Identification of lncRNA Signature Associated With Pan-Cancer Prognosis

LncRNA ALMS1-IT1 modulates ferroptosis and immune evasion in colorectal cancer through activating STAT3

Colorectal cancer (CRC) represents a significant malignancy within the digestive system, characterized by high incidence and mortality rates. In recent years, molecular targeted therapy has been introduced as a supplementary strategy in CRC management, complementing traditional modalities such as surgery, radiation and chemotherapy. The identification of novel therapeutic targets for CRC remains critically important. Ferroptosis, a unique form of programmed cell death distinct from apoptosis and necrosis, is characterized by cellular damage resulting from iron-induced lipid peroxidation, leading to cell death. This study utilizes a combination of bioinformatics analysis and clinical specimen validation to demonstrate that the long non-coding RNA (lncRNA) ALMS1-IT1 is significantly upregulated in CRC tissues and strongly associated with ferroptosis. Through a series of experimental investigations, we have determined that ALMS1-IT1 negatively regulates ferroptosis in CRC cells, thereby promoting cancer growth and metastasis, acting as an oncogenic factor. Furthermore, we explored the molecular interactions of ALMS1-IT1, revealing its role in activating STAT3 protein phosphorylation. This activation enhances the immune evasion capabilities of CRC cells. Rescue experiments indicated that STAT3 activation is essential for ALMS1-IT1's suppression of ferroptosis, immune evasion and oncogenic behaviour in CRC. Our findings underscore the critical biological role of ALMS1-IT1 in the progression of CRC and suggest its potential as a target for drug development.

LncRNA CTBP1-AS inhibits TP63-mediated activation of S100A14 during prostate cancer progression

Long noncoding RNAs (lncRNAs) have emerged as important molecules and potential new targets for human cancers. This study investigates the function of lncRNA CTBP1 antisense RNA (CTBP1-AS) in prostate cancer (PCa) and explores the entailed molecular mechanism. Aberrantly expressed genes potentially correlated with PCa progression were probed using integrated bioinformatics analyses. A cohort of 68 patients with PCa was included, and their tumor and para-cancerous tissues were collected. CTBP1-AS was highly expressed in PCa tissues and cells and associated with poor patient prognosis. By contrast, tumor protein p63 (TP63) and S100 calcium binding protein A14 (S100A14) were poorly expressed in the PCa tissues and cells. CTBP1-AS did not affect TP63 expression; however it blocked the TP63-mediated transcriptional activation of S100A14, thereby reducing its expression. CTBP1-AS silencing suppressed proliferation, apoptosis resistance, migration, invasion, and tumorigenicity of PCa cell lines, while its overexpression led to inverse results. The malignant phenotype of cells was further weakened by TP63 overexpression but restored following artificial S100A14 silencing. In conclusion, this study demonstrates that CTBP1-AS plays an oncogenic role in PCa by blocking TP63-mediated transcriptional activation of S100A14. This may provide insight into the management of PCa.

Developing a RiskScore Model based on Angiogenesis-related lncRNAs for Colon Adenocarcinoma Prognostic Prediction

AIM

We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskScore model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment.

BACKGROUND

Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD.

OBJECTIVE

To identify key angiogenesis-related lncRNAs and build a RiskScore model to predict the survival probability of COAD patients and help optimize clinical treatment.

METHODS

Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskScore model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package.

RESULTS

Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskScore model. The high RiskScore group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskScore signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment.

CONCLUSION

We constructed a RiskScore model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.

LncRNA OTUD6B-AS1 overexpression promoted GPX4-mediated ferroptosis to suppress radioresistance in colorectal cancer

BACKGROUND

Radiotherapy is widely employed in colorectal cancer (CRC) treatment but is often compromised by developed radioresistance. This study explored the mechanism of long non-coding RNA ovarian tumor domain containing 6B-antisense RNA1 (lncRNA OTUD6B-AS1) in CRC radioresistance through tripartite motif 16 (TRIM16).

METHODS

CRC and non-cancerous tissues were collected and radioresistant CRC cells were established, with real-time quantitative polymerase chain reaction to determine gene expression in tissues and cells. Radioresistance was evaluated by cell counting kit-8 assay and immunofluorescence (γ-H2AX) and ferroptosis was tested by Western blot assay (ACSL4/GPX4) and assay kits (Fe2+/ROS/MDA/GSH). The association between ferroptosis and lncRNA OTUD6B-AS1-inhibited radioresistance was testified using ferroptosis inhibitor. The subcellular localization of lncRNA OTUD6B-AS1 was tested by the nuclear/cytoplasmic fractionation assay, with RNA immunoprecipitation assay to validate gene interactions. Rescue experiments were conducted to analyze the role of TRIM16 in CRC radioresistance.

RESULTS

LncRNA OTUD6B-AS1 and TRIM16 were poorly expressed (P < 0.01) in CRC tissues and cells and further decreased (P < 0.01) in radioresistant CRC cells. OTUD6B-AS1 overexpression decreased cell survival (P < 0.01), increased γ-H2AX levels (P < 0.01), and elevated ferroptosis and oxidative stress (P < 0.01) after X-ray radiation. Ferroptosis inhibitor attenuated radioresistance (P < 0.01) caused by lncRNA OTUD6B-AS1 overexpression. LncRNA OTUD6B-AS1 stabilized TRIM16 mRNA via binding to HuR. TRIM16 knockdown reduced ferroptosis and increased radioresistance (P < 0.05).

CONCLUSION

OTUD6B-AS1 overexpression stabilized TRIM16 via binding to HuR and increased GPX4-mediated ferroptosis, thus attenuating CRC radioresistance. Our study provided a new rationale for the treatment of CRC.

Comprehensive analysis of basement membrane and immune checkpoint related lncRNA and its prognostic value in hepatocellular carcinoma via machine learning

Background

Hepatocellular carcinoma (HCC), which is characterized by its high malignancy, generally exhibits poor response to immunotherapy. As part of the tumor microenvironment, basement membranes (BMs) are involved in tumor development and immune activities. Presently, there is no integrated analysis linking the basement membrane with immune checkpoints, especially from the perspective of lncRNA.

Methods

Based on transcriptome data from The Cancer Genome Atlas, BMs-related and immune checkpoint-related lncRNAs were identified. By applying univariable Cox regression and Machine learning (LASSO and SVM-RFE algorithm), a 10-lncRNA prognosis signature was constructed. The prognostic significance of this signature was assessed by survival analysis. GSEA, ssGSEA, and drug sensitivity analysis were conducted to investigate potential functional pathways, immune status, and clinical implications of guiding individual treatments in HCC. Finally, the promoting migration effect of LINC01224 was validated via in vitro experiments.

Results

The multiple Cox regression, receiver operating characteristic curves, and stratified survival analysis of clinical subgroups exhibited the robust prognostic ability of the lncRNA signature. Results of the GSEA and drug sensitivity analysis revealed significant differences in potential functional pathways and response to drugs between the two risk groups. In addition, the risk level of HCC patients was distinctly correlated with immune cell infiltration status. More importantly, LINC01224 was independently associated with the OS of HCC patients (P < 0.05), suppressing the expression of LINC01224 inhibited the migration of HCC cells.

Conclusion

This study developed a reliable signature for the prognosis of HCC based on BM and immune checkpoint related lncRNA, revealing that LINC01224 might be a prognostic biomarker for HCC associated with the progression of HCC.

Construction and characterization of a cuproptosis- and immune checkpoint-based LncRNAs signature for breast cancer risk stratification

BACKGROUND

Cuproptosis is the most recently identified form of cell death, and copper homeostasis is an important cancer therapeutic target. However, the therapeutic benefits of cuproptosis-targeted treatment in BRCA remain undetermined. This study utilized LncRNAs linked to cuproptosis genes and immune checkpoint genes to generate a BRCA predictive signature.

METHODS

We screened a population of LncRNAs that correlated with both cuproptosis genes and immune checkpoint genes and used ten of these LncRNAs to construct a prognosis-predictive signature. We then validated and proved the efficacy of the signature in predicting the prognosis of BRCA patients. We also unraveled the relationship between the signature and the immunological milieu, immune function, and susceptibility to chemotherapy.

RESULTS

The signature derived from the ten cuproptosis- and immune-related prognostic LncRNAs (CuImP-LncRNAs) can be implied to categorize patients into two groups, including the high- and low-risk groups. The value of the signature was validated, and the risk score was verified as an independent prognostic indicator. The TIME and TMB distribution patterns and chemosensitivity were depicted in the high- and low-risk groups, respectively. Patients of the high-risk group with a suppressive immunological intratumor context were more sensitive to a broad range of antitumor agents. In contrast, low-risk individuals with active immune function responded more favorably to immunotherapy.

CONCLUSION

Our findings provided a novel and effective model for predicting BRCA prognosis and the propensity to different treatment modalities, thus contributing to the optimization of personalized BRCA therapy in the future.

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Objective: In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism. Methods: The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receiver operator characteristic curve. High-risk patients were differentiated from low-risk patients through the cut-off value, and the correlation analyses of the high-risk subgroup and low-risk subgroup were performed. Results: This research discovered that 25 pairs of lncRNAs were associated with the lipid metabolism of ccRCC, and 12 of these pairs were utilized to build the model. In combination with clinical data, the areas under the 1-, 3- and 5-year survival curves of ccRCC patients were 0.809, 0.764 and 0.792, separately. The cut-off value was used to perform subgroup analysis. The results showed that high-risk patients had poor prognosis. The results of Cox multivariate regressive analyses revealed that age and risk score were independent prediction factors of ccRCC prognosis. In addition, immune cell infiltration, the levels of gene expression at immune checkpoints, and high-risk patients more susceptible to sunitinib-targeted treatment were assessed by the risk model. Conclusion: Our team identified new prognostic markers of ccRCC and established risk models that could assess the prognosis of ccRCC patients and help determine which type of patients were more susceptible to sunitinib. These discoveries are vital for the optimization of risk stratification and personalized management.

Pan-Cancer Methylated Dysregulation of Long Non-coding RNAs Reveals Epigenetic Biomarkers

Different cancer types not only have common characteristics but also have their own characteristics respectively. The mechanism of these specific and common characteristics is still unclear. Pan-cancer analysis can help understand the similarities and differences among cancer types by systematically describing different patterns in cancers and identifying cancer-specific and cancer-common molecular biomarkers. While long non-coding RNAs (lncRNAs) are key cancer modulators, there is still a lack of pan-cancer analysis for lncRNA methylation dysregulation. In this study, we integrated lncRNA methylation, lncRNA expression and mRNA expression data to illuminate specific and common lncRNA methylation patterns in 23 cancer types. Then, we screened aberrantly methylated lncRNAs that negatively regulated lncRNA expression and mapped them to the ceRNA relationship for further validation. 29 lncRNAs were identified as diagnostic biomarkers for their corresponding cancer types, with lncRNA AC027601 was identified as a new KIRC-associated biomarker, and lncRNA ACTA2-AS1 was regarded as a carcinogenic factor of KIRP. Two lncRNAs HOXA-AS2 and AC007228 were identified as pan-cancer biomarkers. In general, the cancer-specific and cancer-common lncRNA biomarkers identified in this study may aid in cancer diagnosis and treatment.

Four Types of RNA Modification Writer-Related lncRNAs Are Effective Predictors of Prognosis and Immunotherapy Response in Serous Ovarian Carcinoma

Serous ovarian carcinoma (SOC) is a gynecological malignancy with high mortality rates. Currently, there is a lack of reliable biomarkers for accurate SOC patient prognosis. Here, we analyzed SOC RNA-Seq data from The Cancer Genome Atlas (TCGA) to identify prognostic biomarkers. Through the pearson correlation analysis, univariate Cox regression analysis, and LASSO-penalized Cox regression analysis, we identified nine lncRNAs significantly associated with four types of RNA modification writers (m6A, m1A, APA, and A-I) and with the prognosis of SOC patients (P <0.05). Six writer-related lncRNAs were ultimately selected following multivariate Cox analysis. We established a risk prediction model based on these six lncRNAs and evaluated its prognostic value in multiple groups (training set, testing set, and entire set). Our risk prediction model could effectively predict the prognosis of SOC patients with different clinical characteristics and their responses to immunotherapy. Lastly, we validated the predictive reliability and sensitivity of the lncRNA-based model via a nomogram. This study explored the association between RNA modification writer-related lncRNAs and SOC prognosis, providing a potential complement for the clinical management of SOC patients.

5-Methylcytosine-Related Long Noncoding RNAs Are Potential Biomarkers to Predict Overall Survival and Regulate Tumor-Immune Environment in Patients with Bladder Cancer

The role of 5-methylcytosine-related long noncoding RNAs (m5C-lncRNAs) in bladder cancer (BLCA) remains unclear. Here, we aim to study the prognostic value, gene expression characteristics, and correlation between the m5C-lncRNA risk model and the tumor microenvironment, immune infiltration, and tumor mutations in BLCA. After collecting BLCA patient RNA sequence transcriptome data, clinical information and mutation data from the Cancer Genome Atlas (TCGA) database, 17 m5C-related lncRNAs independently correlated with OS were obtained by Lasso and multivariate Cox regression analysis, and a risk model was constructed. Univariate Cox, multivariate Cox regression analysis, and the C-index curve proved that the risk model was a significant independent prognostic indicator for patients with BLCA. ESTIMATE and CIBERSORT indicated that the higher the number of immune cells and stromal cells in TME, the higher the prognostic risk. We found that in the low-risk group, the expression levels of immune cells that predicted a good prognosis were higher, including plasma cells, regulatory T cells, and CD 8 T cells. There is a negative correlation between TMB and risk score. The TMB of the low-risk group is significantly higher than that of the high-risk group. In conclusion, the m5C-related risk model is crucial to predict the prognosis of patients with BLCA.

Nine Pyroptosis-Related lncRNAs are Identified as Biomarkers for Predicting the Prognosis and Immunotherapy of Endometrial Carcinoma

Background

Endometrial carcinoma (EC) is one of the most common malignancies. Immunotherapy has shown promising effects in the treatment against specific subtypes of EC.

Methods

The RNA and clinical information of patients with EC were acquired from The Cancer Gene Atlas (TCGA) database. Firstly, the differentially expressed pyroptosis-related lncRNAs (PRLs) were screened between the tumor and normal control tissue. Secondly, the PRLs closely related to survival were identified by univariate and multivariate regression analysis, based on which, we evaluated the risk score for each EC patient to construct a risk signature. Moreover, we assessed the prognostic value, clinical relevance immunity, and immunotherapy based on this signature.

Results

We screened out 9 individual PRLs (AC087491.1, AL353622.1, AL035530.2, LINC02036, AL021578.1, AL390195.2, AC009097.2, AC004585.1, and AC244517.7) closely related to the prognosis of EC. Kaplan–Meier analyses showed a poorer prognosis for the patients in the high-risk FRLs signature (P < 0.001). The area under the curve (AUC) for 1 year, 2 years, 3 years was 0.693, 0.694, 0.750, respectively. Our risk model could be considered as an independent prognostic marker for EC (P < 0.001, HR:2.172, 95% CI:1.532–3.079). Moreover, immune functions and checkpoints were generally different in the 2 groups. Simulation analysis by termed immunophenoscores hinted that immunotherapy might bring optimal therapeutic effect in the low-risk group.

Conclusion

We successfully developed a novel signature with 9 lncRNAs related to pyroptosis, which may be used as biomarkers to evaluate the prognosis and immune treatment of EC.