Identification of Novel Prognostic Signatures for Clear Cell Renal Cell Carcinoma Based on ceRNA Network Construction and Immune Infiltration Analysis

Relationship between lncRNA MALAT1 and Chemo-radiotherapy Resistance of Cancer Cells: Uncovered Truths

The advancement of novel technologies, coupled with bioinformatics, has led to the discovery of additional genes, such as long noncoding RNAs (lncRNAs), that are associated with drug resistance. LncRNAs are composed of over 200 nucleotides and do not possess any protein coding function. These lncRNAs exhibit lower conservation across species, are typically expressed at low levels, and often display high specificity towards specific tissues and developmental stages. The LncRNA MALAT1 plays crucial regulatory roles in various aspects of genome function, encompassing gene transcription, splicing, and epigenetics. Additionally, it is involved in biological processes related to the cell cycle, cell differentiation, development, and pluripotency. Recently, MALAT1 has emerged as a novel mechanism contributing to drug resistance or sensitivity, attracting significant attention in the field of cancer research. This review aims to explore the mechanisms through which MALAT1 confers resistance to chemotherapy and radiotherapy in cancer cells.

Multi-cohort validation: A comprehensive exploration of prognostic marker in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common form of RCC. It is characterized by resistance to traditional radiotherapy and chemotherapy, as well as an unfavorable clinical prognosis. Although TYMP is implicated in the advancement of tumor progression, the role of TYMP in ccRCC is still not understood. Heightened TYMP expression was identified in ccRCC through database mining and confirmed in RCC cell lines. Indeed, TYMP knockdown impacted RCC cell proliferation, migration, and invasion in vitro. TYMP showed a positive correlation with clinicopathological parameters (histological grade, pathological stage). Moreover, patients with high TYMP expression were indicative of poor prognosis in TCGA-ccRCC and external cohorts. The results of single-cell analysis showed that the distribution of TYMP was predominantly observed in monocytes and macrophages. Furthermore, there is a significant association between TYMP and immune status. Methylation analysis further elucidated the relationship between TYMP expression and multiple methylation sites. Drug sensitivity analysis unveiled potential pharmaceutical options. Additionally, mutation analyses identified an association between TYMP and the ccRCC driver genes like BAP1 and ROS1. In summary, TYMP may serve as a reliable prognostic indicator for ccRCC.

Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network

Clear-cell renal-cell carcinoma (ccRCC) is a silent-development pathology with a high rate of metastasis in patients. The activity of coding genes in metastatic progression is well known. New studies evaluate the association with non-coding genes, such as competitive endogenous RNA (ceRNA). This study aims to build a ceRNA network and a gene signature for ccRCC associated with metastatic development and analyze their biological functions. Using data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with differentially expressed genes, assembled nine preliminary gene signatures from eight feature selection techniques, and evaluated the classification metrics to choose a final signature. After that, we performed a genomic analysis, a risk analysis, and a functional annotation analysis. We present an 11-gene signature: SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, and RASD1. It was possible to assess the generalization of the signature using an external dataset from the International Cancer Genome Consortium (ICGC-RECA), which showed an Area Under the Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions. The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 were significantly related to the patient's survival and metastatic development. Additionally, functional annotation resulted in relevant pathways for tumor development and cell cycle control, such as RNA polymerase II transcription regulation and cell control. The gene signature analysis within the ceRNA network, with literature evidence, suggests that the lncRNAs act as "sponges" upon the microRNAs (miRNAs). Therefore, this gene signature presents coding and non-coding genes and could act as potential biomarkers for a better understanding of ccRCC.

Mapping the tumor microenvironment in clear cell renal carcinoma by single-cell transcriptome analysis

Introduction: Clear cell renal cell carcinoma (ccRCC) is associated with unfavorable clinical outcomes. To identify viable therapeutic targets, a comprehensive understanding of intratumoral heterogeneity is crucial. In this study, we conducted bioinformatic analysis to scrutinize single-cell RNA sequencing data of ccRCC tumor and para-tumor samples, aiming to elucidate the intratumoral heterogeneity in the ccRCC tumor microenvironment (TME). Methods: A total of 51,780 single cells from seven ccRCC tumors and five para-tumor samples were identified and grouped into 11 cell lineages using bioinformatic analysis. These lineages included tumor cells, myeloid cells, T-cells, fibroblasts, and endothelial cells, indicating a high degree of heterogeneity in the TME. Copy number variation (CNV) analysis was performed to compare CNV frequencies between tumor and normal cells. The myeloid cell population was further re-clustered into three major subgroups: monocytes, macrophages, and dendritic cells. Differential expression analysis, gene ontology, and gene set enrichment analysis were employed to assess inter-cluster and intra-cluster functional heterogeneity within the ccRCC TME. Results: Our findings revealed that immune cells in the TME predominantly adopted an inflammatory suppression state, promoting tumor cell growth and immune evasion. Additionally, tumor cells exhibited higher CNV frequencies compared to normal cells. The myeloid cell subgroups demonstrated distinct functional properties, with monocytes, macrophages, and dendritic cells displaying diverse roles in the TME. Certain immune cells exhibited pro-tumor and immunosuppressive effects, while others demonstrated antitumor and immunostimulatory properties. Conclusion: This study contributes to the understanding of intratumoral heterogeneity in the ccRCC TME and provides potential therapeutic targets for ccRCC treatment. The findings emphasize the importance of considering the diverse functional roles of immune cells in the TME for effective therapeutic interventions.

Nonsense-Mediated Decay Targeted RNA (ntRNA): Proposal of a ntRNA–miRNA–lncRNA Triple Regulatory Network Usable as Biomarker of Prognostic Risk in Patients with Kidney Cancer

The most prevalent subtype of renal cell carcinoma (RCC), kidney renal clear cell carcinoma (KIRC) may be associated with a poor prognosis in a high number of cases, with a stage-specific prognostic stratification currently in use. No reliable biomarkers have been utilized so far in clinical practice despite the efforts in biomarker research in the last years. Nonsense-mediated mRNA decay (NMD) is a critical safeguard against erroneous transcripts, particularly mRNA transcripts containing premature termination codons (called nonsense-mediated decay targeted RNA, ntRNA). In this study, we first characterized 296 differentially expressed ntRNAs that were independent of the corresponding gene, 261 differentially expressed miRNAs, and 4653 differentially expressed lncRNAs. Then, we constructed a hub ntRNA–miRNA–lncRNA triple regulatory network associated with the prognosis of KIRC. Moreover, the results of immune infiltration analysis indicated that this network may influence the changes of the tumor immune microenvironment. A prognostic model derived from the genes and immune cells associated with the network was developed to distinguish between high- and low-risk patients, which was a better prognostic than other models, constructed using different biomarkers. Additionally, correlation of methylation and ntRNAs in the network suggested that some ntRNAs were regulated by methylation, which is helpful to further study the causes of abnormal expression of ntRNAs. In conclusion, this study highlighted the possible clinical implications of ntRNA functions in KIRC, proposing potential significant biomarkers that could be utilized to define the prognosis and design personalized treatment plans in kidney cancer management in the next future.

Identification of a Novel Tumor Inflammation Signature for Risk Stratification, Prognosis Prediction, and Immune Status in Colorectal Cancer

Background

Inflammation and immune cell dysfunction have been widely known as an essential role in the tumorigenesis of colorectal cancer (CRC). Yet, the role of tumor inflammation signature (TIS) associated with CRC prognosis, immune infiltration, and drug resistance remained unknown.

Method

The transcriptome sequencing data, as well as clinical data of CRC from the public dataset, were acquired for further investigation. Inflammation-related gene expression patterns were obtained and analyzed. Bioinformatics methods were used to build a prognostic TIS, and its prediction accuracy was verified by using ROC curve analyses. The independent prognostic factors in CRC were identified through multivariable Cox regression analysis. In addition, the specific features of the immunological landscape between low- and high-risk CRC cohorts were analyzed.

Results

We firstly screened the differentially expressed inflammation-related genes in CRC and constructed a prognostic TIS. We further classified CRC patients into high or low TIS score groups based on the optimal cutoff of prognostic TIS, and patients with high-risk scores had shorter overall survival (OS) than those in the low-risk cohort. The diagnostic accuracy of TIS was evaluated and confirmed with ROC analysis. The result of the univariate and multivariate analysis found that TIS was directly and independently linked to OS of CRC. Otherwise, an optimal nomogram model based on TIS exhibited a better prognostic accuracy in OS. Finally, the immunological status and immune cell infiltration were observed different in the two-risk cohorts.

Conclusion

In summary, the risk model of the TIS in CRC was found to be useful for predicting patient prognosis and guiding individual treatment. This risk signature could also serve as potential biomarkers and immunotherapeutic targets and indicate immunotherapy response for patients with CRC.