Survival-Related lncRNA Landscape Analysis Identifies LINC01614 as an Oncogenic lncRNA in Gastric Cancer

Clinical Significance and Immune Infiltration Analyses of a Novel Nerve-Related lncRNA Signature in Gastric Cancer

Gastric cancer (GC) is a progressive disease with high morbidity and mortality. Accumulating evidence indicated that nervous system-cancer crosstalk can affect the occurrence and progression of GC. However, the role of nerve-related lncRNAs (NRLs) in GC remains largely unexplored. In this study, a total of 441 nerve-related genes were collected from the KEGG database, and two approaches, unsupervised clustering and WGCNA, were employed to identify NRLs. Lasso regression analysis was then used to construct the nerve-related lncRNA signature (NRLS). Based on the expression profiles of 5 lncRNAs, we developed a stable NRLS to predict survival in GC patients, and survival analyses showed significantly shorter overall survival (OS) in patients with high NRLS. In addition, the NRLS was found to be positively correlated with immune characteristics, including tumor-infiltrating immune cells, immune modulators, cytokines and chemokines. We then analyzed the role of NRLS in predicting chemotherapy and immunotherapy responses, and constructed the OS nomogram combining NRLS and other clinical features. In conclusion, we constructed a robust NRLS model to stratify GC patients and predict the outcomes of chemotherapy and immunotherapy. This study can provide a new perspective for future individualized treatment of GC.

Potential use of SCAT1, SCAT2, and SCAT8 as diagnostic and prognosis markers in colorectal cancer

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related deaths worldwide. Despite advancements, the underlying mechanisms controlling CRC's etiology remain unclear, and reliable biomarkers for diagnosis and treatment are still lacking. Long noncoding RNAs (lncRNAs) are increasingly recognized for their role in cancer progression, though many remain unidentified and their functions poorly understood. In this study, we investigated the expression of SCAT1, SCAT2, and SCAT8 lncRNAs in both cancerous and adjacent non-cancerous tissues from CRC patients. Using cDNA synthesized from total RNA extracted from 100 tissue samples, we performed Real-Time PCR to measure the expression levels of these lncRNAs. In addition, their diagnostic potential was evaluated through ROC curve analysis. Our results demonstrate that SCAT1, SCAT2, and SCAT8 are significantly upregulated in CRC tissues, with ROC analysis suggesting SCAT1 as a moderate biomarker and SCAT2 and SCAT8 as promising biomarkers for CRC diagnosis. Moreover, we found strong correlations between SCAT1 and SCAT8, as well as SCAT2 and SCAT8. Collectively, our findings indicate that SCAT1, SCAT2, and SCAT8 may act as oncogenes in CRC, offering potential as novel biomarkers for diagnosis and prognosis.

Expression of Intragenic LCAL4 Long Non-Coding RNAs as a Potential Diagnostic and Prognostic Marker in Female Breast Cancer

Objective

In breast cancer early detection is associated with reduced mortality and it is essential to identify new biomarkers for early detection and appropriate management of cancer patients with the best response to treatment. Long non-coding RNAs (LncRNAs) have attracted much attention as potential diagnostic, prognostic, or predictive biomarkers due to their high specificity, easy access to non-invasive methods, and their aberrant expression under various pathological and physiological conditions. Have attracted the aim of this study is to investigate the expression profile of intragenic non-coding LncRNAs LCAL4 as a biomarker as potential diagnostic and prognostic biomarkers in cancer.

Materials and methods

In this research, 62 tissue samples were obtained from patients undergoing therapeutic surgery in Khatam al-Anbia Hospital and the normal peripheral tissue that was removed for prevention was used as a control by Real-time PCR method.

Results

The expression pattern of LCAL4 long non-coding RNA gene is significantly different between two groups of healthy control samples and samples obtained from patients with different breast cancer subtypes, Also its expression between samples obtained from different subgroups and different stages showed significant differences.

Conclusion

The studied LncRNAs can act as a factor to identify tumor tissue from healthy tissue, and the diagnosis of cancer grades can be different depending on the type of LncRNA. These results can be proposed in the introduction of LncRNA LCAL4 as a new marker in the diagnosis of breast cancer. In addition, by interpreting the results, it can be concluded that these LncRNAs can be considered as influential factors in the process of breast cancer.

LINC01614 is a promising diagnostic and prognostic marker in HNSC linked to the tumor microenvironment and oncogenic function

Background

Tumor initiation and metastasis influence tumor immune exclusion and immunosuppression. Long non-coding RNA (lncRNA) LINC01614 is associated with the prognosis and metastasis of several cancers. However, the relationship between LINC01614 and cancer immune infiltration and the biofunction of LINC01614 in head and neck squamous cell carcinoma (HNSC) remain unclear.

Methods

The Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets were used to analyze the expression difference and diagnostic value of LINC01614 in normal and tumor tissues. The correlation of pan-cancer prognosis and tumor stage of LINC01614 was analyzed based on the TCGA database. The pan-cancer association of LINC01614 expression with the tumor microenvironment (TME) including immune infiltration, expression of immune-related genes, and genomic instability parameters, was analyzed using the Spearman correlation method. The correlation between LINC01614 and tumor stemness evaluation indicators, RNA methylation-related genes, and drug resistance was also analyzed. The functional analysis of LINC01614 was performed using the clusterProfiler R package. The protein-protein interaction (PPI) network and ceRNA network of LINC01614 co-expressed genes and miRNA were constructed and visualized by STRING and Cytoscape, respectively. Finally, the cell location and influence of LINC01614 on cell proliferation and metastasis of HNSC cell lines were evaluated using FISH, CCK-8, wound-healing assay, and transwell assay.

Results

LINC01614 was found to be overexpressed in 23 cancers and showed a highly sensitive prediction value in nine cancers (AUC >0.85). LINC01614 dysregulation was associated with tumor stage in 12 cancers and significantly influenced the survival outcomes of 26 cancer types, with only Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), uterine corpus endometrial carcinoma (UCEC), and bladder urothelial carcinoma (BLCA) showing a benign influence. LINC01614 was also associated with immune cell infiltration, tumor heterogeneity, cancer stemness, RNA methylation modification, and drug resistance. The potential biological function of LINC01614 was verified in HNSC, and it was found to play important roles in proliferation, immune infiltration, immunotherapy response, and metastasis of HNSC.

Conclusion

LINC01614 may serve as a cancer diagnosis and prognosis biomarker and an immunotherapy target for specific cancers.

Constructing discriminative feature space for LncRNA-protein interaction based on deep autoencoder and marginal fisher analysis

Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space; (2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein interactions. To resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. The code and data are available on https://github.com/D0ub1e-D/DFRPI.

Identification and validation of a novel angiogenesis-related gene signature for predicting prognosis in gastric adenocarcinoma

Background

Angiogenesis is a major promotor of tumor progression and metastasis in gastric adenocarcinoma (STAD). We aimed to develop a novel lncRNA gene signature by identifying angiogenesis-related genes to better predict prognosis in STAD patients.

Methods

The expression profiles of angiogenesis-related mRNA and lncRNA genes were collected from The Cancer Genome Atlas (TCGA). Then, the "limma" package was used to identify differentially expressed genes (DEGs). The expression profiles of angiogenesis-related genes were clustered by consumusclusterplus. The Pearson correlation coefficient was further used to identify lncRNAs coexpressed with angiogenesis-related clustere genes. We used Lasso Cox regression analysis to construct the angiogenesis-related lncRNAs signature. Furthermore, the diagnostic accuracy of the prognostic risk signature were validated by the TCGA training set, internal test sets and external test set. We used multifactor Cox analysis to determine that the risk score is an independent prognostic factor different from clinical characteristics. Nomogram has been used to quantitatively determine personal risk in a clinical environment. The ssGSEA method or GSE176307 data were used to evaluate the infiltration state of immune cells or predictive ability for the benefit of immunotherapy by angiogenesis-related lncRNAs signature. Finally, the expression and function of these signature genes were explored by RT-PCR and colony formation assays.

Results

Among angiogenesis-related genes clusters, the stable number of clusters was 2. A total of 289 DEGs were identified and 116 lncRNAs were screened to have a significant coexpression relationship with angiogenic DEGs (P value<0.001 and |R| >0.5). A six-gene signature comprising LINC01579, LINC01094, RP11.497E19.1, AC093850.2, RP11.613D13.8, and RP11.384P7.7 was constructed by Lasso Cox regression analysis. The multifactor Cox analysis and Nomogram results showed that our angiogenesis-related lncRNAs signature has good predictive ability for some different clinical factors. For immune, angiogenesis-related lncRNAs signature had the ability to efficiently predict infiltration state of 23 immune cells and immunotherapy. The qPCR analysis showed that the expression levels of the six lncRNA signature genes were all higher in gastric adenocarcinoma tissues than in adjacent tissues. The functional experiment results indicated that downregulation of the expression of these six lncRNA signature genes suppressed the proliferation of ASG and MKN45 cells.

Conclusion

Six angiogenesis-related genes were identified and integrated into a novel risk signature that can effectively assess prognosis and provide potential therapeutic targets for STAD patients.

Inflammatory Genes Associated with Pristine Multi-Walled Carbon Nanotubes-Induced Toxicity in Ocular Cells

Background

The wide application of multi-walled carbon nanotubes (MWCNTs) in various fields has raised enormous concerns regarding their safety for humans. However, studies on the toxicity of MWCNTs to the eye are rare and potential molecular mechanisms are completely lacking. This study was to evaluate the adverse effects and toxic mechanisms of MWCNTs on human ocular cells.

Methods

Human retinal pigment epithelial cells (ARPE-19) were treated with pristine MWCNTs (7-11 nm) (0, 25, 50, 100 or 200 μg/mL) for 24 hours. MWCNTs uptake into ARPE-19 cells was examined using transmission electron microscopy (TEM). The cytotoxicity was evaluated by CCK-8 assay. The death cells were detected by Annexin V-FITC/PI assay. RNA profiles in MWCNT-exposed and non-exposed cells (n = 3) were analyzed using RNA-sequencing. The differentially expressed genes (DEGs) were identified through the DESeq2 method and hub of which were filtered by weighted gene co-expression, protein-protein interaction (PPI) and lncRNA-mRNA co-expression network analyses. The mRNA and protein expression levels of crucial genes were verified using quantitative polymerase chain reaction (qPCR), colorimetric analysis, ELISA and Western blotting. The toxicity and mechanisms of MWCNTs were also validated in human corneal epithelial cells (HCE-T).

Results

TEM analysis indicated the internalization of MWCNTs into ARPE-19 cells to cause cell damage. Compared with untreated ARPE-19 cells, those exposed to MWCNTs exhibited significantly decreased cell viabilities in a dose-dependent manner. The percentages of apoptotic (early, Annexin V positive; late, Annexin V and PI positive) and necrotic (PI positive) cells were significantly increased after exposure to IC50 concentration (100 μg/mL). A total of 703 genes were identified as DEGs; 254 and 56 of them were, respectively, included in darkorange2 and brown1 modules that were significantly associated with MWCNT exposure. Inflammation-related genes (including CXCL8, MMP1, CASP3, FOS, CXCL2 and IL11) were screened as hub genes by calculating the topological characteristics of genes in the PPI network. Two dysregulated long non-coding RNAs (LUCAT1 and SCAT8) were shown to regulate these inflammation-related genes in the co-expression network. The mRNA levels of all eight genes were confirmed to be upregulated, while caspase-3 activity and the release of CXCL8, MMP1, CXCL2, IL11 and FOS proteins were demonstrated to be increased in MWCNT-treated ARPE-19 cells. MWCNTs exposure also can induce cytotoxicity and increase the caspase-3 activity and the expression of LUCAT1, MMP1, CXCL2, and IL11 mRNA and protein in HCE-T cells.

Conclusion

Our study provides promising biomarkers for monitoring MWCNT-induced eye disorders and targets for developing preventive and therapeutic strategies.

A prognostic signature of pyroptosis-related lncRNAs verified in gastric cancer samples to predict the immunotherapy and chemotherapy drug sensitivity

Background: Pyroptosis is a recently identified mode of programmed inflammatory cell death that has remarkable implications for cancer development. lncRNAs can be involved in cellular regulation through various pathways and play a critical role in gastric cancer (GC). However, pyroptosis -related lncRNAs (PRlncRNAs) have been rarely studied in GC.

Methods: Pyroptosis-related gene were abstracted from the literature and GSEA Molecular Signatures data resource. PRlncRNAs were obtained using co-expression analysis. LASSO Cox regression assessment was employed to build a risk model. Kaplan-Meier (KM), univariate along with multivariate Cox regression analysis were adopted to verify the predictive efficiency of the risk model in terms of prognosis. qRT-PCR was adopted to validate the expression of PRlncRNAs in GC tissues. In addition, immune cell infiltration assessment and ESTIMATE score evaluation were adopted for assessing the relationship of the risk model with the tumor immune microenvironment (TME). Finally, immune checkpoint gene association analysis and chemotherapy drug sensitivity analysis were implemented to assess the worthiness of our risk model in immunotherapy and chemotherapy of GC.

Results: We identified 3 key PRlncRNAs (PVT1, CYMP-AS1 and AC017076.1) and testified the difference of their expression levels in GC tumor tissues and neighboring non-malignant tissues (p < 0.05). PRlncRNAs risk model was able to successfully estimate the prognosis of GC patients, and lower rate of survival was seen in the high-GC risk group relative to the low-GC risk group (p < 0.001). Other digestive system tumors such as pancreatic cancer further validated our risk model. There was a dramatic difference in TMB level between high-GC and low-GC risk groups (p < 0.001). Immune cell infiltration analysis and ESTIMATE score evaluation demonstrated that the risk model can be adopted as an indicator of TME status. Besides, the expressions of immunodetection site genes in different risk groups were remarkably different (CTLA-4 (r = −0.14, p = 0.010), VISTA (r = 0.15, p = 0.005), and B7-H3 (r = 0.14, p = 0.009)). PRlncRNAs risk model was able to effectively establish a connection with the sensitivity of chemotherapeutic agents.

Conclusion: The 3 PRlncRNAs identified in this study could be utilized to predict disease outcome in GC patients. It may also be a potential therapeutic target in GC therapy, including immunotherapy and chemotherapy.

Construction of a Prognostic Evaluation Model for Stomach Adenocarcinoma on the Basis of Immune-Related lncRNAs

Progression, prognosis, and therapeutic strategy of stomach adenocarcinoma (STAD) have a close connection with tumor microenvironment (TME). Thus, it is pivotal to delve into the TME and immune-related genes, which may bring possibilities for improving patient's prognosis. TCGA-STAD dataset was analyzed to acquire differentially expressed lncRNAs in tumor samples, which were overlapped with the immune-related lncRNA datasets in the ImmLnc database. Twenty-six lncRNAs related to STAD immunity and patient's prognosis were acquired by univariate Cox analysis. Following lncRNA expression patterns, STAD samples could be classified into two clusters with completely different immune patterns. We performed multivariate Cox regression analysis on lncRNAs to identify 7-feature lncRNAs and constructed a corresponding prognostic model. The model validity was verified by survival analysis and ROC curve in validation and training sets. To explore connection between model and TME and tumor drug resistance, this study analyzed differences in immune cell infiltration between samples from high- and low-risk groups and then revealed immune cells follicular helper with significant differences in tumor tissue infiltration. Analysis of resistance to chemotherapeutic drugs revealed that samples in the high-risk group had resistance to cisplatin, doxorubicin, bleomycin, and gemcitabine. Through univariate and multivariate Cox analyses, we manifested that risk score could be an independent prognostic factor. Combining risk score and clinical factors, a nomogram was constructed to accurately predict patient's prognosis. This model can effectively predict prognosis, TME, and drug resistance of STAD patients, which may provide a reference for tumor development evaluation and precise treatment for clinical STAD.

Risk SNP-mediated LINC01614 upregulation drives head and neck squamous cell carcinoma progression via PI3K/AKT signaling pathway

As potential biomarkers and therapeutic targets, long noncoding RNAs (lncRNAs) are involved in the tumorigenesis of various tumors. Genetic variation in long noncoding regions can lead to lncRNA dysfunction and even cancer. Nevertheless, studies on the association between lncRNA-associated single-nucleotide polymorphisms (SNPs) and the risk of head and neck squamous cell carcinoma (HNSCC) remain inadequate. Here, we aimed to explore the association between SNPs in LINC01614 and HNSCC risk, and the potential role of LINC01614 in tumorigenesis. In this study, we found that rs16854802 A > G (odds ratio [OR] = 1.42, 95% confidence interval [CI]: 1.22-1.77, p < 0.001) and rs3113503 G > C (OR = 1.38, 95% CI: 1.15-1.64, p < 0.001) in LINC01614 increased the risk of HNSCC in the Chinese population. Functional bioinformatic analysis and luciferase reporter assay revealed that rs3113503 G > C variant disrupted the binding of miRNA-616-3p to LINC01614, which resulted in the increased expression of LINC01614. Further analysis of the TCGA database demonstrated that the upregulated LINC01614 in HNSCC cancer tissues was associated with poor prognostic in HNSCC patients. In vitro experiments showed that knockdown of LINC01614 inhibited the proliferation, invasion, and migration ability of HNSCC cells. Mechanistically, allele C of rs3113503 in LINC01614 was more effective than allele G in activating the PI3K/AKT signaling pathway. Moreover, the reduced expression of LINC01614 also inhibited the activation of the PI3K/AKT signaling pathway. In summary, our findings revealed that the risk SNP rs3113503 G > C in LINC01614 altered the binding to miR-616-3p, which led to increased LINC01614 expression and promoted HNSCC progression by activating the PI3K/AKT signaling pathway.