Immune signature of T follicular helper cells predicts clinical prognostic and therapeutic impact in lung squamous cell carcinoma

Consensus clustering and novel risk score model construction based on m6A methylation regulators to evaluate the prognosis and tumor immune microenvironment of early-stage lung adenocarcinoma

BACKGROUND

The aim of this study was to investigate the correlation between m6A methylation regulators and cell infiltration characteristics in tumor immune microenvironment (TIME), so as to help understand the immune mechanism of early-stage lung adenocarcinoma (LUAD).

METHODS

The expression and consensus cluster analyses of m6A methylation regulators in early-stage LUAD were performed. The clinicopathological features, immune cell infiltration, survival and functional enrichment in different subtypes were analyzed. We also constructed a prognostic model. Clinical tissue samples were used to validate the expression of model genes through real-time polymerase chain reaction (RT-PCR). In addition, cell scratch assay and Transwell assay were also performed.

RESULTS

Expression of m6A methylation regulators was abnormal in early-stage LUAD. According to the consensus clustering of m6A methylation regulators, patients with early-stage LUAD were divided into two subtypes. Two subtypes showed different infiltration levels of immune cell and survival time. A prognostic model consisting of HNRNPC, IGF2BP1 and IGF2BP3 could be used to predict the survival of early-stage LUAD. RT-PCR results showed that HNRNPC, IGF2BP1 and IGF2BP3 were significantly up-regulated in early-stage LUAD tissues. The results of cell scratch assay and Transwell assay showed that overexpression of HNRNPC promotes the migration and invasion of NCI-H1299 cells, while knockdown HNRNPC inhibits the migration and invasion of NCI-H1299 cells.

CONCLUSIONS

This work reveals that m6A methylation regulators may be potential biomarkers for prognosis in patients with early-stage LUAD. Our prognostic model may be of great value in predicting the prognosis of early-stage LUAD.

Development and validation of a novel prognostic lncRNA signature based on the APOBEC3 family genes in gastric cancer

Introduction

Gastric Cancer (GC) refers to a prevalent malignant cancer accompanied by a weak prognosis. The APOBEC3 family genes and lncRNAs are linked with cancer progression. Nevertheless, there is still a scarcity of data concerning the prognostic value of APOBEC3-related lncRNAs in GC.

Methods

We extracted the data from GC samples, including transcriptome as well as clinical data, obtained from the TCGA database. Then, we screened for lncRNAs that were correlated with the APOBEC3 family genes and constructed an APOBEC3-related lncRNA prognostic signature (LPS) by utilizing univariate Cox and lasso regression analysis. Furthermore, we validated our constructed signature and evaluated it thoroughly, including analysis of its function, immunity, mutations, and clinical applications via multiple methods, including Metascape, GSEA, and analyses including TIC and TME, immune checkpoints, CNV and SNPs, Kaplan-Meier survival curves, nomogram, decision tree and drug prediction analysis. Finally, we overexpressed LINC01094 to evaluate the impacts on the proliferation as well as migration with regards to KATO-2 cells.

Results

We selected eight lncRNAs for our APOBEC3-related LPS, which is demonstrated as a valuable tool in predicting the individual GC patients' prognosis. Subsequently, we segregated the samples into subgroups of high-as well as low-risk relying on the risk score with regards to APOBEC3-related LPS. By performing functional analysis, we have shown that immune-as well as tumor-related pathways were enriched in high- and low-risk GC patients. Furthermore, immune analysis revealed a robust correlation between the APOBEC3-related LPS and immunity. We found that immune checkpoints were significantly associated with the APOBEC3-related LPS and were greatly exhibited in GC tumor and high-risk samples. Mutational analysis suggested that the mutational rate was greater in low-risk samples. Furthermore, we predicted small molecular drugs displayed greater sensitivity in patients categorized as high-risk. Moreover, the immune response was also better in high-risk patients. Of these drugs, dasatinib was significant in both methods and might be considered a potential novel drug for treating high-risk GC patients. Finally, we found that LINC01094 has the potential to enhance the migration, proliferation as well as inhibit apoptosis of KATO-2 in GC cells. And Dasatinib has an inhibitory effect on the migration as well as proliferation in GC cells.

Conclusion

We created a novel APOBEC3-related LPS in predicting the prognosis with regards to individual GC patients. Importantly, this APOBEC3-related LPS was closely associated with immunity and might guide clinical treatment.

A cuproptosis score model and prognostic score model can evaluate clinical characteristics and immune microenvironment in NSCLC

BACKGROUND

Cuproptosis-related genes (CRGs) are associated with lung adenocarcinoma. However, the links between CRGs and non-small-cell lung cancer (NSCLC) are not clear. In this study, we aimed to develop two cuproptosis models and investigate their correlation with NSCLC in terms of clinical features and tumor microenvironment.

METHODS

CRG expression profiles and clinical data from NSCLC and normal tissues was obtained from GEO (GSE42127) and TCGA datasets. Molecular clusters were classified into three patterns based on CRGs and cuproptosis cluster-related specific differentially expressed genes (CRDEGs). Then, two clinical models were established. First, a prognostic score model based on CRDEGs was established using univariate/multivariate Cox analysis. Then, through principal component analysis, a cuproptosis score model was established based on prognosis-related genes acquired via univariate analysis of CRDEGs. NSCLC patients were divided into high/low risk groups.

RESULTS

Eighteen CRGs were acquired, all upregulated in tumor tissues, 15 of which significantly (P < 0.05). Among the three CRG clusters, cluster B had the best prognosis. In the CRDEG clusters, cluster C had the best survival. In the prognostic score model, the high-risk group had worse prognosis, higher tumor mutation load, and lower immune infiltration while in the cuproptosis score model, a high score represented better survival, lower tumor mutation load, and high-level immune infiltration.

CONCLUSIONS

The cuproptosis score model and prognostic score model may be associated with NSCLC prognosis and immune microenvironment. These novel findings on the progression and immune landscape of NSCLC may facilitate the provision of more personalized immunotherapy interventions for NSCLC patients.

Efficacy of immune checkpoint inhibitors in non-small cell lung cancer with NTRK family mutations

BACKGROUND

The efficacy of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) patients harboring neurotrophin receptor kinase (NTRK) family mutations remains obscure.

METHODS

The Zehir cohort from cBioPortal was used to analyze the mutations (MT) frequency of NTRK family in patients with NSCLC, and their correlation with clinical characteristics and patient survival. The influence of NTRK MT on ICIs efficacy was evaluated in ICIs-treated patients from Samstein cohort and further validated by use of data from OAK/POPLAR cohort.

RESULTS

In the Zehir cohort, a significant difference was observed in median overall survival (mOS) between patients with NTRK MT and wild-type (WT) (mOS: 18.97 vs. 21.27 months, HR = 1.34, 95%CI 1.00-1.78; log-rank P = 0.047). In Samstein cohort, the mOS of NTRK mutant patients receiving ICIs has improved compared to WT patients (mOS: 21.00 vs. 11.00 months, log-rank P = 0.103). Notably, in subgroup analysis, ICIs significantly prolonged mOS in patients with NTRK3 MT than in WT patients (mOS: not available vs. 11.00 months, HR = 0.36, 95%CI 0.16-0.81; log-rank P = 0.009). Identical mOS between NTRK MT and WT patients receiving ICIs treatment (mOS: 13.24 vs. 13.50 months, log-rank P = 0.775) was observed in OAK/POPLAR cohort. Moreover, a similar programmed death ligand 1 (PD-L1) expression, but higher tumor mutational burden (TMB), blood TMB (bTMB) and enriched anti-tumor immunity were observed in NTRK MT compared to WT (P < 0.05).

CONCLUSION

Taking high TMB or bTMB into consideration, patients with NTRK mutant NSCLC could benefit from ICIs treatment.

A programmed cell death-related model based on machine learning for predicting prognosis and immunotherapy responses in patients with lung adenocarcinoma

Background

lung adenocarcinoma (LUAD) remains one of the most common and lethal malignancies with poor prognosis. Programmed cell death (PCD) is an evolutionarily conserved cell suicide process that regulates tumorigenesis, progression, and metastasis of cancer cells. However, a comprehensive analysis of the role of PCD in LUAD is still unavailable.

Methods

We analyzed multi-omic variations in PCD-related genes (PCDRGs) for LUAD. We used cross-validation of 10 machine learning algorithms (101 combinations) to synthetically develop and validate an optimal prognostic cell death score (CDS) model based on the PCDRGs expression profile. Patients were classified based on their median CDS values into the high and low-CDS groups. Next, we compared the differences in the genomics, biological functions, and tumor microenvironment of patients between both groups. In addition, we assessed the ability of CDS for predicting the response of patients from the immunotherapy cohort to immunotherapy. Finally, functional validation of key genes in CDS was performed.

Results

We constructed CDS based on four PCDRGs, which could effectively and consistently stratify patients with LUAD (patients with high CDS had poor prognoses). The performance of our CDS was superior compared to 77 LUAD signatures that have been previously published. The results revealed significant genetic alterations like mutation count, TMB, and CNV were observed in patients with high CDS. Furthermore, we observed an association of CDS with immune cell infiltration, microsatellite instability, SNV neoantigens. The immune status of patients with low CDS was more active. In addition, CDS could be reliable to predict therapeutic response in multiple immunotherapy cohorts. In vitro experiments revealed that high DNA damage inducible transcript 4 (DDIT4) expression in LUAD cells mediated protumor effects.

Conclusion

CDS was constructed based on PCDRGs using machine learning. This model could accurately predict patients' prognoses and their responses to therapy. These results provide new promising tools for clinical management and aid in designing personalized treatment strategies for patients with LUAD.

Understanding the Epitranscriptome for Avant-Garde Brain Tumour Diagnostics

RNA modifications are diverse, dynamic, and reversible transcript alterations rapidly gaining attention due to their newly defined RNA regulatory roles in cellular pathways and pathogenic mechanisms. The exciting emerging field of 'epitranscriptomics' is predominantly centred on studying the most abundant mRNA modification, N6-methyladenine (m⁶A). The m⁶A mark, similar to many other RNA modifications, is strictly regulated by so-called 'writer', 'reader', and 'eraser' protein species. The abundance of genes coding for the expression of these regulator proteins and m⁶A levels shows great potential as diagnostic and predictive tools across several cancer fields. This review explores our current understanding of RNA modifications in glioma biology and the potential of epitranscriptomics to develop new diagnostic and predictive classification tools that can stratify these highly complex and heterogeneous brain tumours.

Immune-related gene-based prognostic index for predicting survival and immunotherapy outcomes in colorectal carcinoma

Introduction

Colorectal cancer shows high incidence and mortality rates. Immune checkpoint blockade can be used to treat colorectal carcinoma (CRC); however, it shows limited effectiveness in most patients.

Methods

To identify patients who may benefit from immunotherapy using immune checkpoint inhibitors, we constructed an immune-related gene prognostic index (IRGPI) for predicting the efficacy of immunotherapy in patients with CRC. Transcriptome datasets and clinical information of patients with CRC were used to identify differential immune-related genes between tumor and para-carcinoma tissue. Using weighted correlation network analysis and Cox regression analysis, the IRGPI was constructed, and Kaplan-Meier analysis was used to evaluate its predictive ability. We also analyzed the molecular and immune characteristics between IRGPI high-and low-risk subgroups, performed sensitivity analysis of ICI treatment, and constructed overall survival-related receiver operating characteristic curves to validate the IRGPI. Finally, IRGPI genes and tumor immune cell infiltration in CRC model mice with orthotopic metastases were analyzed to verify the results.

Results

The IRGPI was constructed based on the following 11 hub genes: ADIPOQ, CD36, CCL24, INHBE, UCN, IL1RL2, TRIM58, RBCK1, MC1R, PPARGC1A, and LGALS2. Patients with CRC in the high-risk subgroup showed longer overall survival than those in the low-risk subgroup, which was confirmed by GEO database. Clinicopathological features associated with cancer progression significantly differed between the high- and low-risk subgroups. Furthermore, Kaplan-Meier analysis of immune infiltration showed that the increased infiltration of naïve B cells, macrophages M1, and regulatory T cells and reduced infiltration of resting dendritic cells and mast cells led to a worse overall survival in patients with CRC. The ORC curves revealed that IRGPI predicted patient survival more sensitive than the published tumor immune dysfunction and rejection and tumor inflammatory signature.

Discussion

Thus, the low-risk subgroup is more likely to benefit from ICIs than the high-risk subgroup. CRC model mice showed higher proportions of Tregs, M1 macrophages, M2 macrophages and lower proportions of B cells, memory B cell immune cell infiltration, which is consistent with the IRGPI results. The IRGPI can predict the prognosis of patients with CRC, reflect the CRC immune microenvironment, and distinguish patients who are likely to benefit from ICI therapy.

Identification of a 5-lncRNA-Based Signature for Immune Characteristics and Prognosis of Lung Squamous Cell Carcinoma and Verification of the Function of lncRNA SPATA41

Lung squamous cell carcinoma (LUSC) is one of the most lethal cancers worldwide. Traditional tumor-node-metastasis (TNM) staging system has many insufficiencies in predicting immune characteristics, overall survival (OS), and prognosis of LUSC. LncRNA is currently found involved in tumor development and effectively predicts tumor prognosis. We screened potential tumor-related lncRNAs for immune characteristics and constructed a nomogram combining lncRNA and traditional clinical indicators for prognosis prediction. We obtained the large-scale gene expression profiles of samples from 492 LUSC patients in The Cancer Genome Atlas database. SPATA41, AL034550.2, AP003721.2, AC106786.1, and AC078889.1 were finally screened to construct a 5-lncRNA-based signature. The risk score of the signature divided patients into subgroups of high-risk and low-risk with significant differences in OS. Their area under the curve (AUC) reached more than 0.70 in 1, 3, and 5 years. In addition, compared with the high-risk subgroup, the low-risk subgroup exhibited a remarkably favorable prognosis and TME score, along with a higher immune infiltration score and lower TIDE score. The signature also significantly related to chemotherapy response, especially in cisplatin, vinorelbine, and paclitaxel. Importantly, the nomogram we constructed had good reliability with the assessment of the calibration chart and consistency index (c-index). GO and KEGG enrichment analysis indicated that co-expression mRNAs of the 5 lncRNAs were mainly focused on RNA splicing, DNA replication, and protein serine/threonine kinase activity. Functional assays demonstrated that SPATA41, one of the five OS-related lncRNAs, regulated invasion, migration, proliferation, and programmed death in vitro. In summary, our 5-lncRNA-based signature has a good performance in predicting immune characteristics and prognosis of LUSC patients.

A novel defined m7G regulator signature to investigate the association between molecular characterization and clinical significance in lung adenocarcinoma

Background

About170 chemical modifications to RNAs have been identified, which significantly affect gene expression. Dysregulation of RNA modifications induced by abnormal expression or mutations in RNA modifiers might result in cancer. The most frequent RNA modifications are N6-methyladenosine (m6A), 5-methylcytosine (m5C), and N7-methylguanosine (m7G). Lung cancer is the leading cause of cancer-related deaths globally. The present study aimed to investigate whether the expression of the m7G-related genes is linked to lung cancer cases with lung adenocarcinoma (LUAD), which accounts for about 40% of lung cancer cases.

Methods

A total of 12 m7G-related differentially expressed genes (DEGs) were identified in LUAD patients by The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) Cox regression method was used to build a four-gene risk model. Then, LUAD patients in the TCGA cohort were divided into low- and high-risk groups based on their risk scores for subsequent molecular and clinical research.

Results

Compared to the low-risk group, the high-risk group had a decreased overall survival (OS) (P=0.047). The risk score and stage were independent factors for predicting the OS of LUAD (P=0.0004 and P<0.0001, respectively). Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses based on the two groups showed that the DEGs were metabolically and hormonally related. The high-risk group showed a higher mutation rate and lesser immune cell infiltration, especially in TP53, KRAS, and MET. The expression level of PD-L1 and CTLA4 was high in the high-risk group (P<0.05). The high-risk group is more sensitive to anti-cancer therapy with lower IC50 and higher immunophenoscore (IPS).

Conclusions

In this study, we developed a novel LUAD stratification model based on m7G-related genes that successfully predicts the prognosis of LUAD patients and serves as a guide for clinically personalized treatment.

An Integrated Analysis of Prognostic Signature and Immune Microenvironment in Tongue Squamous Cell Carcinoma

Tongue squamous cell carcinoma (TSCC) is a prevalent cancer of the oral cavity. Survival metrics are usually unsatisfactory, even using combined treatment with surgery, radiation, and chemotherapy. Immune checkpoint inhibitors can prolong survival, especially in patients with recurrent or metastatic disease. However, there are few effective biomarkers to provide prognosis and guide immunotherapy. Here, we utilized weighted gene co-expression network analysis to identify the co-expression module and selected the turquoise module for further scrutiny. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed the innate pathways. The findings indicated that cell junction organization, response to topologically incorrect protein, and regulation of cell adhesion pathways may be essential. Eleven crucial predictive genes (PLXNB1, N4BP3, KDELR2, INTS8, PLAU, PPFIBP2, OAF, LMF1, IL34, ZFP3, and MAP7D3) were used to establish a risk model based on Cox and LASSO analyses of The Cancer Genome Atlas and GSE65858 databases (regarding overall survival). Kaplan–Meier analysis and receiver operating characteristic curve suggested that the risk model had better prognostic effectiveness than other clinical traits. Consensus clustering was used to classify TSCC samples into two groups with significantly different survival rates. ESTIMATE and CIBERSORT were used to display the immune landscape of TSCC and indicate the stromal score; specific types of immune cells, including naïve B cells, plasma cells, CD8 T cells, CD4 memory resting and memory activated T cells, follicular helper T cells, and T regulatory cells, may influence the heterogeneous immune microenvironment in TSCC. To further identify hub genes, we downloaded GEO datasets (GSE41613 and GSE31056) and successfully validated the risk model. Two hub genes (PLAU and PPFIBP2) were strongly associated with CD4+ and CD8+ T cells and programmed cell death protein 1 (PD1) and PD-ligand 1.

The proteasome-dependent degradation of ALKBH5 regulates ECM deposition in PM2.5 exposure-induced pulmonary fibrosis of mice

Long-term inhalation of fine particulate matter (PM_2.5) can cause serious effects on the respiratory system. It might be attributed to the fact that PM_2.5 could directly enter and deposit in lung tissues. We established models of PM_2.5 exposure in vivo and in vitro to explore the adverse effects of ambient PM_2.5 on pulmonary and its potential pathogenic mechanisms. Our results showed that PM_2.5 exposure promoted the deposition of ECM and the increased stiffness of the lungs, and then led to pulmonary fibrosis in time- and dose- dependent manners. Pulmonary function test showed restrictive ventilation function in mice after PM_2.5 exposure. After PM_2.5 exposure, ALKBH5 was recognized by TRIM11 and then degraded through the proteasome pathway. ALKBH5 deficiency (ALKBH5^-/-) aggravated restrictive ventilatory disorder and promoted ECM deposition in lungs of mice induced by PM2.5. And the YAP1 signaling pathway was more activated in ALKBH5^-/- than WT mice after PM_2.5 exposure. In consequence, decreased ALKBH5 protein levels regulated miRNAs and then the miRNAs-targeted YAP1 signaling was activated to promote pulmonary fibrosis induced by PM_2.5.

MiR-4652-5p Targets RND1 to Regulate Cell Adhesion and Promote Lung Squamous Cell Carcinoma Progression

Lung squamous cell carcinoma (LUSC) is one subtype of non-small-cell lung cancer, whose pathogenesis has not been fully understood. Exploring molecular mechanisms of LUSC helps a lot with the development of LUSC novel therapy. Hence, our study aims to investigate novel molecular mechanisms. Differentially expressed miRNAs and mRNAs were acquired from The Cancer Genome Atlas database. A series of assays were applied to test cell functions, including qRT-PCR to analyze RND1 and miR-4652-5p expression, dual-luciferase reporter gene assay to verify the targeting relationship between these two genes, cell counting kit-8 and colony formation assays to evaluate the ability of LUSC cells to proliferate, transwell to examine the migratory and invasive abilities, and western blot to test expression of RND1 and cell adhesion-related proteins. RND1 was lowly expressed while miR-4652-5p was highly expressed in LUSC cells. The correlation between these two genes was significantly negative and miR-4652-5p could downregulate RND1 expression. Additionally, cellular function assays validated that RND1 suppressed LUSC cells to proliferate, migrate, and invade. Besides, this gene might also affect cell adhesion. Furthermore, rescue assay suggested that miR-4652-5p downregulated RND1 expression to promote the progression of LUSC cells. Together, miR-4652-5p targeted RND1 to modulate cell adhesion and the progression of LUSC cells.

The molecular mechanism of METTL3 promoting the malignant progression of lung cancer

Lung cancer remains one of the major causes of cancer-related death globally. Recent studies have shown that aberrant m⁶A levels caused by METTL3 are involved in the malignant progression of various tumors, including lung cancer. The m⁶A modification, the most abundant RNA chemical modification, regulates RNA stabilization, splicing, translation, decay, and nuclear export. The methyltransferase complex plays a key role in the occurrence and development of many tumors by installing m⁶A modification. In this complex, METTL3 is the first identified methyltransferase, which is also the major catalytic enzyme. Recent findings have revealed that METTL3 is remarkably associated with different aspects of lung cancer progression, influencing the prognosis of patients. In this review, we will focus on the underlying mechanism of METT3 in lung cancer and predict the future work and potential clinical application of targeting METTL3 for lung cancer therapy.

As a prognostic biomarker of clear cell renal cell carcinoma RUFY4 predicts immunotherapy responsiveness in a PDL1-related manner

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most lethal malignancies in the urinary system and the existing immunotherapy has not achieved satisfactory outcomes. Therefore, this study aims at establishing a novel gene signature for immune infiltration and clinical outcome (overall survival and immunotherapy responsiveness) in ccRCC patients.

Methods

Based on RNA sequencing data and clinical information in The Cancer Genome Atlas (TCGA) database, we calculated proportions of immune cells in 611 samples using an online tool CIBERSORTx. Multivariate survival analysis was conducted to determine crucial survival-associated immune cells and immune-infiltration-related genes (IIRGs). Next, the clinical specimens and common renal cancer cell lines were applied to confirm IIRGs expression at protein and RNA levels. Finally, functional enrichment analyses and siRNA technology targeted to RUFY4 were implemented to verify its function of predicting immunotherapy response.

Results

Follicular helper T cells (TFHs) and Regulatory T cells (Tregs) were highly infiltrated in the tumor microenvironment (TME) and their relative proportions were independent prognostic factors for patients. Among IIRGs of TFHs and TREGs, RUFY4 was found to be highly activated in tumor microenvironment and its co-expression network was enriched in PDL1/PD1 checkpoint pathway in cancer. Additionally, knockdown of RUFY4 led to the decline of PDL1 and proliferation ability in ccRCC cell lines.

Conclusion

TFHs and Tregs were considered as prognostic biomarkers and RUFY4 was an immunotherapeutic predictor of ccRCC patients in a PDL1-Related manner.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02480-7.

Prognostic implications of N6-methyladenosine RNA regulators in breast cancer

The significance of N⁶-methyladenosine (m6A) RNA modifications in the progression of breast cancer (BC) has been recognised. However, their potential role and mechanism of action in the tumour microenvironment (TME) and immune response has not been demonstrated. Thus, the role of m6A regulators and their downstream target gene components in BC remain to be explored. In this study, we used a series of bioinformatics methods and experiments to conduct exploratory research on the possible role of m6A regulators in BC. First, two regulatory modes of immune activation and inactivation were determined by tumour classification. The TME, immune cell infiltration, and gene set variation analysis results confirmed the reliability of this pattern. The prognostic model of the m6A regulator was established by the least absolute shrinkage and selection operator and univariate and multivariate Cox analyses, with the two regulators most closely related to survival verified by real-time quantitative reverse transcription polymerase chain reaction. Next, the prognostic m6A regulator identified in the model was crossed with the differential copy number of variant genes in invasive BC (IBC), and it was determined that YTHDF1 was a hub regulator. Subsequently, single-cell analysis revealed the expression patterns of m6A regulators in different IBC cell populations and found that YTHDF1 had significantly higher expression in immune-related IBC cells. Therefore, we selected the intersection of the BC differential expression gene set and the differential expression gene set of a cell line with knocked-down YTHDF1 in literature to identify downstream target genes of YTHDF1, in which we found IFI6, EIR, and SPTBN1. A polymerase chain reaction was conducted to verify the results. Finally, we confirmed the role of YTHDF1 as a potential prognostic biomarker through pan-cancer analysis. Furthermore, our findings revealed that YTHDF1 can serve as a new molecular marker for BC immunotherapy.

N6-methyladenosine Regulator-Mediated Immune Genes Identify Breast Cancer Immune Subtypes and Predict Immunotherapy Efficacy

Breast cancer (BRCA) is a heterogeneous malignancy closely related to the tumor microenvironment (TME) cell infiltration. N6-methyladenosine (m6A) modification of mRNA plays a crucial regulator in regulating the immune microenvironment of BRCA. Immunotherapy represents a paradigm shift in BRCA treatment; however, lack of an appropriate approach for treatment evaluation is a significant issue in this field. In this study, we attempted to establish a prognostic signature of BRCA based on m6A-related immune genes and to investigate the potential association between prognosis and immunotherapy. We comprehensively evaluated the m6A modification patterns of BRCA tissues and non-tumor tissues from The Cancer Genome Atlas and the modification patterns with TME cell-infiltrating characteristics. Overall, 1,977 TME-related genes were identified in the literature. Based on LASSO and Cox regression analyses, the m6A-related immune score (m6A-IS) was established to characterize the TME of BRCA and predict prognosis and efficacy associated with immunotherapy. We developed an m6A-IS to effectively predict immune infiltration and the prognosis of patients with BRCA. The prognostic score model represented robust predictive performance in both the training and validation cohorts. The low-m6A-IS group was characterized by enhanced antigen presentation and improved immune checkpoint expression, further indicating sensitivity to immunotherapy. Compared with the patients in the high-score group, the overall survival rate after treatment in the low-score group was significantly higher in the testing and validation cohorts. We constructed an m6A-IS system to examine the ability of the m6A signature to predict the infiltration of immune cells of the TME in BRCA, and the m6A-IS system acted as an independent prognostic biomarker that predicts the response of patients with BRCA in immunotherapy.

Identification of Immune-Related Genes for Establishment of Prognostic Index in Hepatocellular Carcinoma

Background: Immune checkpoint inhibitor (ICI) therapy has been proved to be a promising therapy to many types of solid tumors. However, effective biomarker for estimating the response to ICI therapy and prognosis of hepatocellular carcinoma (HCC) patients remains underexplored. The aim of this study is to build a novel immune-related prognostic index based on transcriptomic profiles. Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to identify immune-related hub genes that are differentially expressed in HCC cohorts. Next, univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis were used to detect hub genes associated to overall survival (OS). To validate the immune-related prognostic index, univariate and multivariate Cox regression analysis were performed. CIBERSORT and ESTIMATE were used to explore the tumor microenvironment and immune infiltration level. Results: The differential expression analysis detected a total of 148 immune-related genes, among which 25 genes were identified to be markedly related to overall survival in HCC patients. LASSO analysis yielded 10 genes used to construct the immune-related gene prognostic index (IRGPI), by which a risk score is computed to estimate low vs. high risk indicating the response to ICI therapy and prognosis. Further analysis confirmed that this immune-related prognostic index is an effective indicator to immune infiltration level, response to ICI treatment and OS. The IRGPI low-risk patients had better overall survival (OS) than IRGPI high-risk patients on two independent cohorts. Moreover, we found that IRGPI high-risk group was correlated with high TP53 mutation rate, immune-suppressing tumor microenvironment, and these patients acquired less benefit from ICI therapy. In contrast, IRGPI-low risk group was associated with low TP53 and PIK3CA mutation rate, high infiltration of naive B cells and T cells, and these patients gained relatively more benefit from ICI therapy.

Copy number variation analysis of m6 A regulators identified METTL3 as a prognostic and immune-related biomarker in bladder cancer

PURPOSE

Growing evidence has demonstrated an indispensable role for N⁶ -methyladenosine (m⁶ A) in human diseases, but the copy number variations (CNVs) of m⁶ A regulatory genes in bladder cancer (BLCA) remains largely unknown.

METHODS

We investigated the CNVs on all known m⁶ A regulatory genes using the Cancer Genome Atlas (TCGA) database. The association between CNV events and clinicopathological as well as molecular characteristics of BLCA patients were explored. Gene set enrichment analysis (GSEA) was implemented to reveal relative cellular processes. Association between m⁶ A regulatory genes and immune infiltrates was analyzed by The Tumor Immune Estimation Resource (TIMER) database.

RESULTS

CNV events of m⁶ A regulatory genes were frequently observed in BLCA. CNVs of METTL3, METTL14, and METTL16 correlated with molecular characteristics of BLCA patients including TP53 mutation. CNVs of METTL3 associated with the overall survival (OS) of BLCA patients. METTL3 was also associated with several cancer-related cellular processes, including mitotic spindle assembly, G2/M checkpoint, and E2F targets signaling pathway. Besides, the CNVs of m⁶ A regulatory genes were correlated with specific kinds of immune infiltrates.

CONCLUSIONS

There are significant correlations between m⁶ A regulatory genes with CNVs and clinicopathological characteristics. METTL3 with CNVs were associated with the immune infiltrates and performed as a prognostic marker in BLCA.

N6-Methyladenosine RNA Modification: An Emerging Immunotherapeutic Approach to Turning Up Cold Tumors

Immunotherapy is a novel clinical approach that has shown clinical efficacy in multiple cancers. However, only a fraction of patients respond well to immunotherapy. Immuno-oncological studies have identified the type of tumors that are sensitive to immunotherapy, the so-called hot tumors, while unresponsive tumors, known as “cold tumors,” have the potential to turn into hot ones. Therefore, the mechanisms underlying cold tumor formation must be elucidated, and efforts should be made to turn cold tumors into hot tumors. N⁶-methyladenosine (m⁶A) RNA modification affects the maturation and function of immune cells by controlling mRNA immunogenicity and innate immune components in the tumor microenvironment (TME), suggesting its predominant role in the development of tumors and its potential use as a target to improve cancer immunotherapy. In this review, we first describe the TME, cold and hot tumors, and m⁶A RNA modification. Then, we focus on the role of m⁶A RNA modification in cold tumor formation and regulation. Finally, we discuss the potential clinical implications and immunotherapeutic approaches of m⁶A RNA modification in cancer patients. In conclusion, m⁶A RNA modification is involved in cold tumor formation by regulating immunity, tumor-cell-intrinsic pathways, soluble inhibitory mediators in the TME, increasing metabolic competition, and affecting the tumor mutational burden. Furthermore, m⁶A RNA modification regulators may potentially be used as diagnostic and prognostic biomarkers for different types of cancer. In addition, targeting m⁶A RNA modification may sensitize cancers to immunotherapy, making it a promising immunotherapeutic approach for turning cold tumors into hot ones.

Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden

Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC).

Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration.

Results: Single nucleotide polymorphism (SNP), C > T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4⁺ memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group.

Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy.

ARID1A, ARID1B, and ARID2 Mutations Serve as Potential Biomarkers for Immune Checkpoint Blockade in Patients With Non-Small Cell Lung Cancer

Worldwide, non-small cell lung cancer (NSCLC) has the highest morbidity and mortality of all malignancies. The lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy, with the rapid development of immune checkpoint inhibitors (ICIs) in recent years. The human switch/sucrose nonfermentable (SWI/SNF) chromatin-remodeling complex has been reported to be recurrently mutated in patients with cancer, and those with SWI/SNF mutations have been reported to be sensitive to ICIs. Six reported cohorts, a total of 3416 patients, were used to analyze the mutation status of ARID1A, ARID1B, ARID2 and SMARCA4 in patients with NSCLC and the effect of mutations on prognosis after ICIs. Finally, a nomogram was established to guide the clinical use of ICIs. The results show that patients with NSCLC who have ARID1A, ARID1B, and ARID2 mutations of the SWI/SNF complex were more likely to benefit from ICI therapy.

Importance of N6-methyladenosine RNA modification in lung cancer (Review)

The N6-methyladenosine (m6A) modification is the most common mRNA modification in eukaryotes and exerts biological functions by affecting RNA metabolism. The m6A modification is installed by m6A methyltransferases, removed by demethylases and recognized by m6A-binding proteins. The interaction between these three elements maintains the dynamic equilibrium of m6A in cells. Accumulating evidence indicates that m6A RNA methylation has a significant impact on RNA metabolism and is involved in the pathogenesis of cancer. Lung cancer is the leading cause of cancer-related deaths worldwide. The treatment options for lung cancer have developed considerably over the past few years; however, the survival rate of patients with lung cancer still remains very low. Although diagnostic methods and targeted therapies have been rapidly developed in recent years, the underlying mechanism and importance of m6A RNA methylation in the pathogenesis of lung cancer remains ambiguous. The current review summarized the biological functions of m6A modification and considers the potential roles of m6A regulators in the occurrence and development of lung cancer.

Gene expression-based immune infiltration analyses of renal cancer and their associations with survival outcome

BACKGROUND

Renal cancer is a common malignant tumor with an increasing incidence rate.

METHODS

In this study, based on the gene expression profiles, we analyzed the compositions of tumor-infiltrating immune cells (TIICs) in renal cancer and paracancerous samples using CIBERSORT. The proportions of 22 TIICs subsets in 122 paired renal carcinoma and paracancerous samples, and 224 Wilms tumor (WT) samples varied between intragroup and intergroup.

RESULTS

After analyzed the difference of TIICs composition between renal cancer and paired paracancerous samples, we found that M0 macrophages and CD8 T cells were significantly elevated, while naive B cells were significantly decreased in renal cancer samples compared with paracancerous samples. Survival analysis showed that high overall TIICs proportion, the low proportion of resting mast cells and the high proportion of activated memory CD4 T cells were associated with poor prognosis of renal cancer patients. In addition, 3 clusters were identified by hierarchical clustering analysis, and they presented a distinct prognosis. Cluster 1 had superior survival outcomes, while cluster 2 had an inferior survival outcome.

CONCLUSIONS

Our study indicated that overall TIICs proportion, certain TIICs subset proportion, including resting mast cells and activated memory CD4 T cells, and distinct cluster patterns were associated with the prognosis of renal cancer, which was significant for the clinical surveillance and treatment of renal cancer.

The prognostic landscape of tumor-infiltrating immune cells in lung squamous cell carcinoma

Background

Exploring novel biomarkers and developing effective therapeutic strategies can improve the prognosis of lung squamous cell carcinoma (LUSC) in the future. The prognostic value of tumor-infiltrating immune cells (TICs) in solid tumors has been extensively studied. However, the landscape of TICs involved in the prognosis of non-small cell lung cancer (NSCLC), especially in LUSC, remains unclear and should be systematically investigated.

Methods

This retrospective study analyzed the immune-related transcriptional profiles of 490 LUSC patients from The Cancer Genome Atlas (TCGA) cohort. Using the CIBERSORT method, TICs were evaluated and examined for their association with overall survival (OS) in LUSC.

Results

Out of the 27 TICs, 14 were correlated with prognosis in LUSC. A novel prognostic model characterized by fewer memory B cells and more central memory CD8 T cells, regulatory T cells (Tregs), and plasmacytoid dendritic cell (pDC) infiltration predicted poor OS in LUSC with high accuracy. The 1-, 3-, and 5-year areas under the curve (AUC) were 0.95, 0.98, and 0.96, respectively, in the training cohort. This finding was further validated in the validation cohort, where the 1-, 3-, and 5-year AUCs were 0.95, 0.98, and 0.95, respectively.

Conclusions

These findings may provide more effective prognostic biomarkers and potential therapeutic targets for the immunotherapy of LUSC.

The evolving landscape of N6-methyladenosine modification in the tumor microenvironment

The tumor microenvironment (TME), controlled by intrinsic mechanisms of carcinogenesis and epigenetic modifications, has, in recent years, become a heavily researched topic. The TME can be described in terms of hypoxia, metabolic dysregulation, immune escape, and chronic inflammation. RNA methylation, an epigenetic modification, has recently been found to have a pivotal role in shaping the TME. The N⁶-methylation of adenosine (m⁶A) modification is the most common type of RNA methylation that occurs in the N⁶-position of adenosine, which is the primary internal modification of eukaryotic mRNA. Compelling evidence has demonstrated that m⁶A regulates transcriptional and protein expression through splicing, translation, degradation, and export, thereby mediating the biological processes of cancer cells and/or stromal cells and characterizing the TME. The TME also has a crucial role in the complicated regulatory network of m⁶A modifications and, subsequently, influences tumor initiation, progression, and therapy responses. In this review, we describe the features of the TME and how the m⁶A modification modulates and interacts with it. We also focus on various factors and pathways involved in m⁶A methylation. Finally, we discuss potential therapeutic strategies and prognostic biomarkers with respect to the TME and m⁶A modification.

Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.

Construction of a prognostic immune signature for lower grade glioma that can be recognized by MRI radiomics features to predict survival in LGG patients

BACKGROUND

This study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints.

METHOD

LGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature.

RESULTS

Immunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes.

CONCLUSIONS

The IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.

A dual immune signature of CD8+ T cells and MMP9 improves the survival of patients with hepatocellular carcinoma

The 5-year survival of hepatocellular carcinoma (HCC) is difficult due to the high recurrence rate and metastasis. Tumor infiltrating immune cells (TICs) and immune-related genes (IRGs) bring hope to improve survival and treatment of HCC patients. However, there are problems in predicting immune signatures and identifying novel therapeutic targets. In the study, the CIBERSORT algorithm was used to evaluate 22 immune cell infiltration patterns in gene expression omnibus (GEO) and the cancer genome atlas (TCGA) data. Eight immune cells were found to have significant infiltration differences between the tumor and normal groups. The CD8+ T cells immune signature was constructed by least absolute shrinkage and selection operator (LASSO) algorithm. The high infiltration level of CD8+ T cells could significantly improve survival of patients. The weighted gene co-expression network analysis (WGCNA) algorithm identified MMP9 was closely related to the overall survival of HCC patients. K-M survival and tROC analysis confirmed that MMP9 had an excellent prognostic prediction. Cox regression showed that a dual immune signature of CD8+ T cells and MMP9 was independent survival factor in HCC. Therefore, a dual prognostic immune signature could improve the survival of patient and may provide a new strategy for the immunotherapy of HCC.

Roles of RNA Methylation on Tumor Immunity and Clinical Implications

RNA methylation is a kind of RNA modification that exists widely in eukaryotes and prokaryotes. RNA methylation occurs not only in mRNA but also in ncRNA. According to the different sites of methylation, RNA methylation includes m⁶A, m⁵C, m⁷G, and 2-O-methylation modifications. Modifications affect the splicing, nucleation, stability and immunogenicity of RNA. RNA methylation is involved in many physiological and pathological processes. In the immune system, especially for tumor immunity, RNA methylation affects the maturation and response function of immune cells. Through the influence of RNA immunogenicity and innate immune components, modifications regulate the innate immunity of the body. Some recent studies verified that RNA methylation can regulate tumor immunity, which also provides a new idea for the future of treating immunological diseases and tumor immunotherapy.

Gene expression-based immune infiltration analyses of liver cancer and their associations with survival outcomes

INTRODUCTION

Liver cancer is one of the most lethal malignancies, presenting inferior survival outcomes once diagnosed. Current therapeutic approaches mainly target the tumor cells or vasculature, but rarely take the immune factors into consideration.

METHODS

In our study, the compositions of tumor-infiltrating immune cells (TIICs) in liver cancer and paracancer samples were analyzed based on the gene expression profiles by CIBERSORT. After calculating the proportions of 22 TIICs subtypes in 51 paired cancer and paracancer samples, we found their proportions varied between intragroup and intergroup. Compared with the paracancer tissues, the relative proportions of macrophages M0 and resting mast cells in liver cancer samples were significantly elevated, while that of M2 macrophages were reduced.

RESULTS

Univariate Cox regression analysis with the 22 TIICs subtypes as continuous variables showed increased B cells memory and resting NK cells were significantly associated with poor survival outcome. Besides, hierarchical clustering analysis based on the proportions of 22 TIICs subtypes identified 3 clusters, which exhibited distinct prognosis. Among them, cluster 1 had superior survival outcomes, while cluster 3 had inferior survival outcomes.

CONCLUSIONS

Collectively, our research suggested certain TIICs subpopulations proportions, as well as cluster patterns were associated with the prognosis of liver cancer, which provided potential therapeutic targets for liver cancer.

Four hub genes regulate tumor infiltration by immune cells, antitumor immunity in the tumor microenvironment, and survival outcomes in lung squamous cell carcinoma patients

In this study, we performed bioinformatics analyses to identify hub genes that regulate tumor infiltration by immune cells and antitumor immunity in the lung squamous cell carcinoma (LUSC). We identified 1738 robust and stable differentially expressed genes (DEGs) in the LUSC tissues based on robust rank aggregation (RRA) analysis of RNA-sequencing data from 5 GEO-LUSC datasets. We then classified TCGA-LUSC patients based on ssGSEA and ESTIMATE analyses of LUSC tissues into high, medium and low immunity subgroups showing significant differences in tumor purity. Weighted gene co-expression network analysis of the robust DEGs revealed five immunity-related modules, including the brown module with 762 DEGs and 30 hub genes showing the highest correlation with the immunity-related LUSC patient subgroups and their clinicopathological characteristics. We selected four hub genes, LAPTM5, C1QC, CSF1R and SLCO2B1, for validation of the immunity status and prognosis of LUSC patients. High expression of these four genes correlated with increased infiltration of immune cell types, upregulation of the immunosuppressive TOX pathway genes, CD8⁺ T cell exhaustion, and shorter overall survival of LUSC patients. These findings demonstrate that four hub genes regulate tumor infiltration of immune cells, anti-tumor immunity, and survival outcomes in LUSC patients.

Glycolysis-Based Genes Are Potential Biomarkers in Thyroid Cancer

While increased glycolysis has been identified as a cancer marker and attracted much attention in thyroid cancer (THCA), the prognostic role of it remains to be further elucidated. Here we aimed to determine a specific glycolysis-associated risk model to predict THCA patients' survival. We also explored the interaction between this signature and tumor immune microenvironment and performed drug screening to identify specific drugs targeting the glycolysis-associated signature. Six genes (CHST6, POM121C, PPFIA4, STC1, TGFBI, and FBP2) comprised the specific model, which was an independent prognostic indicator in THCA patients determined by univariate, LASSO and multivariate Cox regression analyses. The receiver operating characteristic (ROC) curve analysis confirmed the excellent clinical performance of the prognostic signature. According to the specific gene signature, patients were categorized into high- and low-risk subgroups. The high-risk group was characterized by decreased immune score and elevated tumor purity, as well as worser survival prognosis compared to the low-risk group. We also validated the expression of these genes in clinical samples and in-vitro experiments. Lastly, we identified potential drugs targeting the glycolysis-associated signature. The derived glycolysis-related signature is an independent prognostic biomarker for THCA patients and might be used as an efficacy of biomarker for drug-sensitivity prediction.

A signature of 17 immune-related gene pairs predicts prognosis and immune status in HNSCC patients

Head and neck squamous cell carcinoma (HNSCC) is an invasive malignancy with high worldwide mortality. Growing evidence has indicated a pivotal correlation between HNSCC prognosis and immune signature. This study investigated an immune-related gene pairs (IRGPs) signature to predict the prognostic value of HNSCC patients. We constructed IRGPs via integrating multiple IRG expression data sets. Moreover, we established the predictive model base on the IRGPs for HNSCC, and utilized multidimensional bioinformatics methods to validate the robustness of prognostic value of the IRGPs signature. In addition, we explored the relationship between the IRGPs model and immune status. Seventeen IRGPs signature was built as the predictive model which predicted prognosis independently and reliably for HNSCC. Compared to the high-risk group, the low-risk group demonstrated a distinctly favorable prognosis including overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS). The low-risk group showed higher-immune score and lower-tumor purity than the high-risk group. In addition, the low-risk group exhibited higher expression of Programmed cell death 1 ligand 1 (PD-L1) and Microsatellite instability (MSI) score, and lower expression of Tumor Immune Dysfunction and Exclusion (TIDE), which indicated the low-risk group was much more sensitive to immunotherapy. Lastly, the IRGs signature has achieved a higher accuracy than clinical properties for estimation of survival. The IRGPs model is an independent biomarker for estimating the prognosis, and could be also used to predict immunotherapeutic response in HNSCC patients. These findings may provide new ideas for novel biomarkers and may be helpful to formulate personalized immunotherapy strategy.

DNA methylation-based lung adenocarcinoma subtypes can predict prognosis, recurrence, and immunotherapeutic implications

The marked heterogeneity of lung adenocarcinoma (LUAD) makes its diagnosis and treatment difficult. In addition, the aberrant DNA methylation profile contributes to tumor heterogeneity and alters the immune response. We used DNA methylation array data from publicly available databases to establish a predictive model for LUAD prognosis. Thirty-three methylation sites were identified as specific prognostic biomarkers, independent of patients' clinical characteristics. These methylation profiles were used to identify potential drug candidates and study the immune microenvironment of LUAD and response to immunotherapy. When compared with the high-risk group, the low-risk group had a lower recurrence rate and favorable prognosis. The tumor microenvironment differed between the two groups as reflected by the higher number of resting dendritic cells and a lower number of monocytes and resting mast cells in the low-risk group. Moreover, low-risk patients reported higher immune and stromal scores, lower tumor purity, and higher expression of HLA genes. Low-risk patients responded well to immunotherapy due to higher expression of immune checkpoint molecules and lower stemness index. Thus, our model predicted a favorable prognosis and increased overall survival for patients in the low-risk methylation group. Further, this model could provide potential drug targets to develop effective immunotherapies for LUAD.

[Research Advances of m⁶A RNA Methylation in Non-small Cell Lung Cancer]

m⁶A修饰是真核生物mRNA中最丰富的修饰之一，该过程受m⁶A甲基转移酶和去甲基化酶的共同调控。m⁶A修饰后的RNA能够被m⁶A识别蛋白特异性识别并结合，进而介导RNA的剪接、成熟、出核、降解和翻译等。目前国内外对于m⁶A修饰及其相关蛋白如何参与非小细胞肺癌发生发展的研究，主要集中于细胞恶性增殖、迁移、侵袭、转移和耐药等方面。m⁶A修饰相关蛋白在肺癌组织标本和血液循环肿瘤细胞（circulating tumor cell, CTC）中表达异常，有望成为肺癌诊断和预后判断的潜在分子标志物。本文围绕m⁶A修饰相关蛋白的组成、作用方式、在非小细胞肺癌恶性进展中的生物学功能，以及针对m⁶A修饰的靶向治疗等方面的研究进展进行综述，旨在为非小细胞肺癌的早期临床诊断和靶向药物的开发提供新思路。

A signature of immune-related gene pairs predicts oncologic outcomes and response to immunotherapy in lung adenocarcinoma

In this study, we established the predictive model for lung adenocarcinoma (LUAD) depending on immune-related gene pairs (IRGPs) signature, which could not consider the technical bias of different platforms. Furthermore, we explored the predictive model with regard to the immune microenvironment and response to immunotherapy and identified specific drugs targeting the IRGPs model. Twenty-three IRGPs were identified and comprised the predictive model. When compared with the high-risk group, the low-risk group displayed a distinctly favorable prognosis and was characterized by increased immune score and decreased tumor purity. In addition, the low-risk group exhibited higher expression of immune checkpoint molecules, lower tumor stemness index, and was much more sensitive to immunotherapy. Lastly, candidate drugs that aimed at LUAD subtype differentiation were identified. The derived IRGPs model is an adverse independent biomarker for estimating oncologic outcomes in LUAD patients, and may be helpful to formulate personalized immunotherapy strategy.

Comprehensive analysis of cancer hallmarks in cutaneous melanoma and identification of a novel unfolded protein response as a prognostic signature

Molecular pathways regulating the initiation and development of melanoma are potential therapeutic targets for this aggressive skin cancer. Therefore, transcriptome profiles of cutaneous melanoma were obtained from a public database and used to systematically evaluate cancer hallmark pathways enriched in melanoma. Finally, the unfolded protein response pathway was screened out, and the unfolded protein response-related genes were used to develop a robust biomarker that can predict the prognosis of melanoma, especially for younger, metastatic and high Clark level patients. This biomarker was further validated in two other independent datasets. In addition, melanoma patients were divided into high- and low-risk subgroups by applying a risk score system. The high-risk group exhibited higher immune infiltration and higher expression of N6-methyladenosine RNA methylation regulators, and had significantly shorter survival times than the low-risk subgroup. Gene Set Enrichment Analysis revealed that, among the enriched genes, gene sets involved in immune response and the extracellular matrix receptor interaction were significantly activated in the high-risk group. Our findings thus provide a new clinical application for prognostic prediction as well as potential targets for treatment of melanoma.

The m6A epitranscriptome opens a new charter in immune system logic

N6-methyladenosine (m6A), the most prevalent RNA internal modification, is present in most eukaryotic species and prokaryotes. Studies have highlighted an intricate network architecture by which m6A epitranscriptome impacts on immune response and function. However, it was only until recently that the mechanisms underlying the involvement of m6A modification in immune system were uncovered. Here, we systematically review the m6A involvement in the regulation of innate and adaptive immune cells. Further, the interplay between m6A modification and anti-inflammatory, anti-viral and anti-tumour immunity is also comprehensively summarized. Finally, we focus on the future prospects of m6A modification in immune modulation. A better understanding of the crosstalk between m6A modification and immune system is of great significance to reveal new pathogenic pathways and to develop promising therapeutic targets of diseases.

N6-methyladenosine RNA modification in cancer therapeutic resistance: Current status and perspectives

Several strategies, including chemotherapy and radiotherapy, have improved therapeutic outcomes among cancer patients in clinical practice. However, due to their heterogeneity, cancer cells frequently display primary or acquired therapeutic resistance, thereby resulting in treatment failure. The mechanisms underlying cancer therapeutic resistance are complex and varied. Among them, N6-methyladenosine (m6A) RNA modification has gained increasing attention as a potential determinant of therapy resistance within various cancers. In this review, we primarily describe evidence for the effect of the m6A epitranscriptome on RNA homeostasis modulation, which has been shown to alter multiple cellular pathways in cancer research and treatment. Additionally, we discuss the profiles and biological implications of m6A RNA methylation, which is undergoing intensive investigation for its effect on the control of therapeutic resistance.

Multiomics profile and prognostic gene signature of m6A regulators in uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is the most common type of gynecologic malignancy worldwide. Despite advances in the treatments of UCEC, its incidence and mortality rates are still increasing. N6-methyladenosine (m6A) is the most common form of RNA modification and has attracted increasing interest in cancer pathogenesis and progression. Thus, we aimed to identify the landscape of m6A regulators and build a prognostic gene signature in UCEC. In this study, we first analyzed copy number variations (CNVs), single nucleotide variations (SNVs) and gene expression profiles as well as matched clinical information of UCEC patients from The Cancer Genome Atlas (TCGA) database. Next, we determined that CNVs in m6A regulatory genes had a significant negative impact on patient survival. The mRNA expression levels of a total of 16 m6A regulators were significantly correlated with different CNV patterns. Using univariate Cox regression analysis, IGF2BP1, KIAA1429, IGF2BP3, YTHDF3, and IGF2BP2 were found to be closely associated with UCEC patient survival outcomes. Based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression models, we built a 3-gene (IGF2BP3, KIAA1429 and IGF2BP1) signature of m6A regulators with prognostic value in UCEC that could effectively predict patient prognosis (log-rank test p-value < 0.0001). In addition, risk scores were significantly different between patients stratified by tumor stage, SNV, and CNV. Multivariate Cox regression analysis suggested that risk score might be an independent prognostic indicator for the overall survival of patients with UCEC (p-value < 0.05). Gene enrichment analysis indicated that high IGF2BP1 gene expression is associated with cytoplasmic stress granules. KIAA1429 gene expression is associated with cellular nucleic acid metabolism. The expression of the IGF2BP3 gene is associated with RNA binding processes. In conclusion, we determined that genetic alterations in m6A regulatory genes could be effective and reliable biomarkers for UCEC prognosis prediction.

Immune signature of tumor-infiltrating immune cells predicts the prognosis and therapeutic effects in squamous cell carcinoma

Tumor-infiltrating immune cells (TICs) are involved in tumor progression and determine the prognosis. We investigated how TICs affect the prognosis and therapeutic effects of squamous cell carcinoma (SCC), which share common histological features and certain risk factors. The SCC data from The Cancer Genome Atlas (TCGA) and Gene expression Omnibus (GEO) databases were downloaded to evaluate the composition of TICs with the CIBERSORT algorithm. LASSO and Cox multivariate regression analyses were used to build a prognostic risk model. Chemotherapeutic and immunotherapeutic responses were compared between patients with SCC. A Gene set variation analysis (GSVA) was also performed to elucidate the mechanism. Naïve B cells and resting mast cells were selected to construct the prognostic model. According to these two immune cell subtypes, patients with SCC were divided into low- and high-risk groups. The low-risk group with high proportions of naïve B cells and resting mast cells had a better overall survival rate than the high-risk group and might benefit from immunotherapy and chemotherapy due to differences in the immune microenvironment. Activation of the Wnt signaling pathway was observed in the high-risk group. Based on the findings from the present study, the immune signature provides prognostic determinants of SCC and may be a biomarker to guide chemotherapy and immunotherapy. Wnt inhibitors may be attractive candidates for combination treatment in high-risk patients with SCC.

Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model

Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients.

The emerging roles of N6-methyladenosine RNA methylation in human cancers

N6-methyladenosine (m⁶A) is the most abundant form of mRNA modification in eukaryotes. It affects various aspects of RNA metabolism, including nuclear export, translation, decay and alternative splicing. In addition, m⁶A also participates in a great number of human physiological processes, ranging from spermatogenesis modulation, response to heat shock, the control of T cell homeostasis to stem cell proliferation and differentiation. The dynamic equilibrium of m⁶A level is regulated by m⁶A methyltransferases (“writers”), m⁶A demethylases (“erasers”) as well as m⁶A-binding proteins (“readers”). Once the balance is broken, numerous diseases will knock on the door. Recently, increasing studies reveal that m⁶A methylation exerts a profound impact on tumorigenesis and tumor progression. Therefore, in this review, we summarize the functions of m⁶A modification and its emerging roles in human cancers, and discuss the potential of m⁶A regulators as biomarkers or therapeutic targets.

Analysis of Lung Adenocarcinoma Subtypes Based on Immune Signatures Identifies Clinical Implications for Cancer Therapy

Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of this cancer type heterogeneity. LUAD subtypes were identified on the basis of the immunogenomic profiling of 29 immune signatures. We named three LUAD subtypes: Immunity High, Immunity Medium, and Immunity Low. The Immunity High subtype was characterized by immune activation, e.g., increased immune scores, elevated stromal scores and the highest infiltration of CD8⁺ T cells, and decreased tumor purities. Activated expressions of human leukocyte antigen (HLA) genes, immune checkpoint molecules, and T helper 1 (Th1)/interferon-gamma (IFNγ) gene signature were also observed in the Immunity High subtype. N ⁶-methyladenosine (m⁶A) RNA methylation, associated with cancer initiation and progression, was reduced in the Immunity High subtype. Functional and signaling pathway enrichment analysis further showed that differentially expressed genes between the Immunity High subtype and the other subtypes mainly participated in immune response and some cancer-associated pathways. In addition, the Immunity High subtype exhibited more sensitivity to immunotherapy and chemotherapy. Finally, candidate compounds that aimed at LUAD subtype differentiation were identified. Comprehensively characterizing the LUAD subtypes based on immune signatures may help to provide potential strategies for LUAD treatment.

Fibroblast‑derived exosomal microRNA‑369 potentiates migration and invasion of lung squamous cell carcinoma cells via NF1‑mediated MAPK signaling pathway

Cancer-associated fibroblasts (CAFs) exhibit tumor-stimulating properties and are associated with poor survival in several types of cancer, making them potential therapeutic targets. The present study aimed to determine whether CAFs were associated with cell migration and invasion in lung squamous cell carcinoma (LUSC), as well as their association with microRNA-369 (miR-369) in these processes. Firstly, the changes of the malignant biological behavior were observed by treating the LUSC cells with the CAFs-derived extracellular vesicles (CAFs-EVs). Subsequently, the differentially expressed miRNAs in the cells treated with CAFs-EVs were analyzed by microarray analysis. Following inhibition of miR-369 expression in CAFs-EVs, LUSC cells were co-cultured, and the malignant biological behavior of the cells was re-examined. Then, through bioinformatics analysis and verification, the mRNA targets of miR-369 and the corresponding downstream signaling pathway were screened out. Finally, the effects of CAFs-EVs on the growth and metastasis of LUSC were demonstrated by in vivo tumor formation and metastasis experiments. It was identified that miR-369 was expressed at a relatively high level in the CAFs-EVs. Neurofibromin-1 (NF1) was hypothesized as a direct target of miR-369 in LUSC. Also, the overexpression of miR-369 activated the mitogen-activated protein kinase signaling pathway by interacting with NF1, consequently potentiating LUSC cell growth. The present study provided novel insights into the action of miR-369 in CAFs-EVs in controlling LUSC cell migration, invasion and tumorigenesis, and identified miR-369 in CAFs-EVs as an important prognostic marker and therapeutic target.

A TP53-associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

The tumor-suppressor gene tumor protein p53 (TP53) is one of the most commonly mutated genes in human lung cancer, and TP53 mutations are associated with a worsened prognosis and causes resistance to cancer therapy. RNA sequencing and TP53 mutation data were downloaded to determine specific TP53-associated signature based on differentially expressed genes between patients with lung squamous cell carcinoma (LUSC) with wild type (TP53 ^WT) and mutated (TP53^MUT) TP53. We investigated the predictive value of this signature on the immune microenvironment, tumor mutational burden (TMB), and likelihood of response to immunotherapy and chemotherapy. In total, 1,556 differentially expressed genes were identified based on TP53 mutation status. Three genes (KLK6, MUC22 and CSN1S1) identified by univariate and multivariate Cox regression analyses, comprised the prognostic signature which was an independent and specific prognostic marker of overall survival in patients with LUSC. A nomogram was also established to validate this signature for clinical use. Moreover, the high-risk group was characterized by increased infiltration of monocytes and macrophages M1, and decreased T cells CD8 and T cells follicular helper. High-risk group exhibited a higher TMB, and was much more sensitive to immunotherapy and chemotherapy. KLK6 and CSN1S1 expression and the prognostic prediction values were further validated in clinical samples. The derived TP53-associated signature is a specific and independent prognostic biomarker for LUSC patients, and could provide potential prognostic biomarker or therapeutic targets for the development of novel immunotherapies and chemotherapies.