Long Noncoding RNA LINC00551 Suppresses Glycolysis and Tumor Progression by Regulating c-Myc-Mediated PKM2 Expression in Lung Adenocarcinoma

NPM3 as an Unfavorable Prognostic Biomarker Involved in Oncogenic Pathways of Lung Adenocarcinoma via MYC Translational Activation

BACKGROUND

The nucleoplasmin/nucleophosmin (NPM) family was previously regarded as a critical regulator during disease development, and its mediation in carcinogenesis has achieved intensive attention recently. However, the clinical importance and functional mechanism of NPM3 in lung adenocarcinoma (LUAD) have not been reported yet.

OBJECTIVE

This study aimed to investigate the role and clinical significance of NPM3 in the development and progression of LUAD, including the underlying mechanisms.

METHODS

The expression of NPM3 in pan-cancer was analyzed via GEPIA. The effect of NPM3 on prognosis was analyzed by the Kaplan-Meier plotter and the PrognoScan database. In vitro, cell transfection, RT-qPCR, CCK-8 assay, and wound healing assay were employed to examine the role of NPM3 in A549 and H1299 cells. Gene set enrichment analysis (GSEA) was performed using the R software package to analyze the tumor hallmark pathway and KEGG pathway of NPM3. The transcription factors of NPM3 were predicted based on the ChIP-Atlas database. Dual-luciferase reporter assay was applied to verify the transcriptional regulatory factor of the NPM3 promoter region.

RESULTS

The NPM3 expression was found to be markedly higher in the LUAD tumor group than the normal group and to be positively correlated with poor prognosis, tumor stages, and radiation therapy. In vitro, the knockdown of NPM3 greatly inhibited the proliferation and migration of A549 and H1299 cells. Mechanistically, GSEA predicted that NPM3 activated the oncogenic pathways. Further, the NPM3 expression was found to be positively correlated with cell cycle, DNA replication, G2M checkpoint, HYPOXIA, MTORC1 signaling, glycolysis, and MYC targets. Besides, MYC targeted the promoter region of NPM3 and contributed to the enhanced expression of NPM3 in LUAD.

CONCLUSION

The overexpression of NPM3 is an unfavorable prognostic biomarker participating in oncogenic pathways of LUAD via MYC translational activation and it contributes to tumor progression. Thus, NPM3 could be a novel target for LUAD therapy.

Heat shock protein B7 (HSPB7) inhibits lung adenocarcinoma progression by inhibiting glycolysis

In the present study, it was aimed to investigate the effects and potential mechanisms of heat shock protein B7 (HSPB7) on lung adenocarcinoma (LUAD). Bioinformatic analysis was performed to explore the association between HSPB7 expression and patients with LUAD. MTT, colony formation, wound healing and Transwell assays were performed to examine the proliferative, migratory and invasive abilities of H1975 and A549 cells. Western blot analysis was conducted to determine the corresponding protein expression. Co‑Immunoprecipitation and Chromatin immunoprecipitation assays were carried out to reveal the interaction between HSPB7 and myelodysplastic syndrome 1 and ecotropic viral integration site 1 complex locus (MECOM). In addition, an animal model was conducted by the subcutaneous injection of A549 cells into BALB/c nude mice, and tumor weight and size were measured. HSPB7 was downregulated in LUAD tissues and cells, and its expression level correlated with patient prognosis. Cell functional data revealed that silencing of HSPB7 promoted lung cancer cell proliferation, migration, invasion and epithelial mesenchymal transition (EMT); whereas overexpression of HSPB7 led to the opposite results. Furthermore, bioinformatics analysis showed that HSPB7 inhibited glycolysis. HSPB7 decreased glucose consumption, lactic acid production, and lactate dehydrogenase A, hexokinase 2 and pyruvate kinase muscle isoform 2 protein levels. The results demonstrated that MECOM was a transcription factor of HSPB7. Collectively, these results suggested that HSPB7 is regulated by MECOM, and that HSPB7 attenuates LUAD cell proliferation, migration, invasion and EMT by inhibiting glycolysis.

A comprehensive prognostic and immunological analysis of telomere-related lncRNAs in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent malignant tumors of the urinary system, with a high recurrence and metastasis rate. Telomeres and long non-coding RNAs (lncRNAs) have been documented playing critical roles in cancer progression. However, the prognostic significance of telomere-related lncRNA (TRLs) in KIRC is less well-defined. The Cancer Genome Atlas database was applied to retrieve the expression profiles and corresponding clinical information of KIRC patients. To create the TRLs prognostic signature, univariate Cox regression, least absolute shrinkage and selection operator analyses were performed. The prognostic signature, comprised of nine prognostic TRLs, was developed and demonstrated superior prognostic ability for KIRC patients. Additionally, the risk score acted as an independent prognostic indicator. A nomogram incorporating age, grade, stage, and signature-based risk scores was also developed and exhibited excellent predictive accuracy. Several immune activities were associated with the signature, as determined by gene function analysis. Further analysis revealed differences in the status of immunity and the tumor microenvironment between low- and high-risk groups. Notably, KIRC patients with high-risk scores were more responsive to immunotherapy and chemotherapy. To summarize, our study developed a new prognostic signature consisting of nine telomere-related lncRNA that can precisely predict the prognosis of KIRC patients. The signature was shown to be of substantial value for the tumor microenvironment and tumor mutation burden, thereby contributing to a framework for the individualized treatment of patients.

Chromatin regulators-related lncRNA signature predicting the prognosis of kidney renal clear cell carcinoma and its relationship with immune microenvironment: A study based on bioinformatics and experimental validation

Background: Kidney Renal Clear cell carcinoma (KIRC) is a major concern in the urinary system. A lot of researches were focused on Chromatin Regulators (CRs) in tumors. In this study, CRs-related lncRNAs (CRlncRNAs) were investigated for their potential impact on the prognosis of KIRC and the immune microenvironment.

Methods: The TCGA database was used to obtain transcriptome and related clinical information. CRs were obtained from previous studies, whereas CRlncRNAs were obtained by differential and correlation analysis. We screened the lncRNAs for the signature construction using regression analysis and LASSO regression analysis. The effectiveness of the signature was evaluated using the Kaplan-Meier (K-M) curve and Receiver Operating Characteristic curve (ROC). Additionally, we examined the associations between the signature and Tumor Microenvironment (TME), and the efficacy of drug therapy. Finally, we further verified whether these lncRNAs could affect the biological function of KIRC cells by functional experiments such as CCK8 and transwell assay.

Results: A signature consisting of 8 CRlncRNAs was constructed to predict the prognosis of KIRC. Quantitative Real-Time PCR verified the expression of 8 lncRNAs at the cell line and tissue level. The signature was found to be an independent prognostic indicator for KIRC in regression analysis. This signature was found to predict Overall Survival (OS) better for patients in the subgroups of age, gender, grade, stage, M, N0, and T. Furthermore, a significant correlation was found between riskScore and immune cell infiltration and immune checkpoint. Finally, we discovered several drugs with different IC50 values in different risk groups using drug sensitivity analysis. And functional experiments showed that Z97200.1 could affect the proliferation, migration and invasion of KIRC cells.

Conclusion: Overall, the signature comprised of these 8 lncRNAs were reliable prognostic biomarkers for KIRC. Moreover, the signature had significant potential for assessing the immunological landscape of tumors and providing individualized treatment.

The role of lncRNAs in regulation of DKD and diabetes-related cancer

Diabetes mellitus often results in several complications, such as diabetic kidney disease (DKD) and end-stage renal diseases (ESRDs). Cancer patients often have the dysregulated glucose metabolism. Abnormal glucose metabolism can enhance the tumor malignant progression. Recently, lncRNAs have been reported to regulate the key proteins and signaling pathways in DKD development and progression and in cancer patients with diabetes. In this review article, we elaborate the evidence to support the function of lncRNAs in development of DKD and diabetes-associated cancer. Moreover, we envisage that lncRNAs could be diagnosis and prognosis biomarkers for DKD and cancer patients with diabetes. Furthermore, we delineated that targeting lncRNAs might be an alternative approach for treating DKD and cancer with dysregulated glucose metabolism.

Identification of LOC101927355 as a Novel Biomarker for Preeclampsia

Preeclampsia, a disorder with a heterogeneous physiopathology, can be attributed to maternal, fetal, and/or placental factors. Long non-coding RNAs (lncRNAs) refer to a class of non-coding RNAs, the essential regulators of biological processes; their differential expression has been associated with the pathogenesis of multiple diseases. The study aimed to identify lncRNAs, expressed in the placentas and plasma of patients who presented with preeclampsia, as potential putative biomarkers of the disease. In silico analysis was performed to determine lncRNAs differentially expressed in the placentas of patients with preeclampsia, using a previously published RNA-Seq dataset. Seven placentas and maternal plasma samples collected at delivery from preterm preeclamptic patients (≤37 gestational weeks of gestation), and controls were used to validate the expression of lncRNAs by qRT-PCR. Six lncRNAs were validated and differentially expressed (p < 0.05) in the preeclampsia and control placentas: UCA1 and HCG4 were found upregulated, and LOC101927355, LINC00551, PART1, and NRAD1 downregulated. Two of these lncRNAs, HCG4 and LOC101927355, were also detected in maternal plasma, the latter showing a significant decrease (p = 0.03) in preeclamptic patients compared to the control group. In silico analyses showed the cytoplasmic location of LOC101927355, which suggests a role in post-transcriptional gene regulation. The detection of LOC101927355 in the placenta and plasma opens new possibilities for understanding the pathogenesis of preeclampsia and for its potential use as a biomarker.

Clinicopathological and Prognostic Value of Necroptosis-Associated lncRNA Model in Patients with Kidney Renal Clear Cell Carcinoma

Background. Necroptosis, a recently identified type of programmed necrotic cell death, is closely related to the tumorigenesis and development of cancer. However, it remains unclear whether necroptosis-associated long noncoding RNAs (lncRNAs) can be used to predict the prognosis of kidney renal clear cell carcinoma (KIRC). This work was designed to probe the possible prognostic worth of necroptosis-associated lncRNAs along with their impact on the tumor microenvironment (TME) in KIRC. Methods. The Cancer Genome Atlas (TCGA) database was used to extract KIRC gene expression and clinicopathological data. Pearson correlation analysis was used to evaluate necroptosis-associated lncRNAs against 159 known necroptosis-associated genes. To define molecular subtypes, researchers used univariate Cox regression analysis and consensus clustering, as well as clinical significance, TME, and tumor immune cells in each molecular subtype. We develop the necroptosis-associated lncRNA prognostic model using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Patients were divided into high- and low-risk groups according to prognostic model. Moreover, comprehensive analyses, including prognostic value, gene set enrichment analysis (GSEA), immune infiltration, and immune checkpoint gene expression, were performed between the two risk groups. Finally, anticancer drug sensitivity analyses were employed for assessing associations for necroptosis-associated lncRNA expression profile and anticancer drug chemosensitivity. Results. Through univariate analysis, sixty-nine necroptosis-associated lncRNAs were found to have a significant relationship with KIRC prognosis. Two molecular clusters were identified, and significant differences were found with respect to clinicopathological features and prognosis. The segregation of patients into two risk groups was done by the constructed necroptosis-associated lncRNA model. The survival prognosis, clinical features, degree of immune cell infiltration, and expression of immune checkpoint genes of high-risk and low-risk groups were all shown to vary. Conclusions. Our study identified a model of necroptosis-associated lncRNA signature and revealed its prognostic role in KIRC. It is expected to provide a reference for the screening of KIRC prognostic markers and the evaluation of immune response.

N7-Methylguanosine-Related lncRNAs: Integrated Analysis Associated With Prognosis and Progression in Clear Cell Renal Cell Carcinoma

N7-Methylguanosine (m7G) and long non-coding RNAs (lncRNAs) have been widely reported to play an important role in cancer. However, there is little known about the relationship between m7G-related lncRNAs and clear cell renal cell carcinoma (ccRCC). To find new potential biomarkers and construct an m7G-related lncRNA prognostic signature for ccRCC, we retrieved transcriptome data and clinical data from The Cancer Genome Atlas (TCGA), and divided the entire set into train set and test set with the ratio of 1:1 randomly. The m7G-related lncRNAs were identified by Pearson correlation analysis (|coefficients| &gt; 0.4, and p &lt; 0.001). Then we performed the univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a 12 m7G-related lncRNA prognostic signature. Next, principal component analysis (PCA), the Kaplan–Meier method, time-dependent receiver operating characteristics (ROC) were made to verify and evaluate the risk signature. A nomogram based on the risk signature and clinical parameters was developed and showed high accuracy and reliability for predicting the overall survival (OS). Functional enrichment analysis (GO, KEGG and GSEA) was used to investigate the potential biological pathways. We also performed the analysis of tumor mutation burden (TMB), immunological analysis including immune scores, immune cell infiltration (ICI), immune function, tumor immune escape (TIE) and immunotherapeutic drug in our study. In conclusion, using the 12 m7G-related lncRNA risk signature as a prognostic indicator may offer us insight into the oncogenesis and treatment response prediction of ccRCC.

Development and Validation of Lactate Metabolism-Related lncRNA Signature as a Prognostic Model for Lung Adenocarcinoma

BACKGROUND

Lung cancer has been a prominent research focus in recent years due to its role in cancer-related fatalities globally, with lung adenocarcinoma (LUAD) being the most prevalent histological form. Nonetheless, no signature of lactate metabolism-related long non-coding RNAs (LMR-lncRNAs) has been developed for patients with LUAD. Accordingly, we aimed to develop a unique LMR-lncRNA signature to determine the prognosis of patients with LUAD.

METHOD

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to derive the lncRNA expression patterns. Identification of LMR-lncRNAs was accomplished by analyzing the co-expression patterns between lncRNAs and LMR genes. Subsequently, the association between lncRNA levels and survival outcomes was determined to develop an effective signature. In the TCGA cohort, Cox regression was enlisted to build an innovative signature consisting of three LMR-lncRNAs, which was validated in the GEO validation cohort. GSEA and immune infiltration analysis were conducted to investigate the functional annotation of the signature and the function of each type of immune cell.

RESULTS

Fourteen differentially expressed LMR-lncRNAs were strongly correlated with the prognosis of patients with LUAD and collectively formed a new LMR-lncRNA signature. The patients could be categorized into two cohorts based on their LMR-lncRNA signatures: a low-risk and high-risk group. The overall survival of patients with LUAD in the high-risk group was considerably lower than those in the low-risk group. Using Cox regression, this signature was shown to have substantial potential as an independent prognostic factor, which was further confirmed in the GEO cohort. Moreover, the signature could anticipate survival across different groups based on stage, age, and gender, among other variables. This signature also correlated with immune cell infiltration (including B cells, neutrophils, CD4⁺ T cells, CD8⁺ T cells, etc.) as well as the immune checkpoint blockade target CTLA-4.

CONCLUSION

We developed and verified a new LMR-lncRNA signature useful for anticipating the survival of patients with LUAD. This signature could give potentially critical insight for immunotherapy interventions in patients with LUAD.

PDGFRβ Modulates Aerobic Glycolysis in Osteosarcoma HOS Cells via the PI3K/AKT/mTOR/c-Myc Pathway

Osteosarcoma is a malignant tumor abundant in vascular tissue, and its rich blood supply may have a significant impact on its metabolic characteristics. PDGFRβ is a membrane receptor highly expressed in osteosarcoma cells and vascular wall cells, and its effect on osteosarcoma metabolism needs to be further studied. In this study, we discussed the effect and mechanism of PDGFRβ on the glucose metabolism of osteosarcomaHOS cells. First, GSEA, Pearson's correlation test, and PPI correlation analysis indicated the positive regulation of PDGFRβ on aerobic glycolysis in osteosarcoma. The results of qPCR and western blot further confirmed the prediction of bioinformatics. Glucose metabolism experiments proved that PDGF/PDGFRβ couldeffectively promote the aerobic glycolysis of osteosarcoma cells. In addition, the mitochondrial membrane potential experiment proved that the metabolic change triggered by PDGFRβ was not caused by mitochondrial damage. PI3K pathway inhibitor LY294002 or MEK pathway inhibitor U0126, or Warburg effect inhibitor DCA was used to carry out western blot and glucose metabolism experiments, and the results showed that PDGFBB/PDGFRβ mainly activated the PI3K/AKT/mTOR/ c-Myc pathway to promote aerobic glycolysis in osteosarcoma HOS cells. The newly elucidated role of PDGFRβ provides a novel metabolic therapeutic target for osteosarcoma.

Involvement of Long Non-Coding RNAs in Glucose Metabolism in Cancer

The rapid and uncontrolled proliferation of cancer cells is supported by metabolic reprogramming. Altered glucose metabolism supports cancer growth and progression. Compared with normal cells, cancer cells show increased glucose uptake, aerobic glycolysis and lactate production. Byproducts of adjusted glucose metabolism provide additional benefits supporting hallmark capabilities of cancer cells. Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts of more than 200 nucleotides in length. They regulate numerous cellular processes, primarily through physical interaction with other molecules. Dysregulated lncRNAs are involved in all hallmarks of cancer including metabolic alterations. They may upregulate metabolic enzymes, modulate the expression of oncogenic or tumor-suppressive genes and disturb metabolic signaling pathways favoring cancer progression. Thus, lncRNAs are not only potential clinical biomarkers for cancer diagnostics and prediction but also possible therapeutic targets. This review summarizes the lncRNAs involved in cancer glucose metabolism and highlights their underlying molecular mechanisms.