Unlocking the Potential of Disulfidptosis-Related LncRNAs in Lung Adenocarcinoma: A Promising Prognostic LncRNA Model for Survival and Immunotherapy Prediction.
Disulfidptosis was stimulated in high SLC7A11 expression cells starving to glucose. We attempted to identify disulfidptosis-related lncRNAs (DRLs), built a prognostic model to predict survival, and analyzed the tumor microenvironment.
The TCGA database was utilized to procure the pertinent data. By utilizing both Cox regression and the least absolute shrinkage and selection operator (LASSO) method, a risk model based on DRLs was formulated for prognostic evaluation. The ability of survival prediction was validated by multiple approaches. The biological functions were screened through GO, KEGG, and GSEA. Various methods were employed to evaluate the tumor immune environment, which included ESTIMATE, tumor mutation burden (TMB) score, CIBERSORT algorithm, and tumor immune dysfunction and exclusion (TIDE) score.
Ninety-one DRLs were recognized, and lncRNA AC092718.4, AL365181.2, AL606489.1, EMSLR, and ENTPD3-AS1 were involved in the risk model. The GEO database was used to verify the influence of these lncRNAs on survival. The following analyses showed that survival could be predicted excellently by the DRLs risk model. The results of enrichment analyses pointed toward the involvement of the cell cycle and IgA production pathways. In the low-risk patient group, there was a notable surge in stromal, immune, and ESTIMATE scores, while the TMB scores took a tumble. Conversely, the high-risk patient group displayed a converse trend. Notably, the group of patients with lower risk scores and higher TMB scores showed the most favorable survival outcomes, underscoring the importance of considering both risk score and TMB in predicting the response to immune checkpoint blockade therapy. Furthermore, patients classified as high-risk might display resistance to both chemotherapy and targeted therapy. Cellular biological experiments proved that lncRNA AC092718.4 promoted invasion, migration, and proliferation abilities in vitro. These results provided valuable insights into the role of DRLs in LUAD and presented a possible effective treatment approach for LUAD.
We developed a disulfidptosis-related risk model with 5 lncRNAs that enables survival prediciton for LUAD patients and aids cilinical decisions by forecasting the TME, TMB, and drug sensitivity, making it a valuable tool for outcomes prediction.
Nie X
，Ge H
，Wu K
，Liu R
，He C
... -
《Cancer Medicine》
A methylation-related lncRNA-based prediction model in lung adenocarcinomas.
The collaboration between methylation and the lung adenocarcinoma (LUAD) occurrence and development is closes. Long noncoding RNA (lncRNA), as a regulatory factor of various biological functions, can be used for cancer diagnosis. Our study aimed to construct a robust methylation-related lncRNA signature of LUAD.
In the Cancer Genome Atlas (TCGA) dataset, we download the RNA expression data and clinical information of LUAD cases. To develop the best prognostic signature based on methylation-related lncRNAs, Cox regression analyses were utilized. Using Kaplan-Meier analysis, overall survival rates were compared between risk category included both low- and high-risk patients. To categorize genes according to their functional significance, GSEA (Subramanian et al, 2005) was used. Single-sample gene set enrichment analysis (ssGSEA) was used to further reveal the potential molecular mechanism of the methylation-related lncRNA prognostic model in immune infiltration. Using TRLnc (http://www.licpathway.net/TRlnc) and lncRNASNP to analyse the SNP sites and TRLnc of these 18 lncRNAs. LncSEA website was used to analyse 18 lncRNA in the process of tumour development and development. Go was used to analyse the enriched pathways enriched by TFs (transcription factors), Cerna networks, and proteins bound to each other of these 18 lncRNAs. The 'prophetic' package was used to analyse the value of this prognostic model in guiding personalized immunotherapy.
In this study, we identified 18 methylation-related lncRNAs (AP002761.1, AL118558.3, CH17-340M24.3, AL353150.1, AC004687.1, LINC00996, AF186192.1, HSPC324, AC087752.3, FAM30A, AC106047.1, AC026355.1, ABALON, LINC01843, AL606489.1, NKILA, AP001453.2, GSEC) to establish a methylation-related lncRNA signature that can detect patients prognosis in LUAD. The enriched pathways enriched by proteins interacting with 18 lncRNAs are mainly EMT, hypoxia, stemness and proliferation, among which LINC00996 and AF186192.1 are regulated by multiple tumour associated transcription factors, such as TP53 and TP63, and fam30a and mRNA form a Cerna network. There are 2319 SNP loci in LINC00996, 36 of which are risk SNP loci and 205 SNP loci in af186192.1; AF186192.1 affects 95 conserved miRNAs and 123 non-conserved miRNAs, promotes the binding of 149 pairs of miRNAs: lncRNAs and inhibits the binding of 95 pairs of miRNAs: lncRNAs. The ROC curve demonstrated that the established methylation-related lncRNA signature was more effective in predicting the prognosis of patients in LUAD than the clinicopathological parameters. Our research has confirmed that patients in the high-risk group which was separated by the risk score model based on methylation-related lncRNA had shorter OS. According to GSEA, the high-risk group had a predominantly tumour- and immune-related pathway enrichment. A significant association was shown by ssGSEA between predictive signature and immune status in LUAD patients. In addition, principal component analysis (PCA) demonstrated the prognostic and predictive value of our signature. The correlation between the predictive signature of methylation-related lncRNA and IC50 of conventional chemotherapy drugs can provide personalized chemotherapy regimens for LUAD patients. Methylation-related lncRNA signature can effectively predict DFS of patients in LUAD.
Yang K
，Liu H
，Li JH
《-》
Development of m6A/m5C/m1A regulated lncRNA signature for prognostic prediction, personalized immune intervention and drug selection in LUAD.
Research indicates that there are links between m6A, m5C and m1A modifications and the development of different types of tumours. However, it is not yet clear if these modifications are involved in the prognosis of LUAD. The TCGA-LUAD dataset was used as for signature training, while the validation cohort was created by amalgamating publicly accessible GEO datasets including GSE29013, GSE30219, GSE31210, GSE37745 and GSE50081. The study focused on 33 genes that are regulated by m6A, m5C or m1A (mRG), which were used to form mRGs clusters and clusters of mRG differentially expressed genes clusters (mRG-DEG clusters). Our subsequent LASSO regression analysis trained the signature of m6A/m5C/m1A-related lncRNA (mRLncSig) using lncRNAs that exhibited differential expression among mRG-DEG clusters and had prognostic value. The model's accuracy underwent validation via Kaplan-Meier analysis, Cox regression, ROC analysis, tAUC evaluation, PCA examination and nomogram predictor validation. In evaluating the immunotherapeutic potential of the signature, we employed multiple bioinformatics algorithms and concepts through various analyses. These included seven newly developed immunoinformatic algorithms, as well as evaluations of TMB, TIDE and immune checkpoints. Additionally, we identified and validated promising agents that target the high-risk mRLncSig in LUAD. To validate the real-world expression pattern of mRLncSig, real-time PCR was carried out on human LUAD tissues. The signature's ability to perform in pan-cancer settings was also evaluated. The study created a 10-lncRNA signature, mRLncSig, which was validated to have prognostic power in the validation cohort. Real-time PCR was applied to verify the actual manifestation of each gene in the signature in the real world. Our immunotherapy analysis revealed an association between mRLncSig and immune status. mRLncSig was found to be closely linked to several checkpoints, such as IL10, IL2, CD40LG, SELP, BTLA and CD28, which could be appropriate immunotherapy targets for LUAD. Among the high-risk patients, our study identified 12 candidate drugs and verified gemcitabine as the most significant one that could target our signature and be effective in treating LUAD. Additionally, we discovered that some of the lncRNAs in mRLncSig could play a crucial role in certain cancer types, and thus, may require further attention in future studies. According to the findings of this study, the use of mRLncSig has the potential to aid in forecasting the prognosis of LUAD and could serve as a potential target for immunotherapy. Moreover, our signature may assist in identifying targets and therapeutic agents more effectively.
Ma C
，Gu Z
，Yang Y
《-》
ResearchGate
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钛学术
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