Long Non-Coding RNAs as Novel Biomarkers in the Clinical Management of Papillary Renal Cell Carcinoma Patients: A Promise or a Pledge?

Interplay between LncRNAs and autophagy-related pathways in leukemia: mechanisms and clinical implications

Autophagy is a conserved catabolic process that removes protein clumps and defective organelles, thereby promoting cell equilibrium. Growing data suggest that dysregulation of the autophagic pathway is linked to several cancer hallmarks. Long non-coding RNAs (lncRNAs), which are key parts of gene transcription, are increasingly recognized for their significant roles in various biological processes. Recent studies have uncovered a strong connection between the mutational landscape and altered expression of lncRNAs in the tumor formation and development, including leukemia. Research over the past few years has emphasized the role of lncRNAs as important regulators of autophagy-related gene expression. These RNAs can influence key leukemia characteristics, such as apoptosis, proliferation, epithelial-mesenchymal transition (EMT), migration, and angiogenesis, by modulating autophagy-associated signaling pathways. With altered lncRNA expression observed in leukemia cells and tissues, they hold promise as diagnostic biomarkers and therapeutic targets. The current review focuses on the regulatory function of lncRNAs in autophagy and their involvement in leukemia, potentially uncovering valuable therapeutic targets for leukemia treatment.

Identification of BCL11A, NTN5, and OGN as Diagnosis Biomarker of Papillary Renal Cell Carcinomas by Bioinformatic Analysis

The prevalence of papillary renal cell carcinomas (PRCCs) is estimated to be between 10% and 15%. At present, there is no effective therapeutic approach available for patients with advanced PRCCs. The molecular biomarkers associated with PRCC diagnoses have been rarely studied compared to renal clear cell carcinomas; therefore, the necessity for the identification of novel molecular biomarkers to aid in the early identification of this disease. Bioinformatics and artificial intelligence technologies have become increasingly important in the search for diagnostic biomarkers for early cancer detection. In this study, three genes-BCL11A, NTN5, and OGN-were identified as diagnostic biomarkers using the Cancer Genome Atlas (TCGA) database and deep learning techniques. To identify the differential expression genes (DEGs), ribonucleic acid (RNA) expression profiles of PRCC patients were analyzed using a machine learning approach. A number of molecular pathways and coexpressions of DEGs have been analyzed and a correlation between DEGs and clinical data has been determined. Diagnostic markers were then determined via machine learning analysis. The 10 genes selected with the highest variable importance value (more than 0.9) were further investigated, with six upregulated (BCL11A, NTN5, SEL1L3, SKA3, TAPBP, SEMA6A) and four downregulated (OGN, ADCY4, SMOC2, CCL23). A combined receiver operating characteristic (ROC) curve analysis revealed that the BCL11A-NTN5-OGN genes, which have specificity and sensitivity values of 0.968 and 0.901, respectively, can be used as a diagnostic biomarker for PRCC. In general, the genes introduced in this study may be used as diagnostic biomarkers for the early diagnosis of PRCC, thus providing the possibility of early treatment and preventing the progression of the disease.

Development and validation of a prognostic model based on m6A-related lncRNAs to predict prognosis for papillary renal cell cancer patients

To evaluate the predictive utility of N6-methyladenosine (m6A)-associated long non-coding RNAs (lncRNAs) for the prognosis and immunotherapy response in papillary renal cell carcinoma (pRCC). Transcriptomic data of pRCC samples were extracted from the TCGA database. The m6A-related lncRNAs were identified by Pearson correlation analysis. Univariate and LASSO regression analyses were used to develop a risk model. The discrimination and predictive ability were evaluated through survival analysis, ROC analysis and consensus clustering. Tumor mutation burden (TMB) and immune infiltration of the risk groups were compared. A prognostic nomogram was constructed using six m6A-related lncRNAs, and validated through calibration and decision curve analysis (DCA). The lncRNAs HCG25 and NOP14-AS1 were knocked down in a human pRCC cell line using specific siRNA constructs, and the proliferation and migration rates were assessed by the CCK-8 and transwell assays. We identified a total of 153 m6A-related lncRNAs in pRCC datasets, of which six were selected for constructing a m6A-related lncRNA pRCC prognostic model. Mutations in the SETD2 gene correlated with worse prognosis. Significant differences were observed in immune cell infiltration between the two risk groups. A clinical prognostic nomogram for pRCC was further established based on clinical variables. In vitro assays further showed that HCG25 and NOP14-AS1 regulate the proliferation and migration of pRCC cells. The results validated the discrimination ability of both the m6A-related lncRNA pRCC prognostic model and the pRCC clinical prognostic nomogram. We developed a clinical prognostic nomogram for pRCC using pRCC prognostic-associated m6A-related lncRNAs, which can be utilized for predicting the prognosis and immune landscape of pRCC patients.

Advances in understanding the role of lncRNA in ferroptosis

LncRNA is a type of transcript with a length exceeding 200 nucleotides, which was once considered junk transcript with no biological function during the transcription process. In recent years, lncRNA has been shown to act as an important regulatory factor at multiple levels of gene expression, affecting various programmed cell death modes including ferroptosis. Ferroptosis, as a new form of programmed cell death, is characterized by a deficiency of cysteine or inactivation of glutathione peroxidase, leading to depletion of glutathione, aggregation of iron ions, and lipid peroxidation. These processes are influenced by many physiological processes, such as the Nrf2 pathway, autophagy, p53 pathway and so on. An increasing number of studies have shown that lncRNA can block the expression of specific molecules through decoy effect, guide specific proteins to function, or promote interactions between molecules as scaffolds. These modes of action regulate the expression of key factors in iron metabolism, lipid metabolism, and antioxidant metabolism through epigenetic or genetic regulation, thereby regulating the process of ferroptosis. In this review, we snapshotted the regulatory mechanism of ferroptosis as an example, emphasizing the regulation of lncRNA on these pathways, thereby helping to fully understand the evolution of ferroptosis in cell fate.

LINC02535 + miR-30a-5p combination enhances proliferation and inhibits apoptosis in metastatic breast Cancer cells

Current clinical therapies for metastatic breast cancer (MBC) have limited therapeutic efficacy and induce significant systemic side effects, leading to poor patient compliance. To address this challenge, this investigation focuses on the design of LINC02535 + miR-30a-5p for treating breast cancer. In vitro cytotoxicity studies confirmed that LINC02535 + miR-30a-5p was more effective in 4 T1 cells, with reduced toxicity in NIH3T3 cells. Further verification of cellular morphology was achieved through various biochemical staining methods. Additionally, the antimetastatic attributes of LINC02535 + miR-30a-5p have been evaluated using both migration scratch and Transwell migration assays, which have collectively revealed excellent antimetastatic potential. The DNA fragmentation of the 4 T1 cells was assessed using a comet assay. LINC02535 + miR-30a-5p improved ROS levels and caused mitochondrial membrane potential alterations and DNA damage, which resulted in apoptosis. Therefore, we propose that LINC02535 + miR-30a-5p could be an alternative therapeutic strategy for breast cancer therapy.

Ferroptosis-Related lncRNA to Predict the Clinical Outcomes and Molecular Characteristics of Kidney Renal Papillary Cell Carcinoma

Kidney renal papillary cell carcinoma (KIRP) is a highly heterogeneous type of kidney cancer, resulting in limited effective prognostic targets for KIRP patients. Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in the regulation of ferroptosis and iron metabolism, making them potential targets for the treatment and prognosis of KIRP. In this study, we constructed a ferroptosis-related lncRNA risk score model (FRM) based on the TCGA-KIRP dataset, which represents a novel subtype of KIRP not previously reported. The model demonstrated promising diagnostic accuracy and holds potential for clinical translation. We observed significant differences in metabolic activities, immune microenvironment, mutation landscape, ferroptosis sensitivity, and drug sensitivity between different risk groups. The high-risk groups exhibit significantly higher fractions of cancer-associated fibroblasts (CAFs), hematopoietic stem cells (HSC), and pericytes. Drugs (IC50) analysis provided a range of medication options based on different FRM typing. Additionally, we employed single-cell transcriptomics to further analyze the impact of immune invasion on the occurrence and development of KIRP. Overall, we have developed an accurate prognostic model based on the expression patterns of ferroptosis-related lncRNAs for KIRP. This model has the potential to contribute to the evaluation of patient prognosis, molecular characteristics, and treatment modalities, and can be further translated into clinical applications.

Construction of a cuproptosis-related lncRNA signature for predicting prognosis and immune landscape in osteosarcoma patients

BACKGROUND

Long noncoding RNAs (lncRNAs) influence the onset of osteosarcoma. Cuproptosis is a novel cell death mechanism. We attempted to identify a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape in osteosarcoma patients.

METHODS

Transcriptional and clinical data of 85 osteosarcoma patients were derived from the TARGET database and randomly categorized into the training and validation cohorts. We implemented the univariate and multivariate Cox regression, along with LASSO regression analyses for developing a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, C-index, ROC curves, univariate and multivariate Cox regression, and nomogram were used to assess the capacity of this risk model to predict the osteosarcoma prognosis. Gene ontology, KEGG, and Gene Set Enrichment (GSEA) analyses were conducted for determining the potential functional differences existing between the high-risk and low-risk patients. We further conducted the ESTIMATE, single-smaple GSEA, and CIBERSORT analyses for identifying the different immune microenvironments and immune cells infiltrating both the risk groups.

RESULTS

We screened out four cuproptosis-related lncRNAs (AL033384.2, AL031775.1, AC110995.1, and LINC00565) to construct the risk model in the training cohort. This risk model displayed a good performance to predict the overall survival of osteosarcoma patients, which was confirmed by using the validation and the entire cohort. Further analyses showed that the low-risk patients have more immune activation and immune cells infiltrating as well as a good response to immunotherapy.

CONCLUSIONS

We developed a novel cuproptosis-related lncRNA signature with high reliability and accuracy for predicting outcome and immunotherapy response in osteosarcoma patients, which provides new insights into the personalized treatment of osteosarcoma.

The Clinical and Molecular Features in the VHL Renal Cancers; Close or Distant Relatives with Sporadic Clear Cell Renal Cell Carcinoma?

Von Hippel-Lindau (VHL) disease is an autosomal dominant inherited cancer syndrome caused by germline mutations in the VHL tumor suppressor gene, characterized by the susceptibility to a wide array of benign and malign neoplasms, including clear-cell renal cell carcinoma. Moreover, VHL somatic inactivation is a crucial molecular event also in sporadic ccRCCs tumorigenesis. While systemic biomarkers in the VHL syndrome do not currently play a role in clinical practice, a new promising class of predictive biomarkers, microRNAs, has been increasingly studied. Lots of pan-genomic studies have deeply investigated the possible biological role of microRNAs in the development and progression of sporadic ccRCC; however, few studies have investigated the miRNA profile in VHL patients. Our review summarize all the new insights related to clinical and molecular features in VHL renal cancers, with a particular focus on the overlap with sporadic ccRCC.