA), and analyzed the relationship between IGSF10 expression and the clinicopathological features and survival outcomes of these patients. The potential mechanisms and pathways associated with IGSF10 in breast cancer were explored by performing a gene set enrichment analysis (GSEA).

Results

According to TCGA data, qRT-PCR and IHC experiments, levels of the IGSF10 mRNA and protein were significantly decreased in breast cancer tissues. IGSF10 expression was significantly correlated with age, tumor size, and tumor stage. Moreover, shorter overall survival (OS) and relapse-free survival (RFS) correlated with lower IGSF10 expression, according to the survival analysis. The multivariate analysis identified that IGSF10 as an independent prognostic factor for the OS (hazard ratio (HR) = 1.793, 95% confidence interval (CI) [1.141–2.815], P = 0.011) and RFS (HR = 2.298, 95% CI [1.317–4.010], P = 0.003) of patients with breast cancer. Based on the GSEA, IGSF10 was involved in DNA repair, cell cycle, and glycolysis. IGSF10 was also associated with the PI3K/Akt/mTOR and mTORC1 signaling pathways.

Conclusions

This study revealed a clear relationship between IGSF10 expression and the tumorigenesis of breast cancer for the first time. Therefore, further studies are needed to understand the mechanism of IGSF10 in breast cancer.

Caspase Recruitment Domain-Containing Protein 9 Expression is a Novel Prognostic Factor for Lung Adenocarcinoma

Purpose

Caspase recruitment domain-containing protein 9 (CARD9) is expressed at high levels in bone marrow cells and has a crucial role in innate immunity. Current studies indicate that CARD9 also plays a key role in tumor progression, but there are few reports on the role of CARD9 in lung cancer. The aim of this study was to clarify the role of CARD9 in lung adenocarcinoma.

Patients and Methods

Lung adenocarcinoma tumor samples from 74 patients who underwent complete resection at Kobe University Hospital from January 2014 to December 2014 were analyzed by immunohistochemistry. The role of CARD9 in cancer cells was analyzed using lung cancer cell lines treated with CARD9 siRNA.

Results

High expression of CARD9 was observed in 32.4% of tumors, and compared to low expression of CARD9, high expression was associated with poorer overall survival (P = 0.0365). Univariate and multivariate analyses showed that high expression of CARD9 was an independent prognostic factor. Knockdown of CARD9 in lung adenocarcinoma cells inhibited proliferation but did not increase apoptosis. In addition, CARD9 activated the NF-κB pathway in a lung adenocarcinoma cell line.

Conclusion

CARD9 was shown to be an independent prognostic factor of poor outcome for lung cancer and may represent a molecular target for treatment.