Establishment and validation of a 7-microRNA prognostic signature for non-small cell lung cancer

miRNA-Seq Tissue Diagnostic Signature: A Novel Model for NSCLC Subtyping

Non-small cell lung cancer (NSCLC) encompasses distinct histopathological subtypes, namely adenocarcinoma (AC) and squamous cell lung carcinoma (SCC), which require precise differentiation for effective treatment strategies. In this study, we present a novel molecular diagnostic model that integrates tissue-specific expression profiles of microRNAs (miRNAs) obtained through next-generation sequencing (NGS) to discriminate between AC and SCC subtypes of NSCLC. This approach offers a more comprehensive and precise molecular characterization compared to conventional methods such as histopathology or immunohistochemistry. Firstly, we identified 31 miRNAs with significant differential expression between AC and SCC cases. Subsequently, we constructed a 17-miRNA signature through rigorous multistep analyses, including LASSO/elastic net regression. The signature includes both upregulated miRNAs (hsa-miR-326, hsa-miR-450a-5p, hsa-miR-1287-5p, hsa-miR-556-5p, hsa-miR-542-3p, hsa-miR-30b-5p, hsa-miR-4728-3p, hsa-miR-450a-1-3p, hsa-miR-375, hsa-miR-147b, hsa-miR-7705, and hsa-miR-653-3p) and downregulated miRNAs (hsa-miR-944, hsa-miR-205-5p, hsa-miR-205-3p, hsa-miR-149-5p, and hsa-miR-6510-3p). To assess the discriminative capability of the 17-miRNA signature, we performed receiver operating characteristic (ROC) curve analysis, which demonstrated an impressive area under the curve (AUC) value of 0.994. Our findings highlight the exceptional diagnostic performance of the miRNA signature as a stratifying biomarker for distinguishing between AC and SCC subtypes in lung cancer. The developed molecular diagnostic model holds promise for providing a more accurate and comprehensive molecular characterization of NSCLC, thereby guiding personalized treatment decisions and improving clinical management and prognosis for patients.

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC).

Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model.

Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways.

Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.

Identification of Vitamin D-related gene signature to predict colorectal cancer prognosis

Colorectal cancer (CRC) is one of the most common malignant carcinomas worldwide with poor prognosis, imposing an increasingly heavy burden on patients. Previous experiments and epidemiological studies have shown that vitamin D and vitamin D-related genes play a vital role in CRC. Therefore, we aimed to construct a vitamin D-related gene signature to predict prognosis in CRC. The CRC data from The Cancer Genome Atlas (TCGA) was performed as the training set. A total of 173 vitamin D-related genes in the TCGA CRC dataset were screened, and 17 genes associated with CRC prognosis were identified from them. Then, a vitamin D-related gene signature consisting of those 17 genes was established by univariate and multivariate Cox analyses. Moreover, four external datasets (GSE17536, GSE103479, GSE39582, and GSE17537) were used as testing set to validate the stability of this signature. The high-risk group presented a significantly poorer overall survival than low-risk group in both of training set and testing sets. Besides, the areas under the curve (AUCs) for signature on OS in training set at 1, 3, and 5 years were 0.710, 0.708, 0.710 respectively. The AUCs of the ROC curve in GSE17536 for 1, 3, and 5 years were 0.649, 0.654, and 0.694. These results indicated the vitamin D-related gene signature model could effectively predict the survival status of CRC patients. This vitamin D-related gene signature was also correlated with TNM stage in CRC clinical parameters, and the higher risk score from this model was companied with higher clinical stage. Furthermore, the high accuracy of this prognostic signature was validated and confirmed by nomogram model. In conclusion, we have proposed a novel vitamin D-related gene model to predict the prognosis of CRC, which will help provide new therapeutic targets and act as potential prognostic biomarkers for CRC.

Hypoxia-associated alternative splicing signature in lung adenocarcinoma

Aim: To establish a signature based on hypoxia-related alternative splicing (AS) events for lung adenocarcinoma. Materials & methods: The least absolute shrinkage and selection operator Cox approach was used to construct a prognostic model. A nomogram that integrates the final AS predictor and stage was created. The network of the key AS events and splicing factors was created. Results: We created a prognostic signature of 11 AS events. Moreover, a nomogram that constitutes the pathological stage and risk was exhibited to be greatly effective in estimating the survival likelihood of lung adenocarcinoma patients. Conclusion: Herein we developed the first-ever signature based on hypoxia-related AS events with both prognostic predictive power and diagnostic efficacy.

Development of a serum miRNA panel for detection of early stage non-small cell lung cancer

Minimally invasive testing for early detection of lung cancer to improve patient survival is a major unmet clinical need. This study aimed to develop and validate a serum multi-microRNA (multimiR) panel as a minimally invasive test for early detection of nonsmall cell lung cancer (NSCLC) regardless of smoking status, gender, and ethnicity. Our study included 744 NSCLC cases and 944 matched controls, including smokers and nonsmokers, male and female, with Asian and Caucasian subjects. Using RT-qPCR and a tightly controlled workflow, we quantified the absolute expression of 520 circulating microRNAs (miRNAs) in a Chinese cohort of 180 early stage NSCLC cases and 216 healthy controls (male smokers). Candidate biomarkers were verified in two case-control cohorts of 432 Chinese and 218 Caucasians, respectively (including females and nonsmokers). A multimiR panel for NSCLC detection was developed using a twofold cross-validation and validated in three additional Asian cohorts comprising 642 subjects. We discovered 35 candidate miRNA biomarkers, verified 22 of them, and developed a five-miR panel that detected NSCLC with area under curve (AUC) of 0.936-0.984 in the discovery and verification cohorts. The panel was validated in three independent cohorts with AUCs of 0.973, 0.916, and 0.917. The sensitivity of five-miR test was 81.3% for all stages, 82.9% for stages I and II, and 83.0% for stage I NSCLC, when the specificity is at 90.7%. We developed a minimally invasive five-miR serum test for detecting early stage NSCLC and validated its performance in multiple patient cohorts independent of smoking status, gender, and ethnicity.

MiR-200b-3p Functions as an Oncogene by Targeting ABCA1 in Lung Adenocarcinoma

Objective:

The aim of this study was to investigate the microRNA-200b-3p expression in lung adenocarcinoma and the possible functional associations of microRNA-200b-3p with cell proliferation, migration, and invasion.

Methods:

Quantitative real-time polymerase chain reaction was used to detect the expression of microRNA-200b-3p in lung adenocarcinoma samples and in the human lung adenocarcinoma cell lines A549 and H1299. A549 and H1299 cells were transfected with either a microRNA-200b-3p mimic or a negative control microRNA or either an empty vector or an adenosine triphosphate-binding cassette transporter A-1 overexpression vector. A Cell Counting Kit-8 assay was employed to assess the ability of cell proliferation. Transwell assays and transwell-Matrigel invasion assay were, respectively, utilized to assess the capacity of migration and invasion in A549 and H1299 cells.

Results:

The results showed that microRNA-200b-3p expression was significantly upregulated in tumor tissues compared with that in adjacent normal tissues. Overexpression of microRNA-200b-3p promoted lung adenocarcinoma cell proliferation and metastasis. Furthermore, adenosine triphosphate-binding cassette transporter A-1 was a direct target of microRNA-200b-3p, and this binding was verified by luciferase reporter analysis. Overexpression of adenosine triphosphate-binding cassette transporter A-1 obviously suppressed lung adenocarcinoma cell proliferation, migration, and invasion. Lung adenocarcinoma cell phenotypes induced by microRNA-200b-3p overexpression could be partially remitted by the co-overexpression of microRNA-200b-3p and adenosine triphosphate-binding cassette transporter A-1.

Conclusion:

This study first identified that microRNA-200b-3p is upregulated in lung adenocarcinoma cells and associated with cell proliferation and metastasis. MicroRNA-200b-3p promoted lung adenocarcinoma cell proliferation and metastasis by suppressing adenosine triphosphate-binding cassette transporter A-1. MicroRNA-200b-3p may function as a novel molecular marker and therapeutic target for lung adenocarcinoma treatment.

An Immune-Related Signature Predicts Survival in Patients With Lung Adenocarcinoma

We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (PSMD11, PPIA, MIF, BMP5, DKK1, PDGFB, ANGPTL4, IL1R2, THRB, LTBR, TNFRSF1, TNFRSF17, IL20RB, and MC1R) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine–cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.

Identification of RAD54 homolog B as a promising therapeutic target for breast cancer

Breast cancer is a recognized threat to the health of women globally. Due to the lack of the knowledge about the molecular pathogenesis of breast cancer, therapeutic strategies remain inadequate, especially for aggressive breast cancer. In the present study, sequential bioinformatics analysis was performed using data from the GSE20711 dataset, and the results demonstrated that three genes may impact the survival of patients with breast cancer. One of these genes, RAD54 homolog B (RAD54B), may be a potential prognostic factor for breast cancer. A signature was established that could evaluate the overall survival for patients with breast cancer based on the risk score calculated from RAD54B expression and the Tumor-Node-Metastasis (TNM) stage [risk score=expRAD54B × 0.236 + TNM stage (I/II=0 or III/IV=1) ×1.025]. In addition, based on the GSE85871 dataset and inhibitory assay, the study identified a natural compound, Japonicone A, which may reduce the proliferation of breast cancer cells by inhibiting the expression of RAD54B. Overall, the present study identified a novel candidate gene and a candidate compound as promising therapeutic targets for the treatment of breast cancer.

Plasma miR-32 levels in non-small cell lung cancer patients receiving platinum-based chemotherapy can predict the effectiveness and prognosis of chemotherapy

BACKGROUND

Previous studies have shown that microRNA-32 (miRNA-32) is an exosome microRNA that affects the proliferation and metastasis of non-small cell lung cancer (NSCLC) cells. In this study, our goal was to assess the expression of plasma microRNA-32 and its potential as a biomarker to predict the tumor response and survival of patients with NSCLC undergoing platinum-based chemotherapy.

METHODS

Plasma microRNA-32 levels before and after 1 cycle of platinum-based chemotherapy in 43 patients with NSCLC were measured using a quantitative real-time polymerase chain reaction assay (qPCR). In addition, the demographic and survival data of the patients were collected for analysis.

RESULTS

A significant correlation was observed between the changes in microRNA-32 levels before and after 1 chemotherapy cycle and the treatment response (P = .035). In addition, Kaplan-Meier analysis showed that the level of microRNA-32 after 1 chemotherapy cycle was significantly correlated with the prognosis of patients. The median progression-free survival (P = .025) and overall survival (P = .015) of patients with high microRNA-32 levels (≥7.73) after 1 chemotherapy cycle was 9 and 21 months, respectively. In contrast, the median survival of patients with low microRNA-32 levels (<7.73) was 5 and 10 months, respectively.

CONCLUSIONS

The plasma levels of microRNA-32 correlated with the efficacy of platinum-based chemotherapy and survival, indicating that microRNA-32 may be useful for predicting the effectiveness of platinum-based chemotherapy and prognosis in NSCLC.

Development of a miRNA-seq based prognostic signature in lung adenocarcinoma

Background

We utilized miRNAs expression and clinical data to develop a prognostic signature for patients with lung adenocarcinoma, with respect to their overall survival, to identify high-risk subjects based on their miRNA genomic profile.

Methods

MiRNA expressions based on miRNA sequencing and clinical data of lung adenocarcinoma patients (n = 479) from the Cancer Genome Atlas were randomly partitioned into non-overlapping Model (n = 320) and Test (n = 159) sets, respectively, for model estimation and validation.

Results

Among the ten miRNAs identified using the univariate Cox analysis, six from miR-8, miR-181, miR-326, miR-375, miR-99a, and miR-10, families showed improvement of the overall survival chance, while two miRNAs from miR-582 and miR-584 families showed a worsening of survival chances. The final prognostic signature was developed with five miRNAs—miR-375, miR-582-3p, miR-326, miR-181c-5p, and miR-99a-5p—utilizing a stepwise variable selection procedure. Using the KEGG pathway analysis, we found potential evidence supporting their significance in multiple cancer pathways, including non-small cell lung cancer. We defined two risk groups with a score calculated using the Cox regression coefficients. The five-year survival rates for the low-risk group was approximately 48.76% (95% CI = (36.15, 63.93)); however, it was as low as 7.50% (95% CI = (2.34, 24.01)) for the high-risk group. Furthermore, we demonstrated the effect of the genomic profile using the miRNA signature, quantifying survival rates for hypothetical subjects in different pathological stages of cancer.

Conclusions

The proposed prognostic signature can be used as a reliable tool for identifying high-risk subjects regarding survival based on their miRNA genomic profile.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-5206-8) contains supplementary material, which is available to authorized users.