Screening key lncRNAs for human lung adenocarcinoma based on machine learning and weighted gene co-expression network analysis

LncRNA LINC01703 promotes the proliferation, migration, and invasion of colorectal cancer by activating PI3K/AKT pathway

The role of LINC01703 in cancers, especially in colorectal cancer (CRC), is still largely unclear. Bioinformatics prediction, real-time quantitative polymerase chain reaction (RT-qPCR), 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide (MTT) assay, colony formation assay, Transwell assays, in vivo animal experiments, IF, luciferase reporter assay, and Western blot were carried out for the exploration of the potential involvement and underlying molecular mechanisms of LINC01703 in CRC cells. The results showed that LINC01703 appeared upregulated in CRC and was linked to poor prognosis. LINC01703 acted as an oncogene in both in vitro and in vivo CRC cell environments. LINC01703 activated the PI3K/AKT signaling pathway by mediating the miR-205-5p/E2F1 axis in CRC. In summary, LINC01703 possesses an oncogenic function and can be a possible biomarker or target to treat CRC.

AI/ML advances in non-small cell lung cancer biomarker discovery

Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.

Epigenetic regulation of LINC01270 in breast cancer progression by mediating LAMA2 promoter methylation and MAPK signaling pathway

Application of long non-coding RNAs (lncRNAs) for modulation of breast cancer (BC) has attracted much attention. Here, we probed into the role and underlying mechanism of long intergenic non-coding RNA 01270 (LINC01270) in BC. With the help of bioinformatics tools, we identified laminin subunit alpha 2 (LAMA2) as a BC-related differentially expressed gene to discern the effect of LAMA2 in BC cells. LAMA2 was initially poorly expressed while LINC01270 was highly expressed in BC. BC cells were subsequently treated with sh-LINC01270 or/and sh-LAMA2 for exploration of their regulatory mechanism in BC, which unfolded that LINC01270 inhibition up-regulated LAMA2 and inactivated the MAPK signaling pathway to suppress malignant characteristics of BC cells. Functional assays demonstrated that LINC01270 bound to DNMT1, DNMT3a, and DNMT3b promoted the methylation of CpG islands in LAMA2 promoter and inhibited the LAMA2 expression. Moreover, our data suggested that LAMA2 suppressed MAPK signaling pathway to inhibit BC cell malignant characteristics. The in vitro results were re-produced with the help of the in vivo experimentations. In conclusion, LINC01270 silencing inhibited the methylation of LAMA2 promoter to suppress the activation of MAPK signaling pathway, which subsequently restrained the BC progression. 1, Overexpression of LAMA2 inhibits malignant features of BC cells. 2, LINC01270 promotes LAMA2 promoter methylation by recruiting DNMTs to the LAMA2 promoter region. 3, 5-aza-dc reverses the promotion of LAMA2 promoter methylation by LINC01270. 4, LAMA2 inhibits malignant features of BC cells by suppressing the activation of MAPK signaling pathway.

Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.

Circular RNA-mediated ceRNA network was identified in human lung adenocarcinoma by high-throughput sequencing

OBJECTIVES

Aberrantly expressed circular RNAs (circRNAs) have been detected in many types of tumors. Hence, they are currently investigated as candidate biomarkers for diagnostic and potential targets for therapy in cancers. The objective of this study was to assess the expression profile of circRNA in lung adenocarcinoma (LUAD).

METHODS

This study included 14 pairs of postoperative lung adenocarcinoma specimens, including cancer tissues and matched adjacent tissues. Second-generation sequencing was applied to the specimens to determine the circRNA expression in them among the 5242 distinct circRNAs detected.

RESULTS

We identified a total of 18 significantly dysregulated circRNAs in the LUAD tissues: upregulation in four and downregulation in 14. ROC (The receiver operating characteristic curve) further suggested that hsa_circ_0120106, has_circ_0007342, has_circ_0005937, and circRNA_0000826 could potentially be used as biomarkers in the diagnosis of LUAD. Furthermore, study of the circRNA-microRNA (miRNA)-messenger RNA (mRNA) revealed interactions between the 18 dysregulated circRNA and several cancer-related miRNAs. Finally, a further Kyoto Encyclopedia of Genes and Genomes analysis showed that the cell cycle phase transition, p53 signaling pathway, AMP-activated protein kinase (AMPK) relative signaling pathway, and so on were key putative pathways in the process of LUAD.

CONCLUSIONS

These findings demonstrated the correlation between abnormality in circRNA expression and LUAD, which lays the foundation of making CircRNAs candidate biomarkers in the diagnosis of LUAD.

Cuproptosis-related LncRNAs are correlated with immunity and predict prognosis in HNSC independent of TMB

Aims: Cuproptosis is a novel cell death pathway, and the regulatory mechanism in head and neck squamous cell carcinoma (HNSC) remains to be explored. We determined whether cuproptosis-related lncRNAs (CRLs) could predict prognosis in HNSC. Methods and Results: First, we identified 10 prognostic CRLs by Pearson correlation and univariate Cox regression analyses. Next, we constructed the CRLs prognostic model based on 5 CRLs screened by the least absolute shrinkage and selection operator (LASSO) Cox analysis. Following this, we calculated the risk score for HNSC patients and divided patients into high- and low-risk groups. In our prognostic model, HNSC patients with higher risk scores had poorer outcomes. Based on several prognostic features, a predictive nomogram was established. Furthermore, we investigated principal component analysis to distinguish two groups, and functional enrichment analysis of 176 differentially expressed genes (DEGs) between risk groups was performed. Finally, we analyzed relationships between tumor mutation burden (TMB) and risk scores. Conclusion: Cuproptosis-related lncRNAs can be applied to predict HNSC prognosis independent of TMB, which is closely correlated with tumor immunity.

Construction of a ferroptosis scoring system and identification of LINC01572 as a novel ferroptosis suppressor in lung adenocarcinoma

Background: Ferroptosis is a novel process of programmed cell death driven by excessive lipid peroxidation that is associated with the development of lung adenocarcinoma. N6-methyladenosine (m6a) modification of multiple genes is involved in regulating the ferroptosis process, while the predictive value of N6-methyladenosine- and ferroptosis-associated lncRNA (FMRlncRNA) in the prognosis of patients remains with LUAD remains unknown. Methods: Unsupervised cluster algorithm was applied to generate subcluster in LUAD according to ferroptosis-associated lncRNA. Stepwise Cox analysis and LASSO algorithm were applied to develop a prognostic model. Cellular location was detected by single-cell analysis. Also, we conducted Gene set enrichment analysis (GSEA) enrichment, immune microenvironment and drug sensitivity analysis. In addition, the expression and function of the LINC01572 were investigated by several in vitro experiments including qRT-PCR, cell viability assays and ferroptosis assays. Results: A novel ferroptosis-associated lncRNAs-based molecular subtype containing two subclusters were determined in LUAD. Then, we successfully created a risk model according to five ferroptosis-associated lncRNAs (LINC00472, MBNL1-AS1, LINC01572, ZFPM2-AS1, and TMPO-AS1). Our nominated model had good stability and predictive function. The expression patterns of five ferroptosis-associated lncRNAs were confirmed by polymerase chain reaction (PCR) in LUAD cell lines. Knockdown of LINC01572 significantly inhibited cell viability and induced ferroptosis in LUAD cell lines. Conclusion: Our data provided a risk score system based on ferroptosis-associated lncRNAs with prognostic value in LUAD. Moreover, LINC01572 may serve as a novel ferroptosis suppressor in LUAD.

The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis

Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.

Relationship between M6A methylation regulator and prognosis in patients with hepatocellular carcinoma after transcatheter arterial chemoembolization

Background

Patients with mid-stage HCC (hepatocellular carcinoma) may benefit from transcatheter arterial chemoembolization (TACE). However, patient efficacy varies widely, and the detailed assessment index is unknown. The most general methylation alteration in mRNA (Messenger RNA), N6-methyladenosine (m6A), is controlled by the m6A regulator, which is associated with the emergence of tumors. To include the molecular causes of cancer, competition with ceRNA (endogenous RNA) networks is crucial. However, the exact processes they contribute to TACE HCC remain uncertain. The purpose of this study was tantamount to investigating the possible function of ceRNA networks and m6A regulators in patients with TACE HCC.

Methods

Genes Associated with m6A were discovered using the TACE GEO (Gene Expression Omnibus) dataset. An additional estimate of M6A-associated DEGs (differentially expressed genes) was used to create a predictive response model, which is required. LncRNA-miRNA and miRNA-mRNA interactions were then predicted, the regulatory ceRNA network was set up using Cytoscape software, and target genes were identified using GEPIA online analysis. The connection between immunological checkpoints, immune cell marker genes, and target genes for immune cells was also examined.

Results

The detection of 4 m6A-associated DEGs, the development and evaluation of 2 Machine learning models, and the development of risk models that accurately predicted the response rate of specific patients. Additionally, we obtained two miRNAs (micro RNAs)and six lncRNAs (Long non-coding RNAs), forming an 8-pair ceRNA network, and the target gene LRPPRC deletion of one copy number and gene expression was highly correlated with the amount of Tregs immune cells. LRPPRC was related positively with NRP1, IRF5, and ITGAM and negatively with CCR7 and CD8B among immune cell marker genes. We also discovered that LRPPRC correlates positively with immune checkpoint CD274 cells.

Conclusion

The response of HCC patients to TACE therapy may be predicted using a model based on four gene expression data. We also developed a ceRNA network for TACE HCC related to m6A, which offered suggestions for more research into its molecular processes and possible prognostic indicators.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.

Construction of a prognostic model of colon cancer patients based on metabolism-related lncRNAs

Background

Many studies have shown that metabolism-related lncRNAs may play an important role in the pathogenesis of colon cancer. In this study, a prognostic model for colon cancer patients was constructed based on metabolism-related lncRNAs.

Methods

Both transcriptome data and clinical data of colon cancer patients were downloaded from the TCGA database, and metabolism-related genes were downloaded from the GSEA database. Through differential expression analysis and Pearson correlation analysis, long non-coding RNAs (lncRNAs) related to colon cancer metabolism were obtained. CRC patients were divided into training set and verification set at the ratio of 2:1. Based on the training set, univariate Cox regression analysis was utilized to determine the prognostic differential expression of metabolic-related lncRNAs. The Optimal lncRNAs were obtain by Lasso regression analysis, and a risk model was built to predict the prognosis of CRC patients. Meanwhile, patients were divided into high-risk and low-risk groups and a survival curve was drawn accordingly to determine whether the survival rate differs between the two groups. At the same time, subgroup analysis evaluated the predictive performance of the model. We combined clinical indicators with independent prognostic significance and risk scores to construct a nomogram. C index and the calibration curve, DCA clinical decision curve and ROC curve were obtained as well. The above results were all verified using the validation set. Finally, based on the CIBERSORT analysis method, the correlation between lncRNAs and 22 tumor-infiltrated lymphocytes was explored.

Results

By difference analysis, 2491 differential lncRNAs were obtained, of which 226 were metabolic-related lncRNAs. Based on Cox regression analysis and Lasso results, a multi-factor prognostic risk prediction model with 13 lncRNAs was constructed. Survival curve results suggested that patients with high scores and have a poorer prognosis than patients with low scores (P<0.05). The area under the ROC curve (AUC) for the 3-year survival and 5-year survival were 0.768 and 0.735, respectively. Cox regression analysis showed that age, distant metastasis and risk scores can be used as independent prognostic factors. Then, a nomogram including age, distant metastasis and risk scores was built. The C index was 0.743, and the ROC curve was drawn to obtain the AUC of the 3-year survival and the 5-year survival, which were 0.802 and 0.832, respectively. The above results indicated that the nomogram has a good predictive effect. Enrichment analysis of KEGG pathway revealed that differential lncRNAs may be related to chemokines, amino acid and sugar metabolism, NOD-like receptor and Toll-like receptor activation as well as other pathways. Finally, the analysis results based on the CIBERSORT algorithm showed that the lncRNAs used to construct the model had a strong polarized correlation with B cells, CD8+T cells and M0 macrophages.

Conclusion

13 metabolic-related lncRNAs affecting the prognosis of CRC were screened by bioinformatics methods, and a prognostic risk model was constructed, laying a solid foundation for the research of metabolic-related lncRNAs in CRC.

Non-coding RNA-mediated high expression of SFXN3 as a prognostic biomarker associated with paclitaxel resistance and immunosuppressive microenvironment in head and neck cancer

Chemoresistance is the leading cause of poor prognosis in head and neck squamous cell carcinoma (HNSC); however, promising biomarkers to identify patients for stratified chemotherapy are lacking. Sideroflexin 3 (SFXN3) is an important mitochondrial serine transporter during one-carbon metabolism, which is involved in the proliferation of cancer cells. However, the specific role of SFXN3 in HNSC remains unknown. In this study, we performed expression and survival analysis for SFXN3 in pan-cancer using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) and found that SFXN3 served as a potential oncogene in HNSC. Notably, SFXN3 expression was found to be positively associated with enriched tumor-infiltrating macrophages, other immune suppressive cells, and immune checkpoint expression and resistance to paclitaxel. Gene, clinical, and immune variables included in the univariate and multivariate analyses showed that SFXN3 expression was an independent risk factor. Moreover, the LINC01270/hsa-miR-29c-3p/SFXN3 axis was identified as the most likely upstream non-coding RNA-related pathway of SFXN3 in HNSC using bioinformatic analysis, expression analysis, correlation analysis, and survival analysis. Taken together, our findings demonstrated that a non-coding RNA-mediated high expression of SFXN3 is a prognostic biomarker and is associated with the immunosuppressive microenvironment in HNSC.

Characterization and validation of a ferroptosis-related LncRNA signature as a novel prognostic model for lung adenocarcinoma in tumor microenvironment

Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.

Long non-coding RNA LINC01270 is an onco-promotor in lung adenocarcinoma by upregulating LARP1 via sponging miR-326

Accumulating evidence have proved the key role of long non-coding RNA in lung adenocarcinoma (LUAD) progression. Bioinformatics analysis is used to seek the differentially expressed lncRNA LINC01270 from TCGA database. The overexpression of LINC01270 was then verified in LUAD tumor tissues and cell lines by qRT-PCR. LINC01270 knockdown resulted in impaired cell proliferative and invasive ability via CCK-8 assay, EdU assay, colony formation assay, transwell assay, while aberrant upregulation of LINC01270 led to enhanced cell growth and invasion. Moreover, LINC01270 was found inhibiting miR-326 and thereby overexpressing the abundance of LARP1 to promote LUAD development via PI3K/AKT pathway. It was also proved that LINC01270 knockdown could suppress LUAD tumor growth in vivo. All of these findings demonstrate thatLINC01270 is a tumor promotor in LUAD via enhancing LARP1 expressed by sponging miR-326 to facilitate the development of LUAD. LINC01270 play a significant role in LUAD, which could serve as biomarkers for early diagnosis and a novel targeted remedy.

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods

Background

Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease development; therefore, a classifier based on gene expression might contribute to CAD diagnosis. This study aimed to construct genetic classification models for CAD using gene expression data, which may provide new insight into the understanding of its pathogenesis.

Methods

All statistical analysis was completed by R 3.4.4 software. Three raw gene expression datasets (GSE12288, GSE7638 and GSE66360) related to CAD were downloaded from the Gene Expression Omnibus database and included for analysis. Limma package was performed to identify differentially expressed genes (DEGs) between CAD samples and healthy controls. The WGCNA package was conducted to recognize CAD-related gene modules and hub genes, followed by recursive feature elimination analysis to select the optimal features genes (OFGs). The genetic classification models were established using support vector machine (SVM), random forest (RF) and logistic regression (LR), respectively. Further validation and receiver operating characteristic (ROC) curve analysis were conducted to evaluate the classification performance.

Results

In total, 374 DEGs, eight gene modules, 33 hub genes and 12 OFGs (HTR4, KISS1, CA12, CAMK2B, KLK2, DDC, CNGB1, DERL1, BCL6, LILRA2, HCK, MTF2) were identified. ROC curve analysis showed that the accuracy of SVM, RF and LR were 75.58%, 63.57% and 63.95% in validation; with area under the curve of 0.813 (95% confidence interval, 95% CI 0.761–0.866, P < 0.0001), 0.727 (95% CI 0.665–0.788, P < 0.0001) and 0.783 (95% CI 0.725–0.841, P < 0.0001), respectively.

Conclusions

In conclusion, this study found 12 gene signatures involved in the pathogenic mechanism of CAD. Among the CAD classifiers constructed by three machine learning methods, the SVM model has the best performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-022-02481-4.

ETV4 mediated lncRNA C2CD4D-AS1 overexpression contributes to the malignant phenotype of lung adenocarcinoma cells via miR-3681-3p/NEK2 axis

Lung adenocarcinoma (LUAD) is originated from the mucus-producing glands of the lungs. The involvement of long noncoding RNAs (lncRNAs) has been discovered in multiple diseases. In the present research, we aimed to unmask the role of C2CD4D and THEM5 antisense RNA 1 (C2CD4D-AS1) in LUAD. RT-qPCR or western blot analysis was respectively applied in the detection of RNA or protein expressions. The function of C2CD4D-AS1 in LUAD was assessed by functional assays. Through ChIP, RNA pull down, DNA pull down, RIP and luciferase reporter assays, the in-depth regulatory mechanism of C2CD4D-AS1 in LUAD was explored. C2CD4D-AS1 was dramatically overexpressed in LUAD tissues and cell lines. As a result, depletion of C2CD4D-AS1 significantly repressed cell proliferation, migration, invasion and stimulated cell apoptosis in LUAD. Mechanistically, ETS variant transcription factor 4 (ETV4) activated the transcription of C2CD4D-AS1 and stimulated its up-regulation in LUAD cells, thus affecting LUAD cell biological functions. Furthermore, C2CD4D-AS1 sponged microRNA-3681-3p (miR-3681-3p) and regulated NIMA-related kinase 2 (NEK2), thus participating in modulating LUAD cell biological behaviors. To conclude, C2CD4D-AS1 up-regulation induced by ETV4 enhanced NEK2 expression by sequestering miR-3681-3p to contribute to the malignant behaviors of LUAD cells.

Long noncoding RNA LINC01703 exacerbates the malignant properties of non-small-cell lung cancer by upregulating MACC1 in a microRNA-605-3p-mediated manner

Long intergenic nonprotein coding RNA 1703 (LINC01703) has diagnostic significancein lung adenocarcinoma. However, its specific roles in non-small-cell lung cancer(NSCLC) and downstream mechanisms have not been investigated. In the current study,we characterized the role of LINC01703 in NSCLC malignancy and elucidated itsdetailed mechanism of action. LINC01703 expression was measured by qRT-PCR. Theregulatory effects of LINC01703 on the malignancy of NSCLC cells were assessed bymultiple functional experiments. The targeted interaction was confirmed by RNAimmunoprecipitation and luciferase reporter assays. Herein, overexpression ofLINC01703 in NSCLC was indicated in the TCGA database and further proven in ourcohort. Functional studies revealed that knocking down LINC01703 repressed cellproliferation, colony formation, migration and invasion in vitro, which wasaccompanied by the induction of apoptosis. The tumor growth of LINC01703-silencedcells was also inhibited in vivo. Mechanistic analyses revealed that LINC01703functioned as a competing endogenous RNA for microRNA-605-3p (miR-605-3p) inNSCLC cells, which thereby upregulated the miR-605-3p target metastasis associatedwith colon cancer 1 (MACC1). Rescue experiments highlighted that the regulatoryactions of LINC01703 ablation on NSCLC cells were abolished in response to miR-605-3p downregulation or MACC1 overexpression. In conclusion, LINC01703enhanced the aggressiveness of NSCLC cells by altering miR-605-3p/MACC1. Ourwork suggests the therapeutic potential of LINC01703/miR-605-3p/MACC1 in NSCLC.

The Emerging Role of Thymopoietin-Antisense RNA 1 as Long Noncoding RNA in the Pathogenesis of Human Cancers

Long noncoding RNAs (lncRNAs) play essential roles in the occurrence and development of multiple human cancers. An accumulating body of researches have investigated thymopoietin antisense RNA 1 (TMPO-AS1) as a newly discovered lncRNA, which functions as an oncogenic lncRNA that is upregulated in various human malignancies and associated with poor prognosis. Many studies have detected abnormally high expression levels of TMPO-AS1 in multiple cancers, such as lung cancer, breast cancer, colorectal cancer (CRC), hepatocellular carcinoma, CRC, gastric cancer, ovarian cancer, thyroid cancer, esophageal cancer, Wilms tumor, cervical cancer, retinoblastoma, bladder cancer, osteosarcoma, and prostate cancer. TMPO-AS1 has been subsequently demonstrated to play a pivotal role in tumorigenesis and progression. The aberrantly expressed TMPO-AS1 acts as a competing endogenous RNA (ceRNA) that inhibits miRNA expression, thus activating the expression of downstream oncogenes. This study comprehensively summarizes the aberrant expressions of TMPO-AS1 as reported in the current literature and explains the relevant biological regulation mechanisms in carcinogenesis and tumor progression. Corresponding studies have indicated that TMPO-AS1 has a potential value as a promising biomarker or a target for cancer therapy.

The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma

Background

lncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs’ clinical significance, and find new targeted treatment strategies.

Methods

The corresponding data of HCC patients and autophagy genes were obtained from the TCGA database, and the human autophagy database respectively. Based on the co-expression and Cox regression analysis to construct prognostic prediction signature.

Results

Finally, a signature containing seven autophagy-related lncRNAs (PRRT3-AS1, RP11-479G22.8, RP11-73M18.8, LINC01138, CTD-2510F5.4, CTC-297N7.9, RP11-324I22.4) was constructed. Based on the risk score of signature, Overall survival (OS) curves show that the OS of high-risk patients is significantly lower than that of low-risk patients (P = 2.292e−10), and the prognostic prediction accuracy of risk score (AUC = 0.786) is significantly higher than that of ALBI (0.532), child_pugh (0.573), AFP (0.5751), and AJCC_stage (0.631). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are obviously accuracy by the combined analysis of the risk score, child_pugh, age, M_stage, and Grade (The AUC of 1- and 3-years are 0.87, and 0.855). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing.

Conclusion

In conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04123-6.

Association between lincRNA expression and overall survival for patients with triple-negative breast cancer

PURPOSE

Long intergenic non-coding RNAs (lincRNAs) are increasingly recognized as important regulators for pathogenesis and/or prognosis of breast cancer, including triple-negative breast cancer (TNBC) subtype. However, few previous studies used RNA-sequencing (RNA-Seq) technology, and none included an independent replication.

METHODS

To systematically evaluate the association between expression of lincRNAs and TNBC survival, we examined lincRNA expression profiles in TNBC tissues using RNA-Seq data for 200 TNBC patients from the Shanghai Breast Cancer Survival Study (SBCSS) and Southern Community Cohort Study (SCCS).

RESULTS

Twenty-five lincRNAs were found to be associated with overall survival (P < 0.05 and no significant heterogeneity across studies at Q statistic P > 0.1), and 61 lincRNAs were associated with disease-free survival (DFS). Among these, two lincRNAs (LINC01270 and LINC00449) were significantly associated with both worse overall survival and DFS and were expressed at significantly higher levels in tumor tissues compared with adjacent normal breast tissues (log2[Fold Change] > 0.5 and FDR < 0.05). We further evaluated the potential functions of LINC01270 and LINC00449 using in vitro functional experiments and found that siRNA-mediated knockdown of LINC01270 and LINC00449 expression significantly decreased cell viability, colony formation and cell migration ability in TNBC cells (P < 0.05).

CONCLUSIONS

Evidence from observational studies and in vitro experiments indicates that LINC00449 and LINC01270 may be prognostic biomarkers for TNBC.

LncRNA-LINC01089 inhibits lung adenocarcinoma cell proliferation and promotes apoptosis via sponging miR-543

LINC01089, a newly discovered long non-coding RNA (lncRNA), has been reported to inhibit the progression of various types of cancers. This study aimed to characterize LINC01089 in the pathogenesis of lung adenocarcinoma (LUAD). LINC01089 expression in LUAD tissues or/and cells and its association with the overall survival of LUAD patients was analyzed in The Cancer Genome Atlas (TCGA)-LUAD database, by qRT-PCR or by Kaplan-Meier's curve. Databases of StarBase, LncBase, and DEmiRNA were used to predict and confirm the interaction between LINC01089 and potential LINC01089-targeted microRNAs (miRNAs). The expressions of these miRNAs in LUAD tissues or/and cells were determined by qRT-PCR, and dual-luciferase reporter assay was performed to validate lncRNA-miRNA interaction. The expressions of B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X protein (Bax) and Cleaved caspase-3 in LUAD cells were analyzed by Western blot. LINC01089 improved overall survival of LUAD patients and was low-expressed in LUAD. Upregulating LINC01089 expression reduced LUAD cell viability, inhibited colony formation, enhanced apoptosis, accompanied by downregulated Bcl-2 and miR-543 and upregulated Bax and Cleaved caspase-3. MiR-543 was determined as a target gene of LINC01089, and was high-expressed in LUAD tissues. Upregulating miR-543 expression induced the opposite effects to LINC01089 upregulation on these cellular biological behaviors and the expressions of Bcl-2, Bax and Cleaved caspase-3. Moreover, the effects of miR-543 upregulation and LINC01089 upregulation were mutually counteracted by each other. LINC01089 inhibited lung adenocarcinoma cell proliferation and promoted apoptosis via sponging miR-543.

Long non-coding RNA TMPO-AS1 serves as a tumor promoter in pancreatic carcinoma by regulating miR-383-5p/SOX11

The dysregulation of lncRNA TMPO antisense RNA 1 (TMPO-AS1) has been detected in various malignant tumors. However, the role of lncRNA TMPO-AS1 remains unclear in pancreatic carcinoma. The present study aimed to elucidate the functional mechanism of TMPO-AS1 in pancreatic carcinoma. In the present study, RT-qPCR, western blotting, MTT, Transwell, luciferase reporter and xenograft assays were used to investigate the role of lncRNA TMPO-AS1 in pancreatic carcinoma. Upregulation of lncRNA TMPO-AS1 was revealed in pancreatic carcinoma tissues and cells. Furthermore, knockdown of TMPO-AS1 restrained cell proliferation and motility in pancreatic carcinoma. In addition, microRNA (miR)-383-5p acted as a 'sponge' for lncRNA TMPO-AS1. The expression levels of lncRNA TMPO-AS1 and miR-383-5p were mutually inhibited in pancreatic carcinoma. Moreover, miR-383-5p was revealed to directly target SRY-related high-mobility group box 11 (SOX11). Notably, SOX11 could promote the occurrence of pancreatic carcinoma by interacting with the lncRNA TMPO-AS1/miR-383-5p axis. In conclusion, upregulation of lncRNA TMPO-AS1 promoted tumor growth, cell migration and invasion in pancreatic carcinoma by downregulating miR-383-5p and upregulating SOX11.

Oncogenic roles of lncRNA BLACAT1 and its related mechanisms in human cancers

Long non-coding RNAs (lncRNAs) play indispensable roles in mediating regulation of epigenetics, and their dysregulation is strongly associated with the initiation and progression of human cancers. Recently, lncRNA bladder cancer-associated transcript 1 (BLACAT1) has been observed to exert oncogenic effects on cancers, including glioma, breast cancer, lung cancer, hepatocellular carcinoma, gastric cancer, colorectal cancer, ovarian cancer, cervical cancer and osteosarcoma. Additional mechanical analyses have uncovered that lncRNA BLACAT1 is positively correlated with tumor stage, lymph node metastasis and distant metastasis of primary tumors via involvement with various cellular activities, thus leading to poor overall survival and progression-free survival (PFS). In this review, we generalize the oncogenic roles of BLACAT1 in multiple human cancers through correlation with clinical implications and cellular activities. Moreover, we forecast its potential clinical application as a novel biomarker and a promising therapeutic target for cancers.

13-lncRNAs Signature to Improve Diagnostic and Prognostic Prediction of Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common type of cancer with a high mortality rate and is usually detected at the middle or late stage, missing the optimal treatment period. The current study aims to identify potential long non-coding RNA (lncRNAs) biomarkers that contribute to the diagnosis and prognosis of HCC.

METHODS

The differentially expressed lncRNAs (DElncRNAs) in HCC patients were detected from the Cancer Genome Atlas (TCGA) dataset. LncRNAs signature was screened by LASSO regression, univariate, and multivariate Cox regression. The models for predicting diagnosis and prognosis were established, respectively. The prognostic model was evaluated by Kaplan-Meier survival curve receiver operating characteristic (ROC) curve and stratified analysis. The diagnostic model was validated by ROC. The lncRNAs signature was further demonstrated by functional enrichment analysis.

RESULTS

We found the 13-lncRNAs signature that had a good performance in predicting prognosis and could help to improve the value of diagnosis. In the training set, testing set, and entire cohort, the low-risk group had longer survival than the high-risk group (median OS: 3124 vs. 649 days, 2456 vs. 770 days and 3124 vs. 755 days). It performed well in 1-, 3-, and 5-year survival prediction. 13-lncRNAs-based risk score, age, and race were good predictors of prognosis. The AUC of diagnosis was 0.9487, 0.9265, and 0.9376, respectively. Meanwhile, the 13-lncRNAs were involved in important pathways, including the cell cycle and multiple metabolic pathways.

CONCLUSION

In our study, the 13-lncRNAs signature may be a potential marker for the prognosis of HCC and improve the diagnosis.

Long Non-Coding RNA BLACAT1 in Human Cancers

Long non-coding RNAs (lncRNAs) are a cluster of RNAs with more than 200 nucleotides in length, which lack protein-coding capacity. They are important regulators of numerous cellular processes, including gene transcription, translation, and posttranslational modification, especially in tumor initiation and progression. Aberrant expression of lncRNA bladder cancer-associated transcript 1 (BLACAT1) has been reported in various human cancers and was usually associated with unfavorable prognosis. Previous studies have revealed that dysregulation of BLACAT1 could promote the proliferation and metastasis of cancer cells. In this review, we summarize the present understanding of the functions and underlying mechanisms of BLACAT1 in the occurrence and development of various human cancers and discuss the roles of this lncRNA in cancers, including its promising application as a prognostic biomarker or a novel therapeutic target for malignancies.

Identification and validation of key modules and hub genes associated with the pathological stage of oral squamous cell carcinoma by weighted gene co-expression network analysis

Background

Oral squamous cell carcinoma (OSCC) is a major lethal malignant cancer of the head and neck region, yet its molecular mechanisms of tumourigenesis are still unclear.

Patients and methods

We performed weighted gene co-expression network analysis (WGCNA) on RNA-sequencing data with clinical information obtained from The Cancer Genome Atlas (TCGA) database. The relationship between co-expression modules and clinical traits was investigated by Pearson correlation analysis. Furthermore, the prognostic value and expression level of the hub genes of these modules were validated based on data from the TCGA database and other independent datasets from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas database. The significant modules and hub genes were also assessed by functional analysis and gene set enrichment analysis (GSEA).

Results

We found that the turquoise module was strongly correlated with pathologic T stage and significantly enriched in critical functions and pathways related to tumourigenesis. PPP1R12B, CFD, CRYAB, FAM189A2 and ANGPTL1 were identified and statistically validated as hub genes in the turquoise module and were closely implicated in the prognosis of OSCC. GSEA indicated that five hub genes were significantly involved in many well-known cancer-related biological functions and signaling pathways.

Conclusion

In brief, we systematically discovered a co-expressed turquoise module and five hub genes associated with the pathologic T stage for the first time, which provided further insight that WGCNA may reveal the molecular regulatory mechanism involved in the carcinogenesis and progression of OSCC. In addition, the five hub genes may be considered candidate prognostic biomarkers and potential therapeutic targets for the precise early diagnosis, clinical treatment and prognosis of OSCC in the future.