Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

LINC02253 promote the malignant phenotype of Colon adenocarcinoma cells by up-regulating WWP1-mediated SMAD3 ubiquitination

OBJECTIVES

Colon adenocarcinoma (COAD) represents a type of common malignant tumor originating in the digestive tract. Long non-coding RNAs (lncRNAs) have been identified to engage in regulating the initiation and development of COAD. LncRNA LINC02253 has been reported abnormal expressed in COAD, but the underlying mechanism has not been discussed so far. This study aimed to determine the role and the molecular biology mechanism of LINC02253 in COAD progression and unearthed its specific molecular mechanism.

MATERIALS AND RESULTS

RT-qPCR and Western blot assays were conducted to detect gene expression. Function assays were performed to evaluate the effect of gene expression on COAD cell phenotype. Mechanism analyses were done to verify the association among genes after bioinformatics analysis. The obtained data revealed that LINC02253 demonstrated a high expression in COAD tissues and cells. This gene served as an oncogene, permitting to stimulate proliferation and suppress apoptosis of COAD cells. Mechanically, it was found that LINC02253 recruited FUS to stabilize WWP1 mRNA and WWP1 could mediate SMAD3 ubiquitination, thereby promoting the malignant phenotype formation of COAD cells.

CONCLUSIONS

LINC02253 was uncovered to exert an oncogenic role, enhancing the proliferation of COAD cells and repressing the cell apoptosis by recruiting FUS and encouraging WWP1-mediated SMAD3 ubiquitination.

Identification of a basement membrane-related genes signature with immune correlation in bladder urothelial carcinoma and verification in vitro

BACKGROUND

Bladder urothelial carcinoma (BLCA) is the most common genitourinary cancer and the prognosis of patients is often poor. However, studies of basement membrane-related genes (BM-related genes) in BLCA are less reported. Therefore, we established a BM-related genes signature to explore their functional and prognostic value in BLCA.

METHODS

In this study, a BM-related genes signature was constructed by LASSO-Cox regression analysis, and then a series of bioinformatics methods was used to assess the accuracy and validity of the signature. We constructed a nomogram for clinical application and also screened for possible therapeutic drugs. To investigate the functions and pathways affected by BM-related genes in BLCA, we performed functional enrichment analyses. In addition, we analyzed the immune cell infiltration landscape and immune checkpoint-related genes in the high and low-risk groups. Finally, we confirmed the prognostic value of BM-related genes in BLCA in vitro.

RESULTS

Combining multiple bioinformatics approaches, we identified a seven-gene signature. The accuracy and validity of this signature in predicting BLCA patients were confirmed by the test cohort. In addition, the risk score was strongly correlated with prognosis, immune checkpoint genes, drug sensitivity, and immune cell infiltration landscape. The risk score is an independent prognostic factor for BLCA patients. Further experiments revealed that all seven signature genes were differentially expressed between BLCA cell lines and normal bladder cells. Finally, overexpression of LAMA2 inhibited the migration and invasion ability of BLCA cell lines.

CONCLUSIONS

In summary, the BM-related genes signature was able to predict the prognosis of BLCA patients accurately, indicating that the BM-related genes possess great clinical value in the diagnosis and treatment of BLCA. Moreover, LAMA2 could be a potential therapeutic target, which provides new insights into the application of the BM-related genes in BLCA patients.

A new marker constructed from immune-related lncRNA pairs can be used to predict clinical treatment effects and prognosis: in-depth exploration of underlying mechanisms in HNSCC

BACKGROUND

Long non-coding RNA (lncRNA) plays a vital role in tumor proliferation, migration, and treatment. Since it is challenging to standardize the gene expression levels detected by different platforms, the signatures composed of many immune-related single lncRNAs are still inaccurate. Utilizing a gene pair formed of two immune-related lncRNAs and strategically assigning values can effectively meet the demand for a higher-accuracy dual biomarker combination.

METHODS

Co-expression and differential expression analyses were performed on immune genes and lncRNAs data from The Cancer Genome Atlas and the ImmPort database to obtain differentially expressed immune-related lncRNAs for pairwise pairing. The prognostic-related differentially expressed immune-related lncRNAs (PR-DE-irlncRNAs) pairs were then identified by univariate Cox regression and used for lasso regression to construct a prognostic model. Various methods were used to validate the predictive prognostic performance of the model. Additionally, we explored the potential guiding value of the model in immunotherapy and chemotherapy and constructed a nomogram suitable for efficient prognosis prediction. Mechanistic exploration of anti-tumor immunity and mutational perspectives are also included. We also analyzed the correlation between the model and immune checkpoint inhibitors (ICIs)-related, N6-methyadenosine (m6A)-related, and multidrug resistance genes.

RESULTS

We used a total of 20 pairs of PR-DE-irlncRNAs to create a prognosis model. Quantitative real-time polymerase chain reaction experiments further verified the abnormal expression of 11 lncRNAs in HNSCC cells. Various methods have confirmed the excellent performance of the model in predicting patient prognosis. We reasoned that lncRNAs/TP53 mutation might play a positive/negative anti-tumor role through the immune system by multi-perspective analyses. Finally, it was found that the prognostic model was closely related to immunotherapy and chemotherapy as well as the expression of ICIs/m6A/multidrug resistance-related genes.

CONCLUSION

The prognostic model performs excellently in predicting the prognosis of patients and provides the potential value of practical guidance for treatment.

Identification and validation of a m7G-related lncRNA signature for predicting the prognosis and therapy response in hepatocellular carcinoma

BACKGROUND

N7-methylguanosine (m7G) is one of the most common RNA posttranscriptional modifications; however, its potential role in hepatocellular carcinoma (HCC) remains unknown. We developed a prediction signature based on m7G-related long noncoding RNAs (lncRNAs) to predict HCC prognosis and provide a reference for immunotherapy and chemotherapy.

METHODS

RNA-seq data from The Cancer Genome Atlas (TCGA) database and relevant clinical data were used. Univariate and multivariate Cox regression analyses were conducted to identify m7G-related lncRNAs with prognostic value to build a predictive signature. We evaluated the prognostic value and clinical relevance of this signature and explored the correlation between the predictive signature and the chemotherapy treatment response of HCC. Moreover, an in vitro study to validate the function of CASC19 was performed.

RESULTS

Six m7G-related lncRNAs were identified to create a signature. This signature was considered an independent risk factor for the prognosis of patients with HCC. TIDE analyses showed that the high-risk group might be more sensitive to immunotherapy. ssGSEA indicated that the predictive signature was strongly related to the immune activities of HCC. HCC in high-risk patients was more sensitive to the common chemotherapy drugs bleomycin, doxorubicin, gemcitabine, and lenalidomide. In vitro knockdown of CASC19 inhibited the proliferation, migration and invasion of HCC cells.

CONCLUSION

We established a 6 m7G-related lncRNA signature that may assist in predicting the prognosis and response to chemotherapy and immunotherapy of HCC.

Constructing a novel signature and predicting the immune landscape of colon cancer using N6-methylandenosine-related lncRNAs

Background: Colon cancer (CC) is a prevalent malignant tumor that affects people all around the world. In this study, N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) in 473 colon cancers and 41 adjacent tissues of CC patients from The Cancer Genome Atlas (TCGA) were investigated. Method: The Pearson correlation analysis was conducted to examine the m6A-related lncRNAs, and the univariate Cox regression analysis was performed to screen 38 prognostic m6A-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression analysis were carried out on 38 prognostic lncRNAs to develop a 14 m6A-related lncRNAs prognostic signature (m6A-LPS) in CC. The availability of the m6A-LPS was evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. Results: Three m6A modification patterns with significantly different N stages, survival time, and immune landscapes were identified. It has been discovered that the m6A-LPS, which is based on 14 m6A-related lncRNAs (TNFRSF10A-AS1, AC245041.1, AL513550.1, UTAT33, SNHG26, AC092944.1, ITGB1-DT, AL138921.1, AC099850.3, NCBP2-AS1, AL137782.1, AC073896.3, AP006621.2, AC147651.1), may represent a new, promising biomarker with great potential. It was re-evaluated in terms of survival rate, clinical features, tumor infiltration immune cells, biomarkers related to Immune Checkpoint Inhibitors (ICIs), and chemotherapeutic drug efficacy. The m6A-LPS has been revealed to be a novel potential and promising predictor for evaluating the prognosis of CC patients. Conclusion: This study revealed that the risk signature is a promising predictive indicator that may provide more accurate clinical applications in CC therapeutics and enable effective therapy strategies for clinicians.

Based on cuproptosis-related lncRNAs, a novel prognostic signature for colon adenocarcinoma prognosis, immunotherapy, and chemotherapy response

Introduction: Colon adenocarcinoma (COAD) is a special pathological subtype of colorectal cancer (CRC) with highly heterogeneous solid tumors with poor prognosis, and novel biomarkers are urgently required to guide its prognosis. Material and methods: RNA-Seq data of COAD were downloaded through The Cancer Genome Atlas (TCGA) database to determine cuproptosis-related lncRNAs (CRLs) using weighted gene co-expression network analysis (WGCNA). The scores of the pathways were calculated by single-sample gene set enrichment analysis (ssGSEA). CRLs that affected prognoses were determined via the univariate COX regression analysis to develop a prognostic model using multivariate COX regression analysis and LASSO regression analysis. The model was assessed by applying Kaplan-Meier (K-M) survival analysis and receiver operating characteristic curves and validated in GSE39582 and GSE17538. The tumor microenvironment (TME), single nucleotide variants (SNV), and immunotherapy response/chemotherapy sensitivity were assessed in high- and low-score subgroups. Finally, the construction of a nomogram was adopted to predict survival rates of COAD patients during years 1, 3, and 5. Results: We found that a high cuproptosis score reduced the survival rates of COAD significantly. A total of five CRLs affecting prognosis were identified, containing AC008494.3, EIF3J-DT, AC016027.1, AL731533.2, and ZEB1-AS1. The ROC curve showed that RiskScore could perform well in predicting the prognosis of COAD. Meanwhile, we found that RiskScore showed good ability in assessing immunotherapy and chemotherapy sensitivity. Finally, the nomogram and decision curves showed that RiskScore would be a powerful predictor for COAD. Conclusion: A novel prognostic model was constructed using CRLs in COAD, and the CRLs in the model were probably a potential therapeutic target. Based on this study, RiskScore was an independent predictor factor, immunotherapy response, and chemotherapy sensitivity for COAD, providing a new scientific basis for COAD prognosis management.

Succinylation-associated lncRNA signature to predict the prognosis of colon cancer based on integrative bioinformatics analysis

Colon cancer (CC) has a poor 5-year survival rate though the treatment techniques and strategies have been improved. Succinylation and long noncoding RNAs (lncRNAs) have prognostic value for CC patients. We analyzed and obtained succinylation-related lncRNA by co-expression in CC. A novel succinylation-related lncRNA model was developed by univariate and Least absolute shrinkage and selection operator (Lasso) regression analysis and we used principal component analysis (PCA), functional enrichment annotation, tumor immune environment, drug sensitivity and nomogram to verify the model, respectively. Six succinylation-related lncRNAs in our model were finally confirmed to distinguish the survival status of CC and showed statistically significant differences in training set, testing set, and entire set. The prognosis of with this model was associated with age, gender, M0 stage, N2 stage, T3 + T4 stage and Stage III + IV. The high-risk group showed a higher mutation rate than the low-risk group. We constructed a model to predict overall survival for 1-, 3-, and 5-year with AUCs of 0.694, 0.729, and 0.802, respectively. The high-risk group was sensitive to Cisplatin and Temozolomide compounds. Our study provided novel insights into the value of the succinylation-related lncRNA signature as a predictor of prognosis, which had high clinical application value in the future.

A novel cuproptosis-related lncRNA signature predicts prognosis and therapeutic response in bladder cancer

Bladder cancer (BC) ranks the tenth in the incidence of global tumor epidemiology. LncRNAs and cuproptosis were discovered to regulate the cell death. Herein, we downloaded transcriptome profiling, mutational data, and clinical data on patients from The Cancer Genome Atlas (TCGA). High- and low-risk BC patients were categorized. Three CRLs (AL590428.1, AL138756.1 and GUSBP11) were taken into prognostic signature through least absolute shrinkage and selection operator (LASSO) Cox regression. Worse OS and PFS were shown in high-risk group (p < 0.05). ROC, independent prognostic analyses, nomogram and C-index were predicted via CRLs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated IncRNAs play a biological role in BC progression. Immune-related functions showed the high-risk group received more benefit from immunotherapy and had stronger immune responses, and the overall survival was better (p < 0.05). Finally, a more effective outcome (p < 0.05) was found from clinical immunotherapy via the TIDE algorithm and many potential anti-tumor drugs were identified. In our study, the cuproptosis-related signature provided a novel tool to predict the prognosis in BC patients accurately and provided a novel strategy for clinical immunotherapy and clinical applications.

Development and validation of a chromatin regulator prognostic signature in colon adenocarcinoma

Aberrant expression of chromatin regulators (CRs) could lead to the development of various diseases including cancer. However, the biological function and prognosis role of CRs in colon adenocarcinoma (COAD) remains unclear. We performed the clustering analyses for expression profiling of COAD downloaded from The Cancer Genome Atlas. We developed a chromatin regulator prognostic model, which was validated in an independent cohort data. Time-intendent receiver operating characteristics curve was used to evaluate predict ability of model. Univariate and multivariate cox regression were used to assess independence of risk score. Nomogram was established to assess individual risk. Gene ontology, and Kyoto Encyclopedia of genes and genomes, gene set variation analysis and gene set enrichment analysis were performed to explore the function of CRs. Immune infiltration and drug sensitivity were also performed to assess effect of CRs on treatment in COAD. COAD can be separated into two subtypes with different clinical characteristics and prognosis. The C2 had elevated immune infiltration levels and low tumor purity. Using 12 chromatin regulators, we developed and validated a prognostic model that can predict the overall survival of COAD patients. We built a risk score that can be an independent prognosis predictor of COAD. The nomogram score system achieved the best predict ability and were also confirmed by decision curve analysis. There were significantly different function and pathway enrichment, immune infiltration levels, and tumor mutation burden between high-risk and low-risk group. The external validation data also indicated that high-risk group had higher stable disease/progressive disease response rate and poorer prognosis than low-risk group. Besides, the signature genes included in the model could cause chemotherapy sensitivity to some small molecular compounds. Our integrative analyses for chromatin regulators could provide new insights for the risk management and individualized treatment in COAD.

Prognostic signature of lipid metabolism associated LncRNAs predict prognosis and treatment of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most predominant histological subtype of lung cancer. Abnormal lipid metabolism is closely related to the development of LUAD. LncRNAs are involved in the regulation of various lipid metabolism-related genes in various cancer cells including LUAD. Here, we aimed to identify lipid metabolism-related lncRNAs associated with LUAD prognosis and to propose a new prognostic signature.

METHODS

First, differentially expressed lncRNAs (DE-lncRNAs) from the TCGA-LUAD and the GSE31210 dataset were identified. Then the correlation analysis between DE-lncRNAs and lipid metabolism genes was performed to screen lipid metabolism-related lncRNAs. Cox regression analyses were performed in the training set to establish a prognostic model and the model was validated in the testing set and the validation set. Moreover, The role of this model in the underlying molecular mechanisms, immunotherapy, and chemotherapeutic drug sensitivity analysis was predicted by methods such as Gene Set Enrichment Analysis, immune infiltration, tumor mutational burden (TMB), neoantigen, Tumor Immune Dysfunction and Exclusion, chemosensitivity analysis between the high- and low-risk groups. The diagnostic ability of prognostic lncRNAs has also been validated. Finally, we validated the expression levels of selected prognostic lncRNAs by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

The prognostic model was constructed based on four prognostic lncRNAs (LINC00857, EP300-AS1, TBX5-AS1, SNHG3) related to lipid metabolism. The receiver operating characteristic curve (ROC) and Kaplan Meier (KM) curves of the risk model showed their validity. The results of Gene Set Enrichment Analysis suggested that differentially expressed genes in high- and low-risk groups were mainly enriched in immune response and cell cycle. There statistical differences in TMB and neoantigen between high- and low-risk groups. Drug sensitivity analysis suggested that patients with low risk scores may have better chemotherapy outcomes. The results of qRT-PCR were suggesting that compared with the normal group, the expressions of EP300-AS1 and TBX5-AS1 were down-regulated in the tumor group, while the expressions of LINC00857 and SNHG3 were up-regulated. The four prognostic lncRNAs had good diagnostic capabilities, and the overall diagnostic model of the four prognostic lncRNAs was more effective.

CONCLUSION

A total of 4 prognostic lncRNAs related to lipid metabolism were obtained and an effective risk model was constructed.

Construction of prognostic signature of breast cancer based on N7-Methylguanosine-Related LncRNAs and prediction of immune response

Background: Long non-coding RNA (LncRNA) is a prognostic factor for malignancies, and N7-Methylguanosine (m7G) is crucial in the occurrence and progression of tumors. However, it has not been documented how well m7G-related LncRNAs predict the development of breast cancer (BC). This study aims to develop a predictive signature based on long non-coding RNAs (LncRNAs) associated with m7G to predict the prognosis of breast cancer patients.

Methods: The Cancer Genome Atlas (TCGA) database provided us with the RNA-seq data and matching clinical information of individuals with breast cancer. To identify the signature of N7-Methylguanosine-Related LncRNAs and create a prognostic model, we employed co-expression network analysis, least absolute shrinkage selection operator (LASSO) regression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. The signature was assessed using the Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) curve. A nomogram and principal component analysis (PCA) were employed to confirm the predictive signature’s usefulness. Then, we examined the drug sensitivity between the two risk groups and utilized single-sample gene set enrichment analysis (ssGSEA) to investigate the association between predictive factors and the tumor immune microenvironment in high-risk and low-risk groups.

Results: Nine m7G-related LncRNAs (LINC01871, AP003469.4, Z68871.1, AC245297.3, EGOT, TFAP2A-AS1, AL136531.1, SEMA3B-AS1, AL606834.2) that are independently associated with the overall survival time (OS) of BC patients make up the signature we developed. For predicting 1-, 3-, and 5-year survival rates, the areas under the ROC curve (AUC) were 0.715, 0.724, and 0.726, respectively. The Kaplan-Meier analysis revealed that the prognosis of BC patients in the high-risk group was worse than that of those in the low-risk group. When compared to clinicopathological variables, multiple regression analysis demonstrated that risk score was a significant independent predictive factor for BC patients. The results of the ssGSEA study revealed a substantial correlation between the predictive traits and the BC patients’ immunological status, low-risk BC patients had more active immune systems, and they responded better to PD1/L1 immunotherapy.

Conclusion: The prognostic signature, which is based on m7G-related LncRNAs, can be utilized to inform patients’ customized treatment plans by independently predicting their prognosis and how well they would respond to immunotherapy.

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data

Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.

Prognostic prediction of head and neck squamous cell carcinoma: Construction of cuproptosis-related long non-coding RNA signature

BACKGROUND

Recently, a new type of programmed cell death, cuproptosis, has been identified to play important role in the progression of tumors. We constructed a cuproptosis-related long non-coding RNA (lncRNA) signature to predict the prognostic significance for head and neck squamous cell carcinoma (HNSCC).

METHODS

The risk model was developed based on differentially expressed lncRNAs associated with cuproptosis. Principal component analysis was used to assess the validity. The Kaplan-Meier curves were analyzed to compare the overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) values. The multivariate and univariate Cox regression analyses were used to evaluate the prognostic efficiency. Furthermore, the functional enrichment, immune cell infiltration, tumor mutation burden (TMB), and sensitivity toward chemotherapy were also explored.

RESULTS

Six cuproptosis-related lncRNAs (AL109936.2, CDKN2A-DT, AC090587.1, KLF3-AS1, AL133395.1, and LINC01063) were identified to construct the independent prognostic predictor for HNSCC. The area under the curve and C-index values obtained using the risk model were higher than the values corresponding to the clinical factors. Analysis of Kaplan-Meier curves indicated that the OS, PFS, and DSS time recorded for the patients in the low-risk group were higher than the corresponding values recorded for the patients belonging to the high-risk group. By functional enrichment analysis, we observed that differentially expressed genes were enriched in the immune response and tumor-associated pathways. The patients characterized by a low-risk score exhibited better immune cell infiltration than the patients belonging to the other group. We also observed that the sensitivity of the individuals belonging to the low-risk group to chemotherapeutic agents (cisplatin, docetaxel, and paclitaxel) was higher than the sensitivity of those in the other group.

CONCLUSIONS

A cuproptosis-related lncRNA-based signature that functioned as an independent prognosis predictor for HNSCC patients was constructed. The chemosensitivity of individual patients can be potentially predicted using this signature.

Identification of pyroptosis-related lncRNA signature and AC005253.1 as a pyroptosis-related oncogene in prostate cancer

Background

Pyroptosis and prostate cancer (PCa) are closely related. The role of pyroptosis-related long non-coding RNAs (lncRNAs) (PRLs) in PCa remains elusive. This study aimed to explore the relationship between PRL and PCa prognosis.

Methods

Gene expression and clinical signatures were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A PRL risk prediction model was established by survival random forest analysis and least absolute shrinkage and selection operator regression. Functional enrichment, immune status, immune checkpoints, genetic mutations, and drug susceptibility analyses related to risk scores were performed by the single-sample gene set enrichment analysis, gene set variation analysis, and copy number variation analysis. PRL expression was verified in PCa cells. Cell Counting Kit-8, 5-ethynyl-2′-deoxyuridine, wound healing, transwell, and Western blotting assay were used to detect the proliferation, migration, invasion, and pyroptosis of PCa cells, respectively.

Results

Prognostic features based on six PRL (AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1-AS1, and LINC01852) were constructed, and patients in the high-score group had a worse prognosis than those in the low-score group. This feature was determined to be independent by Cox regression analysis, and the area under the curve of the 1-, 3-, and 5-year receiver operating characteristic curves in the testing cohort was 1, 0.93, and 0.92, respectively. Moreover, the external cohort validation confirmed the robustness of the PRL risk prediction model. There was a clear distinction between the immune status of the two groups. The expression of multiple immune checkpoints was also reduced in the high-score group. Gene mutation proportion in the high-score group increased, and the sensitivity to drugs increased significantly. Six PRLs were upregulated in PCa cells. Silencing of AC005253.1 inhibited cell proliferation, migration, and invasion in DU145 and PC-3 cells. Moreover, silencing of AC005253.1 promoted pyroptosis and inflammasome AIM2 expression.

Conclusions

Overall, we constructed a prognostic model of PCa with six PRLs and identified their expression in PCa cells. The experimental verification showed that AC005253.1 could affect the proliferation, migration, and invasion abilities of PCa cells. Meanwhile, AC005253.1 may play an important role in PCa by affecting pyroptosis through the AIM2 inflammasome. This result requires further research for verification.

Constructing a prognostic immune-related lncRNA model for colon cancer

Colon cancer is a common digestive tract tumor. Although many gene prognostic indicators have been used to predict the prognosis of colon cancer patients, the accuracy of these prognostic indicators is still uncertain. Thus, it is necessary to construct a model for the prognostic analysis of colon cancer. We downloaded the original transcriptome data of colon cancer and performed a differential coexpression analysis of immune-related genes to obtain different immune-related long noncoding RNAs, which were paired as differentially expressed immune-related lncRNA pairs (DEirlncRNAPs). Then, the 1-year overall survival rate receiver operating characteristic curve was calculated, and the Akaike information criterion value was evaluated to determine the maximum inflection point, which was used as the cutoff point to identify groups of colon cancer patients at high and low risk for death. Subsequently, the optimal prediction model was established. Finally, we used the patients' survival times, clinicopathological features, tumor infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers to verify the DEirlncRNAP model. Seventy-one DEirlncRNAPs were obtained to build the risk assessment model. The patients were divided into a high-risk group and a low-risk group according to the cutoff point. Then, the DEirlncRNAP model was verified using patient survival times, clinicopathological features, tumor-infiltrating immune cells, chemotherapy responses, and immunosuppressive biomarkers. A new DEirlncRNAP model for predicting the prognosis of colon cancer patients was established, which could reveal new insights into the relationships of colon cancer with tumor-infiltrating immune cells and antitumor immunotherapy.

Focus on pattern recognition receptors to identify prognosis and immune microenvironment in colon cancer

In 2011, J. Hoffman, and B. Beutler won the Nobel Prize of medicine for the fact that they discovered the pattern recognition receptors (PRRs) and meanwhile described their effect on cell activation from the innate and adaptive immune systems. There are more and more evidences that have proved the obvious effect of PRRs on tumorigenesis progression. Nevertheless, the overall impact of PRR genes on prognosis, tumor microenvironmental characteristics and treatment response in patients with colon adenocarcinoma (COAD) remains unclear. In this research, we systematically assessed 20 PRR genes and comprehensively identified the prognostic value and enrichment degree of PRRs. The unsupervised clustering approach was employed for dividing COAD into 4 PRR subtypes, namely cluster A, cluster B, cluster C and cluster D, which were significantly different in terms of the clinical features, the immune infiltrations, and the functions. Among them, cluster B has better immune activities and functions. Cox and LASSO regression analysis was further applied to identify a prognostic five-PRR-based risk signature. Such signature can well predict patients’ overall survival (OS), together with a good robustness. Confounding parameters were controlled, with results indicating the ability of risk score to independently predict COAD patients’ OS. Besides, a nomogram with a strong reliability was created for enhancing the viability exhibited by the risk score in clinical practice. Also, patients who were classified based on the risk score owned distinguishable immune status and tumor mutation status, response to immunotherapy, as well as sensitivity to chemotherapy. A low risk score, featuring increased tumor stemness index (TSI), human leukocyte antigen (HLA), immune checkpoints, and immune activation, demonstrated a superior immunotherapeutic response. According to the study results, the prognostic PRR-based risk signature could serve as a robust biomarker for predicting the clinical outcomes as well as evaluating therapeutic response for COAD patients.

In Silico Identification and Validation of Cuproptosis-Related LncRNA Signature as a Novel Prognostic Model and Immune Function Analysis in Colon Adenocarcinoma

Background: Colon adenocarcinoma (COAD) is the most common subtype of colon cancer, and cuproptosis is a recently newly defined form of cell death that plays an important role in the development of several malignant cancers. However, studies of cuproptosis-related lncRNAs (CRLs) involved in regulating colon adenocarcinoma are limited. The purpose of this study is to develop a new prognostic CRLs signature of colon adenocarcinoma and explore its underlying biological mechanism. Methods: In this study, we downloaded RNA-seq profiles, clinical data and tumor mutational burden (TMB) data from the TCGA database, identified cuproptosis-associated lncRNAs using univariate Cox, lasso regression analysis and multivariate Cox analysis, and constructed a prognostic model with risk score based on these lncRNAs. COAD patients were divided into high- and low-risk subgroups based on the risk score. Cox regression was also used to test whether they were independent prognostic factors. The accuracy of this prognostic model was further validated by receiver operating characteristic curve (ROC), C-index and Nomogram. In addition, the lncRNA/miRNA/mRNA competing endogenous RNA (ceRNA) network and protein−protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). Results: We constructed a prognostic model based on 15 cuproptosis-associated lncRNAs. The validation results showed that the risk score of the model (HR = 1.003, 95% CI = 1.001−1.004; p < 0.001) could serve as an independent prognostic factor with accurate and credible predictive power. The risk score had the highest AUC (0.793) among various factors such as risk score, stage, gender and age, also indicating that the model we constructed to predict patient survival was better than other clinical characteristics. Meanwhile, the possible biological mechanisms of colon adenocarcinoma were explored based on the lncRNA/miRNA/mRNA ceRNA network and PPI network constructed by WGCNA. Conclusion: The prognostic model based on 15 cuproptosis-related lncRNAs has accurate and reliable predictive power to effectively predict clinical outcomes in colon adenocarcinoma patients.

m6A-related lncRNAs are potential biomarkers for the prognosis of COAD patients

Background

Colon adenocarcinoma (COAD) is the most common subtype of colon cancer. However, the 5-year survival rate of COAD patients remains unsatisfactory. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play essential roles in the occurrence and development of COAD. Herein, we are committed to establish and validate a prognostic m6A-related lncRNA signature.

Methods

We obtained m6A-related lncRNAs by coexpression. The m6A-related lncRNA risk signature (m6ALncSig) was developed via univariate, LASSO, and multivariate Cox regression analyses. Kaplan-Meier (KM) survival curves, gene set enrichment analysis (GSEA), and nomogram generation were conducted to assess m6ALncSig. In addition, the potential immunotherapeutic signatures were also discussed. Real-time PCR and CCK8 analysis were performed to evaluate the expression and functions of lncRNA UBA6-AS1, which was selected.

Results

The risk signature comprising 14 m6A-related lncRNAs (m6ALncSig) was established, which possessed a superior predictive ability of prognosis. Meanwhile, m6ALncSig was linked to immune cell infiltration. The level of UBA6-AS1 expression was validated in 17 pairs of COAD samples. In cell function experiments, UBA6-AS1 knockdown attenuated cell proliferation capacity.

Conclusions

Collectively, m6ALncSig could serve as an independent predictive factor for COAD and accurately estimate the outcome for COAD patients. Importantly, UBA6-AS1 was first identified as an oncogene in COAD.

Identification of Autophagy-Related LncRNA to Predict the Prognosis of Colorectal Cancer

Objective: To establish a prediction model based on autophagy-related lncRNAs and investigate the functional enrichment of autophagy-related lncRNAs in colorectal cancer.

Methods: TCGA database was used to extract the transcriptome data and clinical features of colorectal cancer patients. HADb was used to obtain autophagy-related genes. Pearson correlation analysis was performed to identify autophagy-related lncRNAs. The autophagy-related lncRNAs with prognostic values were selected. Based on the selected lncRNAs, the risk score model and nomogram were constructed, respectively. Calibration curve, concordance index, and ROC curve were performed to evaluate the predictive efficacy of the prediction model. GSEA was performed to figure out the functional enrichment of autophagy-related lncRNAs.

Results: A total of 13413 lncRNAs and 938 autophagy-related genes were obtained. A total of 709 autophagy-related genes were identified in colon cancer tissues, and 11 autophagy-related lncRNAs (AL138756.1, LINC01063, CD27-AS1, LINC00957, EIF3J-DT, LINC02474, SNHG16, AC105219.1, AC068580.3, LINC02381, and LINC01011) were finally selected and set as prognosis-related lncRNAs. According to the risk score, patients were divided into the high-risk and low-risk groups, respectively. The survival K–M (Kaplan–Meier) curve showed the low-risk group exhibits better overall survival than the high-risk group. The AUCs under the ROC curves were 0.72, 0.814, and 0.83 at 1, 3, and 5 years, respectively. The C-index (concordance index) of the model was 0.814. The calibration curves at 1, 3, and 5 years showed the predicting values were consistent with the actual values. Functional enrichment analysis showed that autophagy-related lncRNAs were enriched in several pathways.

Conclusions: A total of 11 specific autophagy-related lncRNAs were identified to own prognostic value in colon cancer. The predicting model based on the lncRNAs and clinical features can effectively predict the OS. Furthermore, functional enrichment analysis showed that autophagy-related genes were enriched in various biological pathways.

A Novel Necroptosis-Related lncRNA Signature for Osteosarcoma

Backgrounds

Osteosarcoma (OS) is easy to metastasis. Necroptosis-related long noncoding RNA (lncRNA) (NRlncRNA) plays a vital role in the tumorigenesis of many malignant tumors. Nonetheless, there have been few studies investigating the relations between NRlncRNA and OS. During the investigation, NRlncRNAs in OS were confirmed and characterized and their relationships with prognoses were investigated.

Methods

NRlncRNAs were downloaded from The Cancer Genome Atlas (TCGA) OS expression data and clinical-pathological information. First, univariate Cox regression and LASSO regression analyses were used to screen for prognostic-related NRlncRNAs. Second, multivariate regression analyses were used to establish a prognostic nomogram for predicting individual survival probability. Survival analyses demonstrated that high-risk patients (HRPs) had a poor prognosis. In addition, gene set enrichment analyses (GSEA) were used to identify gene function in high- and low-risk groups based on the survival mode.

Results

The 7 NRlncRNAs (AC004812.2, AC022915.1, AC073073.2, AC090559.1, AL512330.1, DDN-AS1, and SENCR) were shown to have a distinct difference and were used to construct an NRlncRNA signature. Using the signature as a risk score was an independent factor for OS patients. The signature divided OS patients into the high- and low-risk groups. Furthermore, the seven lncRNAs were significantly enriched in cell migration and metabolism.

Conclusions

The 7 NRlncRNA survival models have the potential to serve as therapeutic targets and molecular biomarkers for patients with OS, as well as to precisely predict OS prognoses.

Risk Stratification and Validation of Eleven Autophagy-Related lncRNAs for Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC), the most prevalent subtype of esophageal cancer, ranks sixth in cancer-related mortality, making it one of the deadliest cancers worldwide. The identification of potential risk factors for ESCC might help in implementing precision therapies. Autophagy-related lncRNAs are a group of non-coding RNAs that perform critical functions in the tumor immune microenvironment and therapeutic response. Therefore, we aimed to establish a risk model composed of autophagy-related lncRNAs that can serve as a potential biomarker for ESCC risk stratification. Using the RNA expression profile from 179 patients in the GSE53622 and GSE53624 datasets, we found 11 lncRNAs (AC004690.2, AC092159.3, AC093627.4, AL078604.2, BDNF-AS, HAND2-AS1, LINC00410, LINC00588, PSMD6-AS2, ZEB1-AS1, and LINC02586) that were co-expressed with autophagy genes and were independent prognostic factors in multivariate Cox regression analysis. The risk model was constructed using these autophagy-related lncRNAs, and the area under the receiver operating characteristic curve (AUC) of the risk model was 0.728. To confirm that the model is reliable, the data of 174 patients from The Cancer Genome Atlas (TCGA) esophageal cancer dataset were analyzed as the testing set. A nomogram for ESCC prognosis was developed using the risk model and clinic-pathological characteristics. Immune function annotation and tumor mutational burden of the two risk groups were analyzed and the high-risk group displayed higher sensitivity in chemotherapy and immunotherapy. Expression of differentially expressed lncRNAs were further validated in human normal esophageal cells and esophageal cancer cells. The constructed lncRNA risk model provides a useful tool for stratifying risk and predicting the prognosis of patients with ESCC, and might provide novel targets for ESCC treatment.

Expression characteristics of long non-coding RNA in colon adenocarcinoma and its potential value for judging the survival and prognosis of patients: bioinformatics analysis based on The Cancer Genome Atlas database

Background

To investigate the expression characteristics of long non-coding RNA (lncRNA) in colon adenocarcinoma (COAD) and its potential value in predicting the prognosis of patient survival.

Methods

We downloaded COAD-related RNA sequencing (RNA-seq) data and patient survival data from The Cancer Genome Atlas (TCGA). The data were analyzed for lncRNA expression differences, subjected to Cox regression analysis for survival rate, and Kaplan-Meier (KM) survival curves were plotted to analyze the role of the key genes related to prognostic survival by pathway enrichment analysis.

Results

The data of 494 COAD clinical samples from TCGA were analyzed; 204 lncRNAs were differentially expressed, 156 were up-regulated, and 48 were down-regulated. The 10 genes with the most significant expression differences were Linc02418, Blacat1, ELFN1-AS1, CRNDE, AC002384.1, AL353801.1, LINC01645, AC073283.2, AC087379.1, and LINC00484. Cox regression analysis of 204 lncRNA genes showed that 23 lncRNA genes with significant effects on the prognosis and survival rate of COAD patients were obtained when P<0.05 was used as the threshold. With P≤0.001 as the threshold, the KM curves of 4 genes (Linc02257, Linc02474, Ac010789.1, Ac083967.1) were statistically significant (P<0.05). The gene Linc02257 was selected for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and it was revealed that the inheritance of Linc02257-regulated gene expression was closely related to tumor development, such as collagen-containing extracellular matrix, organogenesis, activity of membrane protein receptors, and ion channel activity. The signaling pathways regulated by Linc02257 were also closely related to tumors, such as neuroactive ligand-receptor interaction, the PI3K-AKT signaling pathway, calcium signaling pathway, and protein digestion and absorption.

Conclusions

In COAD, lncRNA is differentially expressed and plays an important role in the disease regulation. It has potential application value in the diagnosis, targeted therapy, and prognosis of COAD patients.

Ferroptosis-Related Long Noncoding RNAs as Prognostic Marker for Colon Adenocarcinoma

Background

The incidence of colon adenocarcinoma (COAD) has been increasing over time. Although ferroptosis and long noncoding RNAs (lncRNAs) have been extensively reported to participate in the tumorigenesis and development of COAD, few studies have investigated the role of ferroptosis-related lncRNAs in the prognosis of COAD.

Methods

Gene-sequencing and clinical data for COAD were obtained from The Cancer Genome Atlas database. The coexpression network was constructed using known ferroptosis-related genes. Cox and least absolute shrinkage and selection operator regression were used to screen ferroptosis-related lncRNAs with prognostic value and to identify a predictive model of COAD. Patients with COAD were divided into low- and high-risk groups according to their risk score. Cases of COAD in the International Cancer Genome Consortium database were included as the testing cohort.

Results

In total, nine lncRNAs (LINC02381, AC105219.1, AC009283.1, LINC01011, ELFN1-AS1, EIF3J-DT, NKILA, LINC01063, and SNHG16) were considered prognostic factors for COAD. Then, a risk score model was established. The overall survival rate of COAD patients was negatively associated with the risk score. Kaplan-Meier analyses in the original and testing cohorts showed similar results. The expression of the lncRNAs in tissue was consistent with the risk score, and the relationship with tumor mutation burden, immunity, and drug sensitivity presented a marked link between the signature and COAD. A nomogram was established for clinical applications.

Conclusions

Nine ferroptosis-related lncRNAs and the established signature have a certain predictive value for prognosis of COAD patients and can be used as potential research targets for exploring treatment of COAD.

Establishment and validation of a prognostic model based on HRR-related lncRNAs in colon adenocarcinoma

BACKGROUND

Colon cancer (CRC) is the second leading cause of cancer-related death, and its 5-year survival rate is very low. Homologous recombination repair (HRR) is deficient in most colon cancer. Some long non-coding RNAs (lncRNAs) participate in tumorigenesis of colon cancer through the HRR pathway. We aim to establish a prognostic model based on the HRR-related lncRNAs, expecting to provide a new strategy for precision treatment development in colon cancer.

METHODS

Pearson's correlation was used to identify the HRR-related prognostic lncRNAs in the TCGA-COAD cohort. The TCGA-COAD cohort was randomized into the training set and the testing set. LASSO Cox regression was used to establish the model which was analyzed in the training set and validated in the testing set and the entire TCGA-COAD cohort. Finally, we explored the potential biological function of our model.

RESULTS

A prognostic model was established based on nineteen HRR-related lncRNAs in the training set. COAD patients were scored by the uniform formula and divided into high-risk and low-risk groups based on the median risk score. Patients with high-risk scores indicated poor prognosis in the training set, and the result was confirmed in the testing set and the entire TCGA-COAD cohort (all p < 0.01). Multivariable analysis suggested that our model was an independent factor for overall survival in COAD. The area under the curve (AUC) and C-index indicated that our model had better predictive efficiency than other indicators in the TCGA-COAD cohort. Functional enrichment analysis suggested that our model was associated with the MAPK pathway in COAD. Besides, our model was positively correlated with the HRD scores.

CONCLUSION

A new prognostic model was established based on nineteen HRR-related lncRNAs which had excellent predictive efficiency on the prognosis of COAD. This prognostic model may provide a new strategy for prognostic prediction of COAD patients.

ISYNA1: An Immunomodulatory-Related Prognostic Biomarker in Colon Adenocarcinoma and Pan-Cancer

Background: Colon adenocarcinoma (COAD) is a common digestive system tumor in the world. However, the role and function of ISYNA1 (inositol-3-phosphate synthase 1) in COAD remain unclear. We aim to explore the role of ISYNA1 in pan-cancer, especially in COAD.

Methods: The expression, clinical characteristic, and prognosis of ISYNA1 in pan-cancer were evaluated using the TCGA (the Cancer Genome Atlas), GTEx (the Genotype-Tissue Expression), and CCLE (Cancer Cell Line Encyclopedia). Pathway enrichment analysis of ISYNA1 was conducted using the R package “clusterProfiler.” We analyzed the correlation between the immune cell infiltration level and ISYNA1 expression using two sources of immune cell infiltration data, including the TIMER online database and ImmuCellAI database.

Results: ISYNA1 was highly expressed in COAD and other cancer types compared with respective normal tissues. High ISYNA1 expression predicted poorer survival in COAD. We also found that ISYNA1 expression was positively correlated with the infiltration level of tumor-associated macrophages and tumor-associated fibroblasts in COAD.

Conclusion: In conclusion, our findings revealed ISYNA1 to be a potential prognostic biomarker in COAD. High ISYNA1 expression indicates the immunosuppressive microenvironment.

LncRNA ubiquitin-binding protein domain protein 10 antisense RNA 1 inhibits colon adenocarcinoma progression via the miR-515-5p/slit guidance ligand 3 axis

Dysregulated long non-coding RNAs (lncRNAs) play an important role in cancer progression. However, there have been limited reports to date of the involvement of ubiquitin-binding protein domain protein 10 antisense RNA 1 (UBXN10-AS1) in cancer. Our aim was to explore the role and underlying mechanism of UBXN10-AS1 in the occurrence of colon adenocarcinoma (COAD). Real-time quantitative PCR and Western blotting were performed to determine the expression of UBXN10-AS1, miR-515-5p, and Slit guidance ligand 3 (SLIT3). Cell Counting Kit-8 and wound healing scratch assays were performed to measure COAD cell proliferation and migration. A xenograft assay was performed to examine tumor growth in vivo. Luciferase reporter and RNA immunoprecipitation (RIP) assays were used to determine the binding interaction among miR-515-5p, UBXN10-AS1, and SLIT3. The results showed that UBXN10-AS1 and SLIT3 were expressed at low levels in COAD tissues, while miR-515-5p was expressed at high levels. UBXN10-AS1 overexpression suppressed tumor growth in vitro and in vivo. The luciferase reporter and RNA RIP assays demonstrated that UBXN10-AS1 targeted miR-515-5p, which in turn targeted SLIT3. Functionally, miR-515-5p overexpression reversed the inhibition of COAD cell proliferation and migration by UBXN10-AS1 overexpression, and SLIT3 overexpression counteracted the oncogenicity of miR-515-5p. Our study shows that UBXN10-AS1 modulates the miR-515-5p/SLIT3 axis, thereby resulting in the inhibition of COAD cell proliferation and migration.

Long Non-Coding RNA CD27-AS1-208 Facilitates Melanoma Progression by Activating STAT3 Pathway

Melanoma is the most lethal skin cancer that originates from epidermal melanocytes. Recently, long non-coding RNAs (lncRNAs) are emerging as critical regulators of cancer pathogenesis and potential therapeutic targets. However, the expression profile of lncRNAs and their role in melanoma progression have not been thoroughly investigated. Herein, we firstly obtained the expression profile of lncRNAs in primary melanomas using microarray analysis and unveiled the differentially-expressed lncRNAs compared with nevus. Subsequently, a series of bioinformatics analysis showed the great involvement of dysregulated lncRNAs in melanoma biology and immune response. Further, we identified lncRNA CD27-AS1-208 as a novel nuclear-localized factor with prominent facilitative role in melanoma cell proliferation, invasion and migration. Mechanistically, CD27-AS1-208 could directly interact with STAT3 and contribute to melanoma progression in a STAT3-dependent manner. Ultimately, the role of CD27-AS1-208 in melanoma progression in vivo was also investigated. Collectively, the present study offers us a new horizon to better understand the role of lncRNAs in melanoma pathogenesis and demonstrates that CD27-AS1-208 up-regulation contributes to melanoma progression by activating STAT3 pathway. Targeting CD27-AS1-208 in melanoma cells can be exploited as a potential therapeutic approach that needs forward validation in clinical trials in the future.

Over Fifty Years of Life, Death, and Cannibalism: A Historical Recollection of Apoptosis and Autophagy

Research in biomedical sciences has changed dramatically over the past fifty years. There is no doubt that the discovery of apoptosis and autophagy as two highly synchronized and regulated mechanisms in cellular homeostasis are among the most important discoveries in these decades. Along with the advancement in molecular biology, identifying the genetic players in apoptosis and autophagy has shed light on our understanding of their function in physiological and pathological conditions. In this review, we first describe the history of key discoveries in apoptosis with a molecular insight and continue with apoptosis pathways and their regulation. We touch upon the role of apoptosis in human health and its malfunction in several diseases. We discuss the path to the morphological and molecular discovery of autophagy. Moreover, we dive deep into the precise regulation of autophagy and recent findings from basic research to clinical applications of autophagy modulation in human health and illnesses and the available therapies for many diseases caused by impaired autophagy. We conclude with the exciting crosstalk between apoptosis and autophagy, from the early discoveries to recent findings.

Development and Validation of an Autophagy-Related LncRNA Prognostic Signature in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) is one of the greatest public challenges because of delayed diagnosis and poor prognosis. In this study, we established an autophagy-associated long non-coding (Lnc)RNA prognostic signature to assess the prognosis of HNSCC patients. The LncRNA expression profiles and clinical information of 499 HNSCC samples were available in The Cancer Genome Atlas. Autophagic LncRNAs were analyzed using Pearson correlation. A co-expression network showed the interactions between autophagic genes and LncRNAs. An autophagic LncRNAs prognostic signature, consisting of MYOSLID, AL139287.1, AC068580.1, AL022328.2, AC104083.1, AL160006.1, AC116914.2, LINC00958, and AL450992.2, was developed through uni- and multivariate Cox regressions. High- and low-risk groups were classified based on the median risk scores. The high-risk group had significantly worse overall survival according to Kaplan–Meier curve analysis. Multivariate Cox regression demonstrated that risk scores were a significant independent prognostic factor (hazard ratio = 1.739, 95% confidence interval: 1.460–2.072), with an area under the curve of 0.735. Principal component analysis distinguished two categories based on the nine-LncRNA prognostic signature. In conclusion, this novel autophagic LncRNA signature is an independent prognostic factor and may suggest novel therapeutic targets for HNSCC.

Identification of a Four-lncRNA Prognostic Signature for Colon Cancer Based on Genome Instability

LncRNAs (long noncoding RNAs) are closely associated with genome instability. However, the identification of lncRNAs related to the genome instability and their relationship with the prognosis and clinical signature of cancer remains to be explored. In this paper, we analyzed differential lncRNA expression based on the somatic mutation profiles of colon cancer patients from TCGA database and finally identified 153 lncRNAs that are associated with genome instability in colon cancer. Taking four lncRNAs from these 153, we established a genome-instability-related prognostic signature (GIRlncPSig). By applying the GIRlncPSig, we calculated a risk score for each patient, and using their risk scores, we divided them into low- and high-risk groups. We found that the prognosis between the two risk groups was significantly different, and the results were further verified in different independent patient cohorts. Moreover, we observed that the GIRlncPSig was related to somatic mutation rates in colon cancer, indicating that it may be a potential means of measuring genome instability levels in colon cancer. We also revealed that the GIRlncPSig was correlated with BRAF and DPYD mutation rates and that it may be a potential mutation marker for the BRAF and DPYD gene. In summary, we constructed a genome-instability-related lncRNA prognostic signature (GIRlncPSig), which has a significant effect on prognosis prediction and may allow for the discovery of new colon cancer biomarkers.

Identification of a Nomogram from Ferroptosis-Related Long Noncoding RNAs Signature to Analyze Overall Survival in Patients with Bladder Cancer

Purpose

This study aimed to establish a nomogram to predict the overall survival (OS) of patients with bladder cancer (BC) by ferroptosis-related long noncoding RNAs (FRlncRNAs) signature.

Methods

We obtained FRlncRNAs expression profiles and clinical data of patients with BC from the Cancer Genome Atlas database. The patients were divided into the training set, testing set, and overall set. Lasso regression and multivariate Cox regression were used to establish the FRlncRNAs signature, the prognosis of each group was compared by Kaplan–Meier (K-M) analysis, and the receiver operating characteristic (ROC) curve evaluated the accuracy of the model. The Gene Set Enrichment Analysis (GSEA) was used for the visualization of the functional enrichment for FRlncRNAs. The databases of GEPIA and K-M Plotter were used for subsequent functional analysis of major FRlncRNAs.

Results

Thirteen prognostic FRlncRNAs (LINC00942, MAFG-DT, AL049840.3, AL136084.3, OCIAD1-AS1, AC062017.1, AC008074.2, AC018653.3, AL031775.1, USP30-AS1, LINC01767, AC132807.2, and AL354919.2) were identified to be significantly different, constituting an FRlncRNAs signature. Patients with BC were divided into low-risk group and high-risk group by this signature in the training, testing, and overall sets. K-M analysis showed that the prognosis of patients in the high-risk group was poor and the difference in the subgroup analyses was statistically significant. ROC analysis revealed that the predictive ability of the model was more accurate than traditional assessment methods. A risk score based on FRlncRNAs signature was an independent prognostic factor for the patients with BC (HR = 1.388, 95%CI = 1.228–1.568, P < 0.001). Combining the FRlncRNAs signature and clinicopathological factors, a predictive nomogram was constructed. The nomogram can accurately predict the overall survival of patients and had high clinical practicability. The GSEA analysis showed that the primary pathways were WNT, MAPK, and cell-matrix adhesion signaling pathways. The major FRlncRNAs (MAFG-DT) were associated with poor prognosis in the GEPIA and K-M Plotter database.

Conclusion

Thirteen prognostic FRlncRNAs and their nomogram were accurate tools for predicting the OS of BC, which might be molecular biomarkers and therapeutic targets.

A Novel Signature Constructed by Immune-Related LncRNA Predicts the Immune Landscape of Colorectal Cancer

Colorectal cancer (CRC) has the characteristics of high morbidity and mortality. LncRNA not only participates in the progression of CRC through genes and transcription levels, but also regulates the tumor microenvironment and leads to the malignant phenotype of tumors. Therefore, we identified immune-related LncRNAs for the construction of clinical prognostic model. We searched The Cancer Genome Atlas (TCGA) database for original data. Then we identified differentially expressed irlncRNA (DEirlncRNA), which was paired and verified subsequently. Next, univariate analysis, Lasso and Cox regression analysis were performed on the DEirlncRNA pair. The ROC curve of the signature was drawn, and the optimal cut-off value was found. Then the cohort was divided into a high-risk and a low-risk group. Finally, we re-evaluated the signature from different perspectives. A total of 16 pairs of DEirlncRNA were included in the construction of the model. After regrouping according to the cut-off value of 1.275, the high-risk group showed adverse survival outcomes, progressive clinicopathological features, specific immune cell infiltration status, and high sensitivity to some chemotherapy drugs. In conclusion, we constructed a signature composed of immune-related LncRNA pair with no requirement of the specific expression level of genes, which shows promising clinical predictive value in CRC patients.

Integrative analysis of ceRNA network reveals functional lncRNAs associated with independent recurrent prognosis in colon adenocarcinoma

Background

Long non-coding RNAs (lncRNAs), acting as competing endogenous RNA (ceRNA) have been reported to regulate the expression of targeted genes by sponging miRNA in colon adenocarcinoma (COAD).

Methods

However, their potential implications for recurrence free survival prognosis and functional roles remains largely unclear in COAD. In this study, we downloaded the TCGA dataset (training dataset) and GSE39582 (validation dataset) of COAD patients with prognostic information.

Results

A total of 411 differentially expressed genes (DElncRNAs: 12 downregulated and 43 upregulated), 18 DE miRNAs (9 downregulated and 9 upregulated) and 338 DEmRNAs (113 downregulated and 225 upregulated) were identified in recurrence samples compared with non-recurrence samples with the thresholds of FDR < 0.05 and |log₂FC|> 0.263. Based on six signature lncRNAs (LINC00899, LINC01503, PRKAG2-AS1, RAD21-AS1, SRRM2-AS1 and USP30-AS1), the risk score (RS) system was constructed. Two prognostic clinical features, including pathologic stage and RS model status were screened for building the nomogram survival model. Moreover, a recurrent-specific ceRNA network was successfully constructed with 2 signature lncRNAs, 4 miRNAs and 113 mRNAs. Furthermore, we further manifested that SRRM2-AS1 predicted a poor prognosis in COAD patients. Furthermore, knockdown of SRRM2-AS1 significantly suppressed cell proliferation, migration, invasion and EMT markers in HT-29 and SW1116 cells.

Conclusion

These identified novel lncRNA signature and ceRNA network associated with recurrence prognosis might provide promising therapeutic targets for COAD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02069-6.

A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

Background

Some studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.

Method

A Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.

Results

A four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P < 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.

Conclusion

The four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.

A Prognostic Ferroptosis-Related lncRNAs Signature Associated With Immune Landscape and Radiotherapy Response in Glioma

Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are implicated in the regulation of tumor cell ferroptosis. However, the prognostic value of ferroptosis-related lncRNAs has never been comprehensively explored in glioma. In this study, the transcriptomic data and clinical information of glioma patients were downloaded from TCGA, CGGA and Rembrandt databases. We identified 24 prognostic ferroptosis-related lncRNAs, 15 of which (SNAI3-AS1, GDNF-AS1, WDFY3-AS2, CPB2-AS1, WAC-AS1, SLC25A21-AS1, ARHGEF26-AS1, LINC00641, LINC00844, MIR155HG, MIR22HG, PVT1, SNHG18, PAXIP1-AS2, and SBF2-AS1) were used to construct a ferroptosis-related lncRNAs signature (FRLS) according to the least absolute shrinkage and selection operator (LASSO) regression. The validity of this FRLS was verified in training (TCGA) and validation (CGGA and Rembrandt) cohorts, respectively. The Kaplan-Meier curves revealed a significant distinction of overall survival (OS) between the high- and low-risk groups. The receiver operating characteristic (ROC) curves exhibited robust prognostic capacity of this FRLS. A nomogram with improved accuracy for predicting OS was established based on independent prognostic factors (FRLS, age, and WHO grade). Besides, patients in the high-risk group had higher immune, stroma, and ESTIMATE scores, lower tumor purity, higher infiltration of immunosuppressive cells, and higher expression of immune checkpoints. Patients in the low-risk group benefited significantly from radiotherapy, while no survival benefit of radiotherapy was observed for those in the high-risk group. In conclusion, we identified the prognostic ferroptosis-related lncRNAs in glioma, and constructed a prognostic signature which was associated with the immune landscape of glioma microenvironment and radiotherapy response.

An epithelial-mesenchymal transition-related long non-coding RNA signature to predict overall survival and immune microenvironment in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (ccRCC) is a malignant tumor originating from renal tubular epithelium, lncRNAs can regulate the occurrence and development of EMT by targeting EMT transcription factors. We constructed a new survival signature based on EMT-related differentially expressed lncRNAs obtained from the Cancer Genome Atlas (TCGA-KIRC). We first determined 1377 EMT-related lncRNAs, lncRNA AL035661.1 with the largest correlation coefficient and the target gene was PFN2 (cor = 0.843; P= 1.37E-146). Meanwhile, we found an AUC of 0.758 in our signature and we predicted the AUC values of the patients’ 1, 2, 3-year survival rate as 0.768, 0.749, and 0.762 in TCGA cohort, respectively. Multivariate COX analysis was performed to determine if risk score was an independent prognostic predictor of OS. The results indicated that our risk score can be an independent predictor for OS (Univariate: HR = 1.350, 95% CI = 1.276–1.428, P< 0.001; Multivariate: HR = 1.295, 95% CI = 1.201–1.396, P< 0.001). We identified novel EMT-related lncRNAs markers for ccRCC prognosis. The underlying mechanism between EMT-related lncRNAs in ccRCC and tumor immunity is still unclear and requires further study.