Long non‑coding RNA LINC00491 promotes proliferation and inhibits apoptosis in esophageal squamous cell carcinoma

Aberrant expression of LGALS3BP drives an unfavorable prognosis and more aggressive in HCC via regulating PI3K/AKT signaling

Lectin galactoside-binding soluble 3-binding protein (LGALS3BP) is associated with cancer metastasis and is a promising prognostic marker in neoplasms. In hepatocellular carcinoma (HCC), the prognostic impact and pro-metastatic function of LGALS3BP remain unclear. This study evaluated the endogenous LGALS3BP expression in HCC tissue and its association with prognosis. LGALS3BP protein levels were significantly elevated in clinical HCC tissues and cell lines. Increased LGALS3BP expression was closely associated with disease progression in HCC patients, and they also exhibited an unfavorable prognosis. Furthermore, the knockdown of LGALS3BP inhibited the growth, migration, and invasion of HCC cells in vitro. In mice xenografts, silencing LGALS3BP significantly inhibited tumor cell growth in vivo. Mechanically, upon LGALS3BP depletion, the tumor-suppressive function was dependent on inactivating Phosphatidylinositol 3-kinase (PI3K)/V-akt murine thymoma viral oncogene homolog (AKT) signaling pathway. Collectively, these findings suggest that LGALS3BP employs a pro-tumorigenic function in HCC and may be a promising HCC prognostic marker.

Development of a prognostic Neutrophil Extracellular Traps related lncRNA signature for soft tissue sarcoma using machine learning

Background

Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS.

Methods

We applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR).

Results

Using the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS.

Conclusion

In conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

A Pleiotropic Role of Long Non-Coding RNAs in the Modulation of Wnt/β-Catenin and PI3K/Akt/mTOR Signaling Pathways in Esophageal Squamous Cell Carcinoma: Implication in Chemotherapeutic Drug Response

Despite the availability of modern techniques for the treatment of esophageal squamous cell carcinoma (ESCC), tumor recurrence and metastasis are significant challenges in clinical management. Thus, ESCC possesses a poor prognosis and low five-year overall survival rate. Notably, the origin and recurrence of the cancer phenotype are under the control of complex cancer-related signaling pathways. In this review, we provide comprehensive knowledge about long non-coding RNAs (lncRNAs) related to Wnt/β-catenin and phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling pathway in ESCC and its implications in hindering the efficacy of chemotherapeutic drugs. We observed that a pool of lncRNAs, such as HERES, TUG1, and UCA1, associated with ESCC, directly or indirectly targets various molecules of the Wnt/β-catenin pathway and facilitates the manifestation of multiple cancer phenotypes, including proliferation, metastasis, relapse, and resistance to anticancer treatment. Additionally, several lncRNAs, such as HCP5 and PTCSC1, modulate PI3K/Akt/mTOR pathways during the ESCC pathogenesis. Furthermore, a few lncRNAs, such as AFAP1-AS1 and LINC01014, block the efficiency of chemotherapeutic drugs, including cisplatin, 5-fluorouracil, paclitaxel, and gefitinib, used for ESCC treatment. Therefore, this review may help in designing a better therapeutic strategy for ESCC patients.

Mining the Biomarkers and Associated-Drugs for Esophageal Squamous Cell Carcinoma by Bioinformatic Methods

Esophageal squamous cell carcinoma (ESCC) showed limited treatment outcome and poor prognosis. This study aimed to screen potential biomarkers and drugs in ESCC. Firstly, GSE26886, GSE111044 and GSE77861 were downloaded from the Gene Expression Omnibus (GEO) database. Next, the differentially expressed genes (DEGs) between cancer and noncancerous tissues were analyzed by the GEO2R. The Gene Ontology (GO) annotation, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, the protein-protein interaction (PPI) analysis and hub genes screened were conducted by some bioinformatic methods, respectively. Lastly, the hub genes and potential drugs were verified by the GEPIA2 and the QuartataWeb database. The results showed that 13 up-regulated genes and 81 down-regulated genes were identified. In GO terms, DEGs were mainly associated with cell proliferation, cell migration and cell differentiation. DEGs did not cluster into the KEGG pathway. After hub genes validated, nine genes (FLG, COL1A1, COL1A2, PSCA, SCEL, PPL, ACPP, CNFN, and A2ML1) expression trends showed no change. Moreover, higher COL1A1 or COL1A2 expression for ESCC patients showed poor prognosis. Finally, five drugs used for promoting blood coagulation were identified. Probably, these drugs could show anticancer effects by promoting blood coagulation or inhibiting vascular formation in cancers, which offers a novel idea for the treatment of ESCC.

Long non-coding RNA OIP5-AS1 promotes the progression of esophageal cancer by regulating miR-30a/VOPP1 expression

Long non-coding RNAs (lncRNAs) serve an important role in the development of esophageal cancer (EC), which is the eighth most common type of cancer worldwide. lncRNA opa-interacting protein 5 antisense transcript 1 (OIP5-AS1) is associated with human malignancy. However, the biological roles of OIP5-AS1 in the development of EC remain unclear. In the present study, transfection was conducted, and reverse transcription-quantitative PCR and western blot analysis were used for the detection of mRNA and protein expression, respectively. Furthermore, dual-luciferase reporter and RNA immunoprecipitation assays were used to study the interaction between miRNA and lncRNA or genes. The results revealed that OIP5-AS1 expression in EC tissues and cultured EC cells was upregulated, microRNA-30a (miR-30a) expression was downregulated. OIP5-AS1-knockdown suppressed the proliferation, migration and invasion of EC9706 and EC109 cells. miR-30a was confirmed to interact with OIP5-AS1, and miR-30a-mimics transfection ameliorated the effects of OIP5-AS1 in EC cells. Vesicular overexpressed in cancer prosurvival protein 1 (VOPP1) was verified as the direct target of miR-30a. VOPP1 expression was positively correlated with OIP5-AS1 expression in EC cells. Overexpression of VOPP1 ameliorated the negative effects of OIP5-AS1-knockdown on EC9706 and EC109 cells. In conclusion, OIP5-AS1 promoted the proliferation, migration and invasion of EC cells by increasing VOPP1 expression by sponging miR-30a.