N7-Methylguanosine-Related lncRNAs: Integrated Analysis Associated With Prognosis and Progression in Clear Cell Renal Cell Carcinoma

LASSO regression and WGCNA-based telomerase-associated lncRNA signaling predicts clear cell renal cell carcinoma prognosis and immunotherapy response

OBJECTIVE

To investigate whether telomerase-associated lncRNA expression affects the prognosis and anti-tumor immunity of patients with renal clear cell carcinoma (ccRCC).

METHODS

A series of analyses were performed to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to assess further the association between immune status, tumor microenvironment, and prognostic risk models.

RESULTS

Eight telomerase-associated lncRNAs associated with prognosis were identified and applied to establish a prognostic risk model. Overall survival was higher in the low-risk group.

CONCLUSION

The established prognostic risk model has a good predictive ability for the prognosis of ccRCC patients and provides a new possible therapeutic target for ccRCC.

Identification of N7-methylguanosine-related miRNAs as potential biomarkers for prognosis and drug response in breast cancer

Objectives

The impact of N7-methylguanosine (m7G) on tumor progression and the regulatory role of microRNAs (miRNAs) in immune function significantly influence breast cancer (BC) prognosis. Investigating the interplay between m7G modification and miRNAs provides novel insights for assessing prognostics and drug responses in BC.

Materials and methods

RNA sequences (miRNA and mRNA profiles) and clinical data for BC were acquired from the Cancer Genome Atlas (TCGA) database. A miRNA signature associated with 15 m7G in this cohort was identified using Cox regression and LASSO. The risk score model was evaluated using Kaplan-Meier and time-dependent ROC analysis, categorizing patients into high-risk and low-risk groups. Functional enrichment analyses were conducted to explore potential pathways. The immune system, including scores, cell infiltration, function, and drug sensitivity, was examined and compared between high-risk and low-risk groups. A nomogram that combines risk scores and clinical factors was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) was employed to explore m7G-related miRNA signatures and immune cell relationships in the tumor microenvironment. Additionally, drug susceptibility was compared between risk groups.

Results

Fifteen m7G-related miRNAs were independently correlated with overall survival (OS) in BC patients. Time-dependent ROC analysis yielded area under the curve (AUC) values of 0.742, 0.726, and 0.712 for predicting 3-, 5-, and 10-year survival rates, respectively. The Kaplan-Meier analysis revealed a significant disparity in OS between the high-risk and low-risk groups (p = 1.3e-6). Multiple regression identified the risk score as a significant independent prognostic factor. An excellent calibration nomogram with a C-index of 0.785 (95 % CI: 0.728-0.843) was constructed. In immune analysis, low-risk patients exhibited heightened immune function and increased responsiveness to immunotherapy and chemotherapy compared to high-risk patients.

Conclusion

This study systematically analyzed m7G-related miRNAs and revealed their regulatory mechanisms concerning the tumor microenvironment (TME), pathology, and the prognosis of BC patient. Based on these miRNAs, a prognostic model and nomogram were developed for BC patients, facilitating prognostic assessments. These findings can also assist in predicting treatment responses and guiding medication selection.

A novel prognostic N7-methylguanosine-related long non-coding RNA signature in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is regulated by methylation modifications and long noncoding RNAs (lncRNAs). However, knowledge of N7-methylguanosine (m7G)-related lncRNAs that predict ccRCC prognosis remains insufficient. A prognostic multi-lncRNA signature was created using LASSO regression to examine the differential expression of m7G-related lncRNAs in ccRCC. Furthermore, we performed Kaplan-Meier analysis and area under the curve (AUC) analysis for diagnosis. In all, a model based on five lncRNAs was developed. Principal component analysis (PCA) indicated that the risk model precisely separated the patients into different groups. The IC50 value for drug sensitivity divided patients into two risk groups. High-risk group of patients was more susceptible to A.443654, A.770041, ABT.888, AMG.706, and AZ628. Moreover, a lower tumor mutation burden combined with low-risk scores was associated with a better prognosis of ccRCC. Quantitative real-time polymerase chain reaction (qRT-PCR) exhibited that the expression levels of LINC01507, AC093278.2 were very high in all five ccRCC cell lines, AC084876.1 was upregulated in all ccRCC cell lines except 786-O, and the levels of AL118508.1 and DUXAP8 were upregulated in the Caki-1 cell line. This risk model may be promising for the clinical prediction of prognosis and immunotherapeutic responses in patients with ccRCC.

Underexplored reciprocity between genome-wide methylation status and long non-coding RNA expression reflected in breast cancer research: potential impacts for the disease management in the framework of 3P medicine

Breast cancer (BC) is the most common female malignancy reaching a pandemic scale worldwide. A comprehensive interplay between genetic alterations and shifted epigenetic regions synergistically leads to disease development and progression into metastatic BC. DNA and histones methylations, as the most studied epigenetic modifications, represent frequent and early events in the process of carcinogenesis. To this end, long non-coding RNAs (lncRNAs) are recognized as potent epigenetic modulators in pathomechanisms of BC by contributing to the regulation of DNA, RNA, and histones' methylation. In turn, the methylation status of DNA, RNA, and histones can affect the level of lncRNAs expression demonstrating the reciprocity of mechanisms involved. Furthermore, lncRNAs might undergo methylation in response to actual medical conditions such as tumor development and treated malignancies. The reciprocity between genome-wide methylation status and long non-coding RNA expression levels in BC remains largely unexplored. Since the bio/medical research in the area is, per evidence, strongly fragmented, the relevance of this reciprocity for BC development and progression has not yet been systematically analyzed. Contextually, the article aims at:consolidating the accumulated knowledge on both-the genome-wide methylation status and corresponding lncRNA expression patterns in BC andhighlighting the potential benefits of this consolidated multi-professional approach for advanced BC management. Based on a big data analysis and machine learning for individualized data interpretation, the proposed approach demonstrates a great potential to promote predictive diagnostics and targeted prevention in the cost-effective primary healthcare (sub-optimal health conditions and protection against the health-to-disease transition) as well as advanced treatment algorithms tailored to the individualized patient profiles in secondary BC care (effective protection against metastatic disease). Clinically relevant examples are provided, including mitochondrial health control and epigenetic regulatory mechanisms involved.

An innovative model based on N7-methylguanosine-related lncRNAs for forecasting prognosis and tumor immune landscape in bladder cancer

BACKGROUND

As a novel type of the prevalent post-transcriptional modifications, N7-methylguanosine (m7G) modification is essential in the tumorigenesis, progression, and invasion of many cancers, including bladder cancer (BCa). However, the integrated roles of m7G-related lncRNAs in BCa remain undiscovered. This study aims to develop a prognostic model based on the m7G-related lncRNAs and explore its predictive value of the prognosis and anti-cancer treatment sensitivity.

METHODS

We obtained RNA-seq data and corresponding clinicopathological information from the TCGA database and collected m7G-related genes from previous studies and GSEA. Based on LASSO and Cox regression analysis, we developed a m7G prognostic model. The Kaplan-Meier (K-M) survival analysis and ROC curves were performed to evaluate the predictive power of the model. Gene set enrichment analysis (GSEA) was conducted to explore the molecular mechanisms behind apparent discrepancies between the low- and high-risk groups. We also investigated immune cell infiltration, TIDE score, TMB, the sensitivity of common chemotherapy drugs, and the response to immunotherapy between the two risk groups. Finally, we validated the expression levels of these ten m7G-related lncRNAs in BCa cell lines by qRT-PCR.

RESULTS

We developed a m7G prognostic model (risk score) composed of 10 m7G-related lncRNAs that are significantly associated with the OS of BCa patients. The K-M survival curves revealed that the high-risk group patients had significantly worse OS than those in the low-risk group. The Cox regression analysis confirmed that the risk score was a significant independent prognostic factor for BCa patients. We found that the high-risk group had higher the immune scores and immune cell infiltration. Furthermore, the results of the sensitivity of common anti-BCa drugs showed that the high-risk group was more sensitive to neoadjuvant cisplatin-based chemotherapy and anti-PD1 immunotherapy. Finally, qRT-PCR revealed that AC006058.1, AC073133.2, LINC00677, and LINC01338 were significantly downregulated in BCa cell lines, while the expression levels of AC124312.2 and AL158209.1 were significantly upregulated in BCa cell lines compared with normal cell lines.

CONCLUSION

The m7G prognostic model can be applied to accurately predict the prognosis and provide robust directions for clinicians to develop better individual-based and precise treatment strategies for BCa patients.

M7G-related molecular subtypes can predict the prognosis and correlate with immunotherapy and chemotherapy responses in bladder cancer patients

BACKGROUND

N7-methylguanosine (m7G) is closely associated with tumor prognosis and immune response in many cancer types. The correlation between m7G and bladder cancer (BC) needs further study. We aimed to orchestrate molecular subtypes and identify key genes for BC from the perspective of m7G.

METHODS

RNA-seq and clinical data of BC patients were extracted from TCGA and GSE13507 datasets. The patients were subtyped by "ConsensusClusterPlus" and "limma." The clusters were validated by the Kaplan‒Meier curves, univariable and multivariate Cox regression models, the concordance index, and calibration curves. The immunotherapy response was evaluated by immune checkpoints, immune infiltration, TIDE score, and IMvigor210 cohort. Genomics of Drug Sensitivity in Cancer was utilized to predict the chemotherapy response between the clusters.

RESULTS

The m7G-related cluster was ultimately established by EIF4G1, NUDT11, NUDT10, and CCNB1. The independent prognostic value of the m7G-related cluster was validated by the TCGA and GSE13507 datasets. The cluster was involved in immune-associated pathways, such as neutrophil degranulation, antigen processing cross-presentation, and signaling by interleukins pathways. Meanwhile, cluster 2 was positively correlated with many immune checkpoints, such as CD274, CTLA4, HAVCR2, LAG3, PDCD1, and PDCD1LG2. The cluster 2 was significantly correlated with a higher TIDE score than the cluster 1. Furthermore, in the IMvigor210 cohort, patients in the cluster 1 had a higher response rate than those in the cluster 2. Patients in the cluster 2 were sensitive to many chemotherapies.

CONCLUSIONS

We successfully determined molecular subtypes and identified key genes for BC from the perspective of m7G, thereby providing a roadmap for the evolution of immunotherapy and precision medicine.

Characterization of immune landscape and development of a novel N7-methylguanine-related gene signature to aid therapy in recurrent aphthous stomatitis

OBJECTIVES

Recurrent aphthous stomatitis (RAS) is the most common inflammatory disease of the oral mucosa resulting in an impaired life quality and even leading to tumors in susceptible populations. N7-Methylguanine (m7G) plays a vital role in various cellular activities but has not yet been investigated in RAS. We aimed at picturing the immune landscape and constructing an m7G-related gene signature, and investigating candidate drugs and gene-disease association to aid therapy for RAS.

METHODS

For our study, m7G-related differentially expressed genes (DEGs) were screened. We outlined the immune microenvironment and studied the correlations between the m7G-related DEGs and immune cells/pathways. We performed functional enrichment analyses and constructed the protein-protein interaction (PPI) and multifactor regulatory network in RAS. The m7G-related hub genes were extracted to formulate the corresponding m7G predictive signature.

RESULTS

We obtained 11 m7G-related DEGs and studied a comprehensive immune infiltration landscape, which indicated several immune markers as possible immunotherapeutic targets. The PPI and multifactor regulatory network was constructed and 4 hub genes (DDX58, IFI27, IFIT5, and PML) were identified, followed by validation of the corresponding m7G predictive signature for RAS. GO and KEGG analyses revealed the participation of JAK-STAT and several immune-related pathways. Finally, we suggested candidate drugs and gene-disease associations for potential RAS medical interventions.

CONCLUSIONS

The present study pictured a comprehensive immune infiltration landscape and suggested that m7G played a vital role in RAS through immune-related pathways. This study provided new insight for the future investigation of the mechanisms and therapeutic strategies for RAS.

Construction and Validation of Protein Expression-related Prognostic Models in Clear Cell Renal Cell Carcinoma

Objective: To construct a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics method and to screen potential drugs for ccRCC. Methods: ccRCC RNA sequencing data, clinical data, and protein expression data were downloaded from the TCGA database. Univariate Cox and Lasso regression analyses were performed on the combined data to screen out the proteins related to the prognosis, and they were included in a multivariate Cox proportional hazard model. The patients were divided into high and low-risk groups for a survival difference analysis. The predictive power of the model was evaluated on the basis of overall survival, progression-free survival, independent prognostic, clinically relevant receiver operating characteristic (ROC) curve, C-index, principal component, and clinical data statistics analyses. GSEA enrichment and immune function correlation analyses were performed. The samples were divided into different subtypes based on the expression of the risk proteins, and survival analysis of the subtypes was performed. The risk-related protein and RNA sequencing data were analyzed to screen out sensitive drugs with significant differences between the high and low-risk groups. Results: A total of 469 ccRCC-related proteins were screened, of which 13 proteins with independent prognostic significance were screened by univariate Cox, Lasso, and multivariate Cox regression analyses to construct the prognostic model. The sensitivity and accuracy of the model in predicting the survival of patients with ccRCC were high (1 year: 0.811, 3 years: 0.783, 5 years: 0.777). The 13 proteins were closely related to immunity, and the model proteins were different between kidney and tumor tissues according to the HPA database. The samples were divided into three subtypes, and there were obvious clinical characteristics of the three subtypes in the grade and T, N and M stages. According to the IC50 values, CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, pyrimethamine, and other drugs were more sensitive in the high-risk group. Conclusions: A prognostic model of protein expression in ccRCC was successfully constructed, which had good predictive ability for the prognosis of ccRCC patients. The ccRCC-related proteins in the model can be used as targets for studying the pathogenesis and targeted therapy.

A telomerase regulation-related lncRNA signature predicts prognosis and immunotherapy response for gastric cancer

BACKGROUND

Telomeres are involved in the development and progression of gastric cancer (GC). However, the association of telomerase regulation-related lncRNAs with prognosis and immunotherapy responsiveness in gastric cancer is unclear.

METHODS

This study systematically evaluated the relationship between lncRNAs co-expressed with 67 telomerase regulatory genes and gastric cancer prognosis. The risk scores of the samples were calculated based on telomerase regulation-related lncRNAs with prognostic value, and the samples were classified into high-/low-risk groups. The prognostic value of risk groups was then evaluated, a GC prognostic prediction model based on risk groups and clinical characteristics was established, and the prediction accuracy of the model was clarified by receiving operating characteristic (ROC) curves and calibration curves. Finally, the value of risk grouping in GC immunotherapy sensitivity was predicted by comparing MSI status and tumor mutation load between the high- and low-risk groups.

RESULTS

We identified 13 lncRNAs with prognostic value co-expressed with telomerase regulatory genes and observed that the prognosis of the low-risk group was significantly better than that of the high-risk group. Meanwhile, a GC overall survival (OS) prediction model based on risk grouping and clinical characteristics was developed, and ROC curves and calibration curves confirmed the good predictive ability of the model. In addition, the low-risk group exhibited a higher tumor mutation load and MSI-H, suggesting a possible benefit of immunotherapy.

CONCLUSION

We found that telomerase regulation-related lncRNAs have prognostic value in GC patients and contribute to the exploration of more effective immunotherapeutic strategies.

m7G-related lncRNAs are potential biomarkers for predicting prognosis and immune responses in patients with oral squamous cell carcinoma

Among head and neck cancers, oral squamous cell carcinoma (OSCC) is the most common malignant tumor. N-7-methylguanosine (m7G) and lncRNAs are both related to the development and progression of tumors. Therefore, this study aims to explore and establish the prognostic signal of OSCC based on m7G-related lncRNAs. In this study, RNA sequencing transcriptome data of OSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Therefore, m7G-related lncRNAs were identified as differentially expressed in OSCC. Then, univariate Cox regression analysis and LASSO regression analysis were conducted to evaluate the prognostic significance of differentially expressed lncRNAs. Consequently, the abovementioned lncRNAs were assigned five OSCC patient risk scores, with high-risk and low-risk patients assigned to each group. Different signaling pathways were significantly enriched between the two groups as determined by set enrichment analysis (GSEA). Multivariate Cox regression analysis confirmed the factors used to construct the nomogram model. Then, the prognosis of the nomogram model was evaluated. Consequently, high-risk individuals had higher immune infiltration levels. According to the results of a study that evaluated the sensitivity of different risk subgroups to antitumour drugs, the high-risk group had a high sensitivity to doxorubicin. By performing real-time polymerase chain reaction (RT‒PCR), we verified the expression of these five m7G lncRNAs. Therefore, the model based on five m7G-related lncRNAs was able to predict the overall survival rates of OSCC patients and guide their treatment. It can also spur new ideas about how to prevent and treat OSCC.

A Novel m7G-Related Gene Signature Predicts the Prognosis of Colon Cancer

Colon cancer (CC), one of the most common malignancies worldwide, lacks an effective prognostic prediction biomarker. N7-methylguanosine (m7G) methylation is a common RNA modification type and has been proven to influence tumorigenesis. However, the correlation between m7G-related genes and CC remains unclear. The gene expression levels and clinical information of CC patients were downloaded from public databases. Twenty-nine m7G-related genes were obtained from the published literature. Via unsupervised clustering based on the expression levels of m7G-related genes, CC patients were divided into three m7G clusters. Based on differentially expressed genes (DEGs) from the above three groups, CC patients were further divided into three gene clusters. The m7G score, a prognostic model, was established using principal component analysis (PCA) based on 15 prognosis-associated m7G genes. KM curve analysis demonstrated that the overall survival rate was remarkably higher in the high-m7G score group, which was much more significant in advanced CC patients as confirmed by subgroup analysis. Correlation analysis indicated that the m7G score was associated with tumor mutational burden (TMB), PD-L1 expression, immune infiltration, and drug sensitivity. The expression level of prognosis-related m7G genes was further confirmed in human CC cell lines and samples. This study established an m7G gene-based prognostic model (m7G score), which demonstrated the important roles of m7G-related genes during CC initiation and progression. The m7G score could be a practical biomarker to predict immunotherapy response and prognosis in CC patients.

Prognostic value and immunological characteristics of a novel cuproptosis-related long noncoding RNAs risk signature in kidney renal clear cell carcinoma

Background: Cuproptosis has been found as a novel cell death mode significantly associated with mitochondrial metabolism, which may be significantly associated with the occurrence and growth of tumors. LncRNAs take on critical significance in regulating the development of kidney renal clear cell carcinoma (KIRC), whereas the correlation between cuproptosis-related LncRNAs (CRLs) and KIRC is not clear at present. Therefore, this study built a prognosis signature based on CRLs, which can achieve accurate prediction of the outcome of KIRC patients. Methods: The TCGA database provided the expression profile information and relevant clinical information of KIRC patients. Univariate Cox, Lasso, and multivariate Cox were employed for building a risk signature based on CRLs. Kaplan-Meier (K-M) survival analysis and time-dependent receiver operating characteristic (ROC) curve were employed for the verification and evaluation of the reliability and accuracy of risk signature. Then, qRT-PCR analysis of risk LncRNAs was conducted. Finally, the possible effect of the developed risk signature on the microenvironment for tumor immunization was speculated in accordance with ssGSEA and ESTIMATE algorithms. Results: A prognosis signature composed of APCDD1L-DT, MINCR, AL161782.1, and AC026401.3 was built based on CRLs. As revealed by the results of the K-M survival study, the OS rate and progression-free survival rate of highrisk KIRC patients were lower than those of lowrisk KIRC patients, and the areas under ROC curves of 1, 3, and 5 years were 0.828, 0.780, and 0.794, separately. The results of the immune analysis showed that there were significant differences in the status of immunization and the microenvironment of tumor between groups at low-risk and at high-risk. The qRT-PCR results showed that the relative expression level of MINCR and APCDD1L-DT were higher in 786-O and 769-P tumor cells than in HK-2 cells, which were normal renal tubular epithelial cells. Conclusion: The developed risk signature takes on critical significance in the prediction of the prognosis of patients with KIRC, and it can bring a novel direction for immunotherapy and clinical drug treatment of KIRC. In addition, 4 identified risk LncRNAs (especially APCDD1L-DT and MINCR) can be novel targets for immunotherapy of KIRC patients.

A Novel m7G-Related Genes-Based Signature with Prognostic Value and Predictive Ability to Select Patients Responsive to Personalized Treatment Strategies in Bladder Cancer

Although N7-methylguanosine (m7G) modification serves as a tumor promoter in bladder cancer (BLCA), the comprehensive role of m7G-related characterization in BLCA remains unclear. In this study, we systematically evaluated the m7G-related clusters of 760 BLCA patients through consensus unsupervised clustering analysis. Next, we investigated the underlying m7G-related genes among these m7G-related clusters. Univariate Cox and LASSO regressions were used for screening out prognostic genes and for reducing the dimension, respectively. Finally, we developed a novel m7G-related scoring system via the GSVA algorithm. The correlation between tumor microenvironment, prediction of personalized therapies and this m7G-related signature was gradually revealed. We first identified three m7G-related clusters and 1108 differentially expressed genes relevant to the three clusters. Based on the profile of 1108 genes, we divided BLCA patients into two clusters, which were quantified by our established m7G-related scoring system. Patients with higher m7G-related scores tended to have a better OS and more chances to benefit from immunotherapy. A significantly negative connection between sensitivity to classic chemotherapeutic drugs and m7G-related signature was uncovered. In summary, our data show that m7G-related characterization of BLCA patients can be of value for prognostic stratification and for patient-oriented therapeutic options, designing personalized treatment strategies in the preclinical setting.

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.

Establishment of m7G-related gene pair signature to predict overall survival in colorectal cancer

Background: N7-methylguanosine (m7G) is an emerging research hotspot in the field of RNA methylation, and its role in tumor regulation is becoming increasingly recognized. However, its role in colorectal cancer (CRC) remains unclear. Hence, our study explored the role of m7G in CRC.

Methods: The mRNA expression data and the corresponding clinical information of the patients with CRC were obtained from The Cancer Genome Atlas (TCGA). A m7G-related gene pair signature was established using the Cox and LASSO regression analyses. A series of in silico analyses based on the signature included analysis of prognosis, correlation analysis, immune-related analysis, and estimation of tumor mutational burden (TMB), microsatellite instability (MSI), and response to immunotherapy. A nomogram prediction model was then constructed.

Results: In total, 2156 m7G-related gene pairs were screened based on 152 m7G-related genes. Then, a prognostic signature of seven gene pairs was constructed, and the patients were stratified into high- or low-risk groups. Better overall survival (OS), left-sided tumor, early stage, immune activity, and low proportion of MSI-low and MSI-high were all associated with a low risk score. High-risk patients had a higher TMB, and patients with a high TMB had a poor OS. Furthermore, the risk score was linked to immune checkpoint expression (including PD-L1), the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapy sensitivity. We also created an accurate nomogram to increase the clinical applicability of the risk score.

Conclusion: We identified an m7G pair-based prognostic signature associated with prognosis, immune landscape, immunotherapy, and chemotherapy in CRC. These findings could help us to better understand the role of m7G in CRC, as well as pave the path for novel methods to assess prognosis and design more effective individualized therapeutic strategies.

A novel prognostic signature based on N7-methylguanosine-related long non-coding RNAs in breast cancer

Long non-coding RNA (lncRNA) are closely associated with the occurrence and progression of tumors. However, research on N7-methylguanosine (m7G)-related lncRNA in breast cancer is lacking. Therefore, the present study explored the prognostic value, gene expression characteristics, and effects of m7G-related lncRNA on tumor immune cell infiltration and tumor mutational burden (TMB) in breast cancer. lncRNA expression matrices and clinical follow-up data of patients with breast cancer were obtained from The Cancer Genome Atlas, revealing eight significantly differentially expressed and prognostically relevant m7G-related lncRNAs in breast cancer tissues: BAIAP2-DT, COL4A2-AS1, FARP1-AS1, RERE-AS1, NDUFA6-DT, TFAP2A-AS1, LINC00115, and MIR302CHG. A breast cancer prognostic signature was created based on these m7G-related lncRNAs according to least absolute shrinkage and selection operator Cox regression. The prognostic signature combined with potential prognostic factors showed independent prognostic value, reliability, and specificity. Meanwhile, we constructed a risk score-based nomogram to assist clinical decision-making. Gene set enrichment analysis revealed that low- and high-risk group were associated with metabolism-related pathways. Our study demonstrated the association between tumor immune cell infiltration based on analyses with the CIBERSORT algorithm and prognostic signature. We also assessed the correlation between prognostic signature and TMB. Lastly, quantitative real-time polymerase chain reaction analysis was performed to validate differentially expressed lncRNAs. The effective prognostic signature based on m7G-related lncRNAs has the potential to predict the survival prognosis of patients with breast cancer. The eight m7G-related lncRNAs identified in this study might represent potential biomarkers and therapeutic targets of breast cancer.