Comprehensive analysis of prognostic biomarkers in lung adenocarcinoma based on aberrant lncRNA-miRNA-mRNA networks and Cox regression models

LINC00115 regulates lung adenocarcinoma progression via sponging miR-154-3p to modulate Sp3 expression

The most commonly diagnosed and most lethal subtype of lung cancer is lung adenocarcinoma (LUAD). Therefore, more detailed understanding of the potential mechanism and identification of potential targets of lung adenocarcinoma is needed. A growing number of reports reveals that long non-coding RNAs (lncRNAs) play crucial roles in cancer progression. In present study, we found that lncRNA LINC00115 was upregulated in LUAD tissues and cells. Functional studies revealed that LINC00115 knockdown inhibits the proliferation, growth, invasion, and migration of LUAD cells. Mechanically, we indicated that miR-154-3p is target microRNA of LINC00115, and the effect of downregulated LINC00115 on LUAD cells was partially reversed by the miR-154-3p antisense oligonucleotide (ASO-miR-154-3p). Further investigation revealed that Specificity protein 3 (Sp3) directly interacted with miR-154-3p, and the Sp3 level was positively correlated with the LINC00115 expression. Rescue experiments further showed that Sp3 overexpression partially restored the effect of downregulated LINC00115 on LUAD cells. Similarly, in vivo experiments confirmed that downregulated LINC00115 inhibited xenograft growth and Sp3 expression. Our results demonstrated that LINC00115 knockdown inhibited LUAD progression via sponging miR-154-3p to modulate Sp3 expression. These data indicate that the LINC00115/miR-154-3p/Sp3 axis can be a potential therapeutic target of LUAD.

Secreted proteins MDK, WFDC2, and CXCL14 as candidate biomarkers for early diagnosis of lung adenocarcinoma

BACKGROUND

Early diagnosis of lung adenocarcinoma (LUAD), one of the most common types of lung cancer, is very important to improve the prognosis of patients. The current methods can't meet the requirements of early diagnosis. There is a pressing need to identify novel diagnostic biomarkers. Secretory proteins are the richest source for biomarker research. This study aimed to identify candidate secretory protein biomarkers for early diagnosis of LUAD by integrated bioinformatics analysis and clinical validation.

METHODS

Differentially expressed genes (DEGs) of GSE31210, gene expression data of early stage of LUAD, were analyzed by GEO2R. Upregulated DEGs predicted to encode secreted proteins were obtained by taking the intersection of the DEGs list with the list of genes encoding secreted proteins predicted by the majority decision-based method (MDSEC). The expressions of the identified secreted proteins in the lung tissues of early-stage LUAD patients were further compared with the healthy control group in mRNA and protein levels by using the UALCAN database (TCGA and CPTAC). The selected proteins expressed in plasma were further validated by using Luminex technology. The diagnostic value of the screened proteins was evaluated by receiver operating characteristic (ROC) analysis. Cell counting kit-8 assay was carried out to investigate the proliferative effects of these screened proteins.

RESULTS

A total of 2183 DEGs, including 1240 downregulated genes and 943 upregulated genes, were identified in the GSE31210. Of the upregulated genes, 199 genes were predicted to encode secreted proteins. After analysis using the UALCAN database, 16 molecules were selected for further clinical validation. Plasma concentrations of three proteins, Midkine (MDK), WAP four-disulfide core domain 2 (WFDC2), and C-X-C motif chemokine ligand 14 (CXCL14), were significantly higher in LUAD patients than in healthy donors. The area under the curve values was 0.944, 0.881, and 0.809 for MDK, WFDC2, and CXCL14, 0.962 when combined them. Overexpression of the three proteins enhanced the proliferation activity of A549 cells.

CONCLUSIONS

MDK, WFDC2, and CXCL14 were identified as candidate diagnostic biomarkers for early-stage LUAD and might also play vital roles in tumorigenesis.

Identification of condition-specific regulatory mechanisms in normal and cancerous human lung tissue

Background

Lung cancer is the leading cause of cancer death in both men and women. The most common lung cancer subtype is non-small cell lung carcinoma (NSCLC) comprising about 85% of all cases. NSCLC can be further divided into three subtypes: adenocarcinoma (LUAD), squamous cell carcinoma (LUSC), and large cell lung carcinoma. Specific genetic mutations and epigenetic aberrations play an important role in the developmental transition to a specific tumor subtype. The elucidation of normal lung versus lung tumor gene expression patterns and regulatory targets yields biomarker systems that discriminate lung phenotypes (i.e., biomarkers) and provide a foundation for the discovery of normal and aberrant gene regulatory mechanisms.

Results

We built condition-specific gene co-expression networks (csGCNs) for normal lung, LUAD, and LUSC conditions. Then, we integrated normal lung tissue-specific gene regulatory networks (tsGRNs) to elucidate control-target biomarker systems for normal and cancerous lung tissue. We characterized co-expressed gene edges, possibly under common regulatory control, for relevance in lung cancer.

Conclusions

Our approach demonstrates the ability to elucidate csGCN:tsGRN merged biomarker systems based on gene expression correlation and regulation. The biomarker systems we describe can be used to classify and further describe lung specimens. Our approach is generalizable and can be used to discover and interpret complex gene expression patterns for any condition or species.

Supplementary Information

The online version contains available at 10.1186/s12864-022-08591-9.

Identification of Prognostic Markers of N6-Methylandenosine-Related Noncoding RNAs in Non-Small-Cell Lung Cancer

Background

Non-small-cell lung cancer (NSCLC) is a major type of lung carcinoma that threatens the health and life of humans worldwide. We aimed to establish an n6-methyladenosine (m6A)-relevant ncRNA model to effectively evaluate the outcome of patients.

Methods

m6A-Related ncRNAs (lncRNA/miRNA) were acquired from the UCSC Xena database. Pearson's correlation analysis among 21 m6A regulatory factors and ncRNAs were implemented to explore m6A-relevant ncRNAs. Weighted gene co-expression network analysis (WGCNA) identified hub modules of gene associated with prognosis of NSCLC patients. Univariate Cox regression analysis identified 80 m6A-related ncRNAs. Least absolute shrinkage and selector operation (LASSO) filtered out redundant factors and established a risk score model (m6A-NSCLC) in the TCGA training data set. Validation of prognostic ability was performed using testing data sets from the TCGA database. We also conducted a correlation analysis among the risk score and different clinical traits. Both univariate and multivariate Cox analyses were combined to verify prognostic factors which have independent value, and a nomogram on the basis of m6A-NSCLC risk scores and clinical traits was constructed to assess the prognosis of patients. In addition, we screened differentially expressed genes (DEGs) based on different risk scores and performed enrichment analysis. Finally, 21 m6A regulators were detected to be differentially expressed between two risk groups.

Results

An m6A-NSCLC risk model with 18 ncRNAs was constructed. By comparison with low-risk patients, high-risk score patients had poor prognosis. The distribution of risk score in the tumor size and extent (T), number of near lymph nodes (N), clinical stage, sex, and tumor types was significantly different. The risk score could act as an independent prognostic factor with the nomogram assessing overall survival in NSCLC. DEGs inherent to cell movement and immune regulation were involved in NSCLC development. Furthermore, 18 of 21 m6A regulators were differentially expressed, implying their correlation to survival prognosis.

Conclusion

The m6A-NSCLC could be effectively utilized for evaluation of prognosis of patients.

Systematic Analysis of the Expression Profile and Prognostic Significance of the IGF2BP Family in Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD)，the most common type of lung cancer associated with poor prognosis, has become a major health problem for human health. IGF2BPs are types of N6-methyladenosine reader proteins, comprising IGF2BP1, IGF2BP2, and IGF2BP3, that promote LUAD progression. However, the expression profiles and prognostic value of IGF2BPs in LUAD remain unclear.

OBJECTIVE

To analyze the expression profiles and prognostic significance of the IGF2BP family in lung adenocarcinoma.

METHODS

In our study, we utilized LUAD patient and normal or para-carcinoma tissue data from TCGA database and the GTEx project. Using survival analysis, Kaplan-Meier curves, and Cox proportional hazards model, we analyzed the expression profiles and prognostic significance of the IGF2BP family.

RESULTS

Patients with high expression levels of IGF2BPs showed significant association with poor overall survival (p < 0.05). Moreover, the somatic mutation rates of IGF2BP1, IGF2BP2, IGF2BP3 were determined as 2.65, 1.59, and 1.76%, respectively, by investigating the genetic mutation. In addition, there were significant associations between TMB and IGF2BP family expression profiles, which positively correlated with the expression of PD-1 (p < 0.05). Cox proportional hazard model for LUAD showed the risk score for IGF2BP1, p-TNM stage, and so forth, all independent prognostic indicators for LUAD patients. Finally, the co-expression genes were obtained to build a PPI network and analyze the hub genes of the IGF2BP family.

CONCLUSION

Our study provides further insights into the role of the IGF2BP family in LUAD and identifies 10 genes that may be associated with IGF2BPs in LUAD patients.

Identification of Two Immune Related Genes Correlated With Aberrant Methylations as Prognosis Signatures for Renal Clear Cell Carcinoma

Almost 75% of renal cancers are renal clear cell carcinomas (KIRC). Accumulative evidence indicates that epigenetic dysregulations are closely related to the development of KIRC. Cancer immunotherapy is an effective treatment for cancers. The aim of this study was to identify immune-related differentially expressed genes (IR-DEGs) associated with aberrant methylations and construct a risk assessment model using these IR-DEGs to predict the prognosis of KIRC. Two IR-DEGs (SLC11A1 and TNFSF14) were identified by differential expression, correlation analysis, and Cox regression analysis, and risk assessment models were established. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.6907. In addition, we found that risk scores were significantly associated with 31 immune cells and factors. Our present study not only shows that two IR-DEGs can be used as prognosis signatures for KIRC, but also provides a strategy for the screening of suitable prognosis signatures associated with aberrant methylation in other cancers.

An 11-Gene Signature Risk-Prediction Model Based on Prognosis-Related miRNAs and Their Target Genes in Lung Adenocarcinoma

Aberrant expression of microRNAs may affect tumorigenesis and progression by regulating their target genes. This study aimed to construct a risk model for predicting the prognosis of patients with lung adenocarcinoma (LUAD) based on differentially expressed microRNA-regulated target genes. The miRNA sequencing data, RNA sequencing data, and patients’ LUAD clinical data were downloaded from the The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs and genes were screened out by combining differential analysis with LASSO regression analysis to further screen out miRNAs associated with patients’ prognosis, and target gene prediction was performed for these miRNAs using a target gene database. Overlapping gene screening was performed for target genes and differentially expressed genes. LASSO regression analysis and survival analysis were then used to identify key genes. Risk score equations for prognostic models were established using multifactorial COX regression analysis to construct survival prognostic models, and the accuracy of the models was evaluated using subject working characteristic curves. The groups were divided into high- and low-risk groups according to the median risk score, and the correlation with the clinicopathological characteristics of the patients was observed. A total of 123 up-regulated miRNAs and 22 down-regulated miRNAs were obtained in this study. Five prognosis-related miRNAs were screened using LASSO regression analysis and Kaplan-Meier method validation, and their target genes were screened with the overlap of differentially expressed genes before multifactorial COX analysis finally resulted in an 11-gene risk model for predicting patient prognosis. The area under the ROC curve proved that the model has high accuracy. The 11-gene risk-prediction model constructed in this study may be an effective predictor of prognosis.

Analysis and Construction of a Competitive Endogenous RNA Regulatory Network of Baicalin-Induced Apoptosis in Human Osteosarcoma Cells

Background

Baicalin is an extract from the traditional Chinese herb Scutellaria baicalensis and has the potential to treat osteosarcoma (OS). However, the transcriptome-level mechanism of baicalin-mediated antitumor effects in OS has not yet been investigated. The aim of this study was to analyze the competitive endogenous RNA (ceRNA) regulatory network involved in baicalin-induced apoptosis of OS cells.

Methods

In this study, CCK-8 and flow cytometry assays were used to detect the antitumor effects of baicalin on human OS MG63 cells. Furthermore, transcriptome sequencing was employed to establish the long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA profiles.

Results

Baicalin inhibited MG63 cell proliferation and induced apoptosis. Totals of 58 lncRNAs, 31 miRNAs, and 2136 mRNAs in the baicalin-treated MG63 cells were identified as differentially expressed RNAs compared to those in control cells. Of these, 2 lncRNAs, 3 miRNAs, and 18 mRNAs were included in the ceRNA regulatory network. The differentially expressed RNAs were confirmed by quantitative real-time PCR (qRT-PCR).

Conclusions

By identifying the ceRNA network, our results provide new information about the possible molecular basis of baicalin, which has potential applications in OS treatment.

Gene expression and DNA methylation analyses suggest that two immune related genes are prognostic factors of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the second most prevalent cancer, as it accounts for approximately 10% of all annually diagnosed cancers. Studies have indicated that DNA methylation is involved in cancer genesis. The purpose of this study was to investigate the relationships among DNA methylation, gene expression and the tumor-immune microenvironment of CRC, and finally, to identify potential key genes related to immune cell infiltration in CRC.

METHODS

In the present study, we used the ChAMP and DESeq2 packages, correlation analyses, and Cox regression analyses to identify immune-related differentially expressed genes (IR-DEGs) that were correlated with aberrant methylation and to construct a risk assessment model.

RESULTS

Finally, we found that HSPA1A expression and CCRL2 expression were positively and negatively associated with the risk score of CRC, respectively. Patients in the high-risk group were more positively correlated with some types of tumor-infiltrating immune cells, whereas they were negatively correlated with other tumor-infiltrating immune cells. After the patients were regrouped according to the median risk score, we could more effectively distinguish them based on survival outcome, clinicopathological characteristics, specific tumor-immune infiltration status and highly expressed immune-related biomarkers.

CONCLUSION

This study suggested that the risk assessment model constructed by pairing immune-related differentially expressed genes correlated with aberrant DNA methylation could predict the outcome of CRC patients and might help to identify those patients who could benefit from antitumor immunotherapy.

A Seven-Long Non-coding RNA Signature Improves Prognosis Prediction of Lung Adenocarcinoma: An Integrated Competing Endogenous RNA Network Analysis

Early and precise prediction is an important way to reduce the poor prognosis of lung adenocarcinoma (LUAD) patients. Nevertheless, the widely used tumor, node, and metastasis (TNM) staging system based on anatomical information only often could not achieve adequate performance on foreseeing the prognosis of LUAD patients. This study thus aimed to examine whether the long non-coding RNAs (lncRNAs), known highly involved in the tumorigenesis of LUAD through the competing endogenous RNAs (ceRNAs) mechanism, could provide additional information to improve prognosis prediction of LUAD patients. To prove the hypothesis, a dataset consisting of both RNA sequencing data and clinical pathological data, obtained from The Cancer Genome Atlas (TCGA) database, was analyzed. Then, differentially expressed RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were identified and a lncRNA-miRNA-mRNA ceRNA network was constructed based on those differentially expressed RNAs. Functional enrichment analysis revealed that this ceRNA network was highly enriched in some cancer-associated signaling pathways. Next, lasso-Cox model was run 1,000 times to recognize the potential survival-related combinations of the candidate lncRNAs in the ceRNA network, followed by the "best subset selection" to further optimize these lncRNA-based combinations, and a seven-lncRNA prognostic signature with the best performance was determined. Based on the median risk score, LUAD patients could be well distinguished into high-/low-risk subgroups. The Kaplan-Meier survival curve showed that LUAD patients in the high-risk group had significantly shorter overall survival than those in the low-risk group (log-rank test P = 4.52 × 10^-9). The ROC curve indicated that the clinical genomic model including both the TNM staging system and the signature had a superior performance in predicting the patients' overall survival compared to the clinical model with the TNM staging system only. Further stratification analysis suggested that the signature could work well in the different strata of the stage, gender, or age, rendering it to be a wide application. Finally, a ceRNA subnetwork related to the signature was extracted, demonstrating its high involvement in the tumorigenesis mechanism of LUAD. In conclusion, the present study established a lncRNA-based molecular signature, which can significantly improve prognosis prediction for LUAD patients.

Research progress on long non-coding RNAs and their roles as potential biomarkers for diagnosis and prognosis in pancreatic cancer

Pancreatic cancer is one of the main causes of tumor-related deaths worldwide because of its low morbidity but extremely high mortality, and is therefore colloquially known as the "king of cancer." Sudden onset and lack of early diagnostic biomarkers directly contribute to the extremely high mortality rate of pancreatic cancer patients, and also make it indistinguishable from benign pancreatic diseases and precancerous pancreatic lesions. Additionally, the lack of effective prognostic biomarkers makes it difficult for clinicians to formulate precise follow-up strategies based on the postoperative characteristics of the patients, which results in missed early diagnosis of recurrent pancreatic cancer. Long non-coding RNAs (lncRNAs) can influence cell proliferation, invasion/migration, apoptosis, and even chemoresistance via regulation of various signaling pathways, leading to pro- or anti-cancer outcomes. Given the versatile effects of lncRNAs on tumor progression, using a single lncRNA or combination of several lncRNAs may be an effective method for tumor diagnosis and prognostic predictions. This review will give a comprehensive overview of the most recent research related to lncRNAs in pancreatic cancer progression, as targeted therapies, and as biomarkers for the diagnosis and prognosis of pancreatic cancer.

Licochalcone A reverses NNK-induced ectopic miRNA expression to elicit in vitro and in vivo chemopreventive effects

BACKGROUND

Chemoprevention is the best cost-effective way regarding cancers. MicroRNAs (miRNAs) have been reported to be differentially expressed during the development of lung cancer. However, if lung cancer prevention can be achieved through modulating miRNAs expression so far remains unknown.

PURPOSE

To discover ectopically expressed miRNAs in NNK-induced lung cancer and clarify whether Licochalcone A (lico A) can prevent NNK-induced lung cancer by modulating miRNA expression.

STUDY DESIGN AND METHODS

A/J mice were used to construct a lung cancer model by intraperitoneal injection with physiological saline NNK (100 mg/kg). Chemopreventive effects of lico A against lung cancer at 2 mg/kg and 20 mg/kg doses were evaluated in vivo. MicroRNA array and RT-qPCR were used to assess the expression levels of miRNAs. MLE-12 cells were treated with 0.1 mg/ml NNK, stimulating the ectopic expression pattern of miR-144-3p, miR-20a-5p, miR-29c-3p, let-7d-3p, and miR-328-3p. miR-144-3p mimics and inhibitors were used to manipulate miR-144-3p levels. The effects of lico A (10 μM) on cell cycle distribution, apoptosis, and the expression of CK19, RASA1, miR-144-3p, miR-20a-5p, miR-29c-3p, let-7d-3p, and miR-328-3p in NNK-treated MLE-12 cells were studied.

RESULTS

The expression levels of miR-144-3p, miR-20a-5p, and miR-29c-3p increased, while those of let-7d-3p and miR-328-3p decreased in both NNK-induced A/J mice and MLE-12 cells. Lico A could reverse the NNK-induced ectopic miRNA (miR-144-3p, miR-20a-5p, miR-29c-3p, let-7d-3p, and miR-328-3p) expression both in vivo and in vitro and elicit in vivo lung cancer chemopreventive effect against NNK. In MLE-12 cells, the overexpression of miR-144-3p elicited the same effect as NNK regarding the expression of lung cancer biomarker CK19; the silencing of miR-144-3p reversed the effect of NNK on cell cycle distribution and apoptosis. Lico A could reverse the effect of NNK on the expression of miR-144-3p, CK19, and RASA1 (predicted target of miR-144-3p).

CONCLUSION

The present study suggests that miR-144-3p, miR-20a-5p, miR-29c-3p, let-7d-3p, and miR-328-3p were involved in the in vivo pathogenesis of NNK-induced lung cancer, and lico A could reverse the effect of NNK both in vivo and in vitro to elicit lung cancer chemopreventive effects through, at least partially, these five ectopically expressed miRNAs, especially miR-144-3p.

Identification of HMG-box family establishes the significance of SOX6 in the malignant progression of glioblastoma

Glioblastoma multiforme (GBM) is the most malignant neuroepithelial primary brain tumor and its mean survival time is 15 months after diagnosis. This study undertook to investigate the genome-wide and transcriptome-wide analyses of human high mobility group box (HMG-box) TF (transcript factor) families / HOX, TOX, FOX, HMG and SOX gene families, and their relationships to GBM. According to the TCGA-GBM profile analysis, differentially expressed HOX, FOX, HMG and SOX gene families (62 DEmRNA) were found in this study. We also analyzed DEmRNA (HMG-box related genes) co-expressed eight DElncRNA in GBM, and constructed a ceRNA network analysis as well. We constructed 50 DElncRNA-DEmiRNA-DEmRNA (HMG-box related genes) pairs between GBM and normal tissues. Then, risk genes SOX6 and SOX21 expression were correlated with immune infiltration levels in GBM. SOX6 also had a strong association with MAPT, GSK3B, FYN and DPYSL4, suggesting that they might be functional members in GBM.

LINC00514 drives osteosarcoma progression through sponging microRNA-708 and consequently increases URGCP expression

Long intergenic nonprotein-coding RNA 00514 (LINC00514) is upregulated in papillary thyroid cancer and contributes to its aggressiveness. In this study, we thoroughly explored the expression profile, specific functions, and relevant molecular mechanism of LINC00514 in osteosarcoma (OS). Herein, LINC00514 was significantly upregulated in OS tissues and cells, and increased LINC00514 expression was closely correlated with tumor size, TNM stage, and distant metastasis. OS patients with high LINC00514 expression had shorter overall survival than those with low LINC00514 expression. LINC00514 interference inhibited OS cell proliferation, colony formation, migration, and invasion in vitro but promoted cell apoptosis and G0/G1 cell cycle arrest. LINC00514 downregulation hindered OS tumor growth in vivo. Mechanistically, LINC00514 functioned as a competing endogenous RNA by directly interacting with microRNA-708-5p (miR-708) and consequently increasing the expression of upregulator of cell proliferation (URGCP). Both miR-708 knockdown and URGCP restoration partially neutralized anticancer activities of LINC00514 silencing in OS cells. LINC00514 increases URGCP expression by acting as a competing endogenous RNA for miR-708, thus exerting oncogenic roles in OS progression. In conclusion, the LINC00514/miR-708/URGCP pathway may be a promising target for drug discovery in the future.