Long non-coding RNA SOS1-IT1 promotes endometrial cancer progression by regulating hypoxia signaling pathway

Construction and Validation of a Prognostic Risk Prediction Model for Lactate Metabolism-Related lncRNA in Endometrial Cancer

Endometrial cancer (EC) is a common group of malignant epithelial tumors that mainly occur in the female endometrium. Lactate is a key regulator of signal pathways in normal and malignant tissues. However, there is still no research on lactate metabolism-related lncRNA in EC. Here, we intended to establish a prognostic risk model for EC based on lactate metabolism-related lncRNA to forecast the prognosis of EC patients. First, we found that 38 lactate metabolism-associated lncRNAs were significantly overall survival through univariate Cox regression analysis. Using minimum absolute contraction and selection operator (LASSO) regression analysis and multivariate Cox regression analysis, six lactate metabolism-related lncRNAs were established as independent predictor in EC patients and were used to establish a prognostic risk signature. We next used multifactorial COX regression analysis and receiver operating characteristic (ROC) curve analysis to confirm that risk score was an independent prognostic factor of overall patient survival. The survival time of patients with EC in different high-risk populations was obviously related to clinicopathological factors. In addition, lactate metabolism-related lncRNA in high-risk population participated in multiple aspects of EC malignant progress through Gene Set Enrichment Analysis, Genomes pathway and Kyoto Encyclopedia of Genes and Gene Ontology. And risk scores were strongly associated with tumor mutation burden, immunotherapy response and microsatellite instability. Finally, we chose a lncRNA SRP14-AS1 to validate the model we have constructed. Interestingly, we observed that the expression degree of SRP14-AS1 was lower in tumor tissues of EC patients than in normal tissues, which was consistent with our findings in the TCGA database. In conclusion, our study constructed a prognostic risk model through lactate metabolism-related lncRNA and validated the model, confirming that the model can be used to predict the prognosis of EC patients and providing a molecular analysis of potential prognostic lncRNA for EC.

Identification and validation of KIF23 as a hypoxia-regulated lactate metabolism-related oncogene in uterine corpus endometrial carcinoma

AIMS

The "Warburg effect" has been developed from the discovery that hypoxia-inducible factor 1α (HIF-1α) could promote the conversion of pyruvate to lactate. However, no studies have linked hypoxia and lactate metabolism to uterine corpus endometrial carcinoma (UCEC).

MAIN METHODS

Sequencing and clinical data of patients with UCEC were extracted from The Cancer Genome Atlas (TCGA) database. Hypoxia-related lactate metabolism genes (HRLGs) were screened using Spearman's correlation analysis. A prognostic signature based on HRLGs was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. A comprehensive analysis was conducted on the molecular features, immune environment, mutation patterns, and response to drugs between different risk groups. In vitro and in vivo experiments were performed to verify the function of KIF23.

KEY FINDINGS

A five HRLG-based prognostic signature was identified. The prognostic outcome was unfavorable for the high-risk subgroup. Observation of increased pathway activities associated with cell proliferation and DNA damage repair was noted in the high-risk subgroup. Additionally, notable correlations were observed between risk score and immune microenvironment, mutational features, and drug responsiveness. Further, we confirmed KIF23 as a novel oncogene in UCEC, whose silencing decreased proliferation and promoted apoptosis of cancer cells. KIF23 knockdown reduced tumor growth in nude mice. We demonstrated that KIF23 was upregulated under hypoxic stress in a HIF-1α dependent manner. Moreover, KIF23 regulated lactate dehydrogenase A expression.

SIGNIFICANCE

The developed HRLG-related signature was associated with prognosis, immune microenvironment, and drug sensitivity in UCEC. We also revealed KIF23 as a hypoxia-regulated lactate metabolism-related oncogene.

The strict regulation of HIF-1α by non-coding RNAs: new insight towards proliferation, metastasis, and therapeutic resistance strategies

The hypoxic environment is prominently witnessed in most solid tumors and is associated with the promotion of cell proliferation, epithelial-mesenchymal transition (EMT), angiogenesis, metabolic reprogramming, therapeutic resistance, and metastasis of tumor cells. All the effects are mediated by the expression of a transcription factor hypoxia-inducible factor-1α (HIF-1α). HIF-1α transcriptionally modulates the expression of genes responsible for all the aforementioned functions. The stability of HIF-1α is regulated by many proteins and non-coding RNAs (ncRNAs). In this article, we have critically discussed the crucial role of ncRNAs [such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), Piwi-interacting RNAs (piRNAs), and transfer RNA (tRNA)-derived small RNAs (tsRNAs)] in the regulation of stability and expression of HIF-1α. We have comprehensively discussed the molecular mechanisms and relationship of HIF-1α with each type of ncRNA in either promotion or repression of human cancers and therapeutic resistance. We have also elaborated on ncRNAs that are in clinical examination for the treatment of cancers. Overall, the majority of aspects concerning the relationship between HIF-1α and ncRNAs have been discussed in this article.

Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs

Purpose

Cervical cancer (CC) has the fourth highest incidence and mortality rate among female cancers. Lactate is a key regulator promoting tumor progression. Long non-coding RNAs (lncRNAs) are closely associated with cervical cancer (CC). The study was aimed to develop a prognostic risk model for cervical cancer based on lactate metabolism-associated lncRNAs and to determine their clinical prognostic value.

Patients and Methods

In this study, CESC transcriptome data were obtained from the TCGA database. 262 lactate metabolism-associated genes were extracted from MsigDB (Molecular Characterization Database). Then, correlation analysis was used to identify LRLs. Univariate Cox regression analysis was performed afterwards, followed by least absolute shrinkage and selection operator (LASSO) regression analysis and multiple Cox regression analysis. 10 lncRNAs were finally identified to construct a risk score model. They were divided into two groups of high risk and low risk according to the median of risk scores. The predictive performance of the models was assessed by Kaplan-Meier (K-M) analysis, subject work characteristics (ROC) analysis, and univariate and multivariate Cox analyses. To assess the clinical utility of the prognostic model, we performed functional enrichment analysis, immune microenvironment analysis, mutation analysis, and column line graph generation.

Results

We constructed a prognostic model consisting of 10 LRLs at CC. We observed that high-risk populations were strongly associated with poor survival outcomes. Risk score was an independent risk factor for CC prognosis and was strongly associated with immune microenvironment analysis and tumor mutational load.

Conclusion

We developed a risk model of lncRNAs associated with lactate metabolism and used it to predict prognosis of CC, which could guide and facilitate the progress of new treatment strategies and disease monitoring in CC patients.

Construction and validation of N6-methyladenosine long non-coding RNAs signature of prognostic value for early biochemical recurrence of prostate cancer

PURPOSE

Early biochemical recurrence (eBCR) indicated a high risk for potential recurrence and metastasis in prostate cancer. The N6-methyladenosine (m6A) methylation modification played an important role in prostate cancer progression. This study aimed to develop a m6A lncRNA signature to accurately predict eBCR in prostate cancer.

METHODS

Pearson correlation analysis was first conducted to explore m6A lncRNAs and univariate Cox regression analysis was further performed to identify m6A lncRNAs of prognostic roles for predicting eBCR in prostate cancer. The m6A lncRNA signature was constructed by least absolute shrinkage and selection operator analysis (LASSO) in training cohort and further validated in test cohort. Furthermore, half maximal inhibitory concentration (IC50) values were utilized to explore potential effective drugs for high-risk group in this study.

RESULTS

Five hundred and thirty-eighth m6A lncRNAs were searched out through Pearson correlation analysis and 25 out of 538 m6A lncRNAs were identified to pose prediction roles for eBCR in prostate cancers. An m6A lncRNA signature including 5 lncRNAs was successfully built in training cohort. The high-risk group derived from m6A lncRNA signature could efficiently predict eBCR occurrence in both training (p < 0.001) and test cohort (p = 0.002). ROC analysis also confirmed that lncRNA signature in this study posed more accurate prediction roles for eBCR occurrence when compared with PSA, TNM stages and Gleason scores. Drug sensitivity analysis further discovered that various drugs could be potentially utilized to treat high-risk samples in this study.

CONCLUSIONS

The m6A lncRNA signature in this study could be utilized to efficiently predict eBCR occurrence, various clinical characteristic and immune microenvironment for prostate cancer.