Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis

LINC00894, YEATS2-AS1, and SUGP2 genes as novel biomarkers for N0 status of lung adenocarcinoma

Research on genes affecting tumors without lymph node metastasis is limited, hence this study employed both bioinformatic and experimental approaches to identify specific genes associated with lung cancer adenocarcinoma (LUAD) before lymph node metastasis occurs. Expression profiles of mRNAs and lncRNAs and LUAD clinical data were downloaded from the Cancer Genome Atlas (TCGA) using R software to identify differentially expressed genes (DEGs) associated with N0 and N + status. TargetScan, miRTarBase, and miRDB databases were used to identify interactions between miRNAs and mRNAs. The DIANA database and lncBase tool were used to find the association between lncRNA and miRNA. After selecting some genes, the expression of candidate genes was confirmed by real-time RT-PCR technique. Following the knockdown LINC00894 gene using the shRNA technique, its effect on invasion, migration, and apoptosis in the calu-3 cell line was investigated. In total, we found 321 specific DEGs not only in N0 vs. normal but also in N0 Vs. N + in LUAD, most of which were lncRNA and we identified a ceRNA network containing nine lncRNAs with the highest degree of connectivity. Among them, in addition to bioinformatic analyses, LINC00894 and YEATS2-AS1 were significantly increased in tumor tissues compared to normal tissues (P = 0.0001). also, SUGP2 that was shared in both lncRNA-related ceRNA subnetworks was significantly upregulated (P = 0.0001). Additionally, following the knockdown of LINC00894 in Calu-3 cell line, a significant decrease in migration and invasion was observed, but early apoptosis was significantly increased in the shLINC00894(48 h) group (P = 0.007). The findings of the present study show that lncRNAs play an important role in the N0 status of LUAD. Moreover, LINC00894, YEATS2-AS1, and SUGP2 can act as biomarkers in patients with N0 LUAD.

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

m6A-related long noncoding RNAs predict prognosis and indicate therapeutic response in endometrial carcinoma

BACKGROUND

N6-methyladenosine (m6A) has been identified as the most common, abundant, and conserved internal transcriptional modification. Long noncoding RNAs (lncRNAs) are noncoding RNAs consisting of more than 200 nucleotides, and the expression of various lncRNAs may affect cancer prognosis. The impact of m6A-associated lncRNAs on uterine corpus endometrial carcinoma (UCEC) prognosis is unknown.

METHODS

In this study, UCEC prognosis-related m6A lncRNAs were screened, bioinformatics analysis was performed, and experimental validation was conducted. Endometrial carcinoma (EC) and normal tissue samples were obtained from The Cancer Genome Atlas. The prognosis-related m6A lncRNAs screened by the least absolute shrinkage and selection operator method were used for multivariate Cox proportional risk regression modeling. Principal component analysis and Gene Ontology, immune function difference, and drug sensitivity analyses of the prognostic models were performed. Prognostic analysis was conducted for m6A-associated lncRNAs. The immune infiltration relationship of m6A-associated lncRNAs in EC was identified using the ssGSEA immune infiltration algorithm. A competing endogenouse RNA network was constructed using the LncACTdb database. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) assays were used to validate the differences in m6A-related lncRNA expression in normal and EC cells.

RESULTS

CDKN2B-AS1 and MIR924HG were found to be risk factors for EC. RAB11B-AS1 was a protective factor in EC patients. MIR924HG expression was upregulated in KLE and RL95-2 endometrial cancer cell lines. Prognostic models involved RAB11B-AS1, LINC01812, HM13-IT1, TPM1-AS, SLC16A1-AS1, LINC01936, and CDKN2B-AS1. The high-risk group was more sensitive to five compounds (ABT.263, ABT.888, AP.24534, ATRA, and AZD.0530) than the low-risk group.

CONCLUSION

These findings contribute to understanding of the function of m6A-related lncRNAs in UCEC and provide promising therapeutic strategies for UCEC.

A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia

Background

Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML).

Methods

RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated.

Results

Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents.

Conclusion

A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.

The integration of single-cell sequencing, TCGA, and GEO data analysis revealed that PRRT3-AS1 is a biomarker and therapeutic target of SKCM

INTRODUCTION

Skin cutaneous melanoma (SKCM) is the world's fourth deadliest cancer, and advanced SKCM leads to a poor prognosis. Novel biomarkers for SKCM diagnosis and prognosis are urgently needed. Long non-coding RNAs (lncRNAs) provide various biological functions and have been proved to play a significant role in tumor progression. Single-cell RNA sequencing (scRNA-seq) enables genome analysis at the single-cell level. This study explored prognostic lncRNAs in SKCM based on scRNA-seq and bulk RNA sequencing data.

MATERIALS AND METHODS

The TCGA cohort and melanoma samples in the GEO database (GSE72056, GSE19234, GSE15605, GSE7553, and GSE81383) were included in this study. Marker genes were filtered, and ensemble lncRNAs were annotated. The clinical significance of selected lncRNAs was verified through TCGA and GEO dataset analysis. SiRNA transfection, wound-healing and transwell assays were performed to evaluate the effect of PRRT3-AS1 on cellular function. Immune infiltration of the selected lncRNAs was also exhibited.

RESULTS

A 5-marker-lncRNAs model of significant prognostic value was constructed based on GSE72056 and the TCGA cohort. PRRT3-AS1 combined with DANCR was then found to provide significant prognostic value in SKCM. PRRT3-AS1 was filtered for its higher expression in more advanced melanoma and significant prognosis value. Cellular function experiments in vitro revealed that PRRT3-AS1 may be required for cancer cell migration in SKCM. PRRT3-AS1 was found to be related to epithelial-mesenchymal transition (EMT) signaling pathways. DNA methylation of PRRT3-AS1 was negatively related to PRRT3-AS1 expression and showed significant prognosis value. In addition, PRRT3-AS1 may suppress immune infiltration and be involved in immunotherapy resistance.

CONCLUSION

PRRT3-AS1 may be a diagnostic and prognostic biomarker of SKCM.

The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis

Objective. N7-methylguanosine modification-related lncRNAs (m7G-related lncRNAs) are involved in progression of many diseases. This study was aimed at revealing the risk correlation between N7-methylguanosine modification-related lncRNAs and survival prognosis of oral squamous cell carcinoma. Methods. In the present study, coexpression network analysis and univariate Cox analysis were used to obtained 31 m7G-related mRNAs and 399 m7G-related lncRNAs. And the prognostic risk score model of OSCC patients was evaluated and optimized through cross-validation. Results. Through the coexpression analysis and risk assessment analysis of m7G-related prognostic mRNAs and lncRNAs, it was found that six m7G-related prognostic lncRNAs (AC005332.6, AC010894.1, AC068831.5, AL035446.1, AL513550.1, and HHLA3) were high-risk lncRNAs. Three m7G-related prognostic lncRNAs (AC007114.1, HEIH, and LINC02541) were protective lncRNAs. Then, survival curves were drawn by comparing the survival differences between patients with high and low expression of each m7G-related prognostic lncRNA in the prognostic risk score model. Further, risk curves, scatter plots, and heat maps were drawn by comparing the survival differences between high-risk and low-risk OSCC patients in the prognostic model. Finally, forest maps and the ROC curve were generated to verify the predictive power of the prognostic risk score model. Our results will help to find early and accurate prognostic risk markers for OSCC, which could be used for early prediction and early clinical intervention of survival, prognosis, and disease risk of OSCC patients in the future.