5mC regulator-mediated molecular subtypes depict the hallmarks of the tumor microenvironment and guide precision medicine in bladder cancer

SLC7A5 correlated with malignancies and immunotherapy response in bladder cancer

BACKGROUND

Metabolic reprogramming contributes to bladder cancer development. This study aimed to understand the role of SLC7A5 in bladder cancer.

METHODS

We systematically analyzed the correlation between SLC7A5 and bladder cancer through various approaches, including bioinformatics, western blotting, cell cycle analysis, cell proliferation assays, and invasion experiments. We also investigated the immunological features within the tumor microenvironment (TME), encompassing cancer immune cycles, immune modulators, immune checkpoints, tumor-infiltrating immune cells (TIIC), T cell inflammation scores, and treatment responses. Additionally, for a comprehensive assessment of the expression patterns and immunological roles of SLC7A5, pan-cancer analysis was performed using cancer genomics datasets.

RESULTS

SLC7A5 was associated with adverse prognosis in bladder cancer patients, activating the Wnt pathway and promoting bladder cancer cell cycle progression, proliferation, migration, and invasion. Based on the evidence that SLC7A5 positively correlated with immunomodulators, TIIC, the cancer immune cycle, immune checkpoint and T cell inflammation scores, we also found that SLC7A5 was associated with the inflammatory tumor immune microenvironment. EGFR-targeted therapy, cancer immunotherapy, and radiation therapy were effective for patients with high SLC7A5 expression in bladder cancer. Low SLC7A5 patients were, however, sensitive to targeted therapies and anti-angiogenic therapy, such as blocking β-catenin network, PPAR-γ and FGFR3 signaling. Anti-SLC7A5 combined with cancer immunotherapy may have greater effectiveness than either therapy alone. Furthermore, we observed specific overexpression of SLC7A5 in TME of various cancers.

CONCLUSION

SLC7A5 can predict therapeutic response to immunotherapy, radiotherapy and chemotherapy in bladder cancer patients. Targeting SLC7A5 in combination with immunotherapy may be a potentially appropriate treatment option.

Identification of m6A/m5C-related lncRNA signature for prediction of prognosis and immunotherapy efficacy in esophageal squamous cell carcinoma

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have garnered significant attention in the field of epigenetic research due to their close association with human cancers. This study we focus on elucidating the expression patterns of m6A/m5C-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) and assessing their prognostic significance and therapeutic potential. Transcriptomic profiles of ESCC were derived from public resources. m6A/m5C-related lncRNAs were obtained from TCGA using Spearman's correlations analysis. The m6A/m5C-lncRNAs prognostic signature was selected to construct a RiskScore model for survival prediction, and their correlation with the immune microenvironment and immunotherapy response was analyzed. A total of 606 m6A/m5C-lncRNAs were screened, and ESCC cases in the TCGA cohort were stratified into three clusters, which showed significantly distinct in various clinical features and immune landscapes. A RiskScore model comprising ten m6A/m5C-lncRNAs prognostic signature were constructed and displayed good independent prediction ability in validation datasets. Patients in the low-RiskScore group had a better prognosis, a higher abundance of immune cells (CD4 + T cell, CD4 + naive T cell, class-switched memory B cell, and Treg), and enhanced expression of most immune checkpoint genes. Importantly, patients with low-RiskScore were more cline benefit from immune checkpoint inhibitor treatment (P < 0.05). Our findings underscore the potential of RiskScore system comprising ten m6A/m5C-related lncRNAs as effective biomarkers for predicting survival outcomes, characterizing the immune landscape, and assessing response to immunotherapy in ESCC.

ALYREF m5C RNA methylation reader predicts bladder cancer prognosis by regulating the tumor immune microenvironment

BACKGROUND

5-Methylcytidine (m5C) methylation is a recently emerging epigenetic modification that is closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the clinicopathological characteristics and prognostic value of m5C regulators in bladder cancer (BLCA), and their correlation with the tumor immune microenvironment.

METHODS

Thirteen m5C RNA methylation regulators were analyzed using RNA-sequencing and corresponding clinical information obtained from the TCGA database. The Cluster Profiler package was used to analyze the gene ontology function of potential targets and enriched the Kyoto Encyclopedia of Genes and Genomes pathway. Kaplan-Meier survival analysis was used to compare survival differences using the log-rank test and univariate Cox proportional hazards regression. The correlation between signature prognostic m5C regulators and various immune cells was analyzed. Univariate and multivariate Cox regression analyses identified independence of the ALYREF gene signature.

RESULTS

Nine out of the 13 m5C RNA methylation regulators were differentially expressed in BLCA and normal samples and were co-expressed. These 9 regulators were associated with clinicopathological tumor characteristics, particularly high or low tumor risk, pT or pTNM stage, and migration. Consensus clustering analysis divides the BLCA samples into 4 clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment annotation and gene ontology function analysis identified 273 upregulated and 594 downregulated genes in BLCA. Notably, only ALYREF was significantly correlated with OS (P < .05). ALYREF exhibited significant infiltration levels in macrophage cells. Therefore, we constructed a nomogram for ALYREF as an independent prognostic factor. Additionally, we observed that both the mRNA and protein levels of ALYREF were upregulated, and immunofluorescence showed that ALYREF was mainly distributed in nuclear speckles. ALYREF overexpression was significantly associated with poor OS.

CONCLUSION

Our findings demonstrated the potential of ALYREF to predict clinical prognostic risks in BLCA patients and regulate the tumor immune microenvironment. As such, ALYREF may serve as a novel prognostic indicator in BLCA patients.

Novel MSH2 and TSC2 variants in a Chinese family with Lynch syndrome and their synergistic impact in urothelial carcinoma

Lynch syndrome, an autosomal dominant hereditary disease arising from mutations in mismatch repair genes, is linked to the development of multiple tumor types, notably colorectal cancer, endometrial carcinoma and upper urinary tract urothelial carcinoma. In this study, we present the case of a young patient diagnosed with upper urinary tract urothelial carcinoma, notable for a familial history of diverse malignancies. By employing genetic analysis, we verified the presence of Lynch syndrome within the family and detected novel variants, MSH2 p.A604D and TSC2 p.C738Y, utilizing NGS technology. Subsequently, we conducted validation experiments to assess the pathogenicity of the MSH2 and TSC2 variants. We illustrated that the MSH2 variant can result in diminished MSH2 expression, compromised mismatch repair function, and induce resistance to cisplatin in urothelial carcinoma. Furthermore, we substantiated the promotional impact of the identified TSC2 variant on urothelial carcinoma, encompassing proliferation, invasion, and migration. Significantly, we found that the MSH2 p.A604D variant and TSC2 p.C738Y variant synergistically enhance the promotion of urothelial carcinoma.

Identification of FLRT2 as a key prognostic gene through a comprehensive analysis of TMB and IRGPs in BLCA patients

Introduction

The tumor immune environment and immune-related genes are instrumental in the development, progression, and prognosis of bladder cancer (BLCA). This study sought to pinpoint key immune-related genes influencing BLCA prognosis and decipher their mechanisms of action.

Methods and results

We analyzed differentially expressed genes (DEGs) between high- and low- tumor mutational burden (TMB) groups. Subsequently, we constructed a reliable prognostic model based on immune-related gene pairs (IRGPs) and analyzed DEGs between high- and low-risk groups. A total of 22 shared DEGs were identified across differential TMB and IRGPs-derived risk groups in BLCA patients. Through univariate Cox and multivariate Cox analyses, we highlighted five genes - FLRT2, NTRK2, CYTL1, ZNF683, PRSS41 - significantly correlated with BLCA patient prognosis. Notably, the FLRT2 gene emerged as an independent prognostic factor for BLCA, impacting patient prognosis via modulation of macrophage infiltration in immune microenvironment. Further investigation spotlighted methylation sites - cg25120290, cg02305242, and cg01832662 - as key regulators of FLRT2 expression.

Discussion

These findings identified pivotal prognostic genes in BLCA and illuminated the intricate mechanisms dictating patient prognosis. This study not only presents a novel prognostic marker but also carves out potential avenues for immunotherapy and targeted therapeutic strategies in BLCA. By demystifying the profound impact of immune-related genes and the tumor immune environment, this study augments the comprehension and prognostic management of bladder cancer.

Cuproptosis-associated ncRNAs predict breast cancer subtypes

BACKGROUND

Cuproptosis is a novel copper-dependent mode of cell death that has recently been discovered. The relationship between Cuproptosis-related ncRNAs and breast cancer subtypes, however, remains to be studied.

METHODS

The aim of this study was to construct a breast cancer subtype prediction model associated with Cuproptosis. This model could be used to determine the subtype of breast cancer patients. To achieve this aim, 21 Cuproptosis-related genes were obtained from published articles and correlation analysis was performed with ncRNAs differentially expressed in breast cancer. Random forest algorithms were subsequently utilized to select important ncRNAs and build breast cancer subtype prediction models.

RESULTS

A total of 94 ncRNAs significantly associated with Cuproptosis were obtained and the top five essential features were chosen to build a predictive model. These five biomarkers were differentially expressed in the five breast cancer subtypes and were closely associated with immune infiltration, RNA modification, and angiogenesis.

CONCLUSION

The random forest model constructed based on Cuproptosis-related ncRNAs was able to accurately predict breast cancer subtypes, providing a new direction for the study of clinical therapeutic targets.

TFRC, associated with hypoxia and immune, is a prognostic factor and potential therapeutic target for bladder cancer

BACKGROUND

Bladder cancer is a common malignancy of the urinary system, and the survival rate and recurrence rate of patients with muscular aggressive (MIBC) bladder cancer are not ideal. Hypoxia is a pathological process in which cells acquire special characteristics to adapt to anoxic environment, which can directly affect the proliferation, invasion and immune response of bladder cancer cells. Understanding the exact effects of hypoxia and immune-related genes in BLCA is helpful for early assessment of the prognosis of BLCA. However, the prognostic model of BLCA based on hypoxia and immune-related genes has not been reported.

PURPOSE

Hypoxia and immune cell have important role in the prognosis of bladder cancer (BLCA). The aim of this study was to investigate whether hypoxia and immune related genes could be a novel tools to predict the overall survival and immunotherapy of BLCA patients.

METHODS

First, we downloaded transcriptomic data and clinical information of BLCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A combined hypoxia and immune signature was then constructed on the basis of the training cohort via least absolute shrinkage and selection operator (LASSO) analysis and validated in test cohort. Afterwards, Kaplan-Meier curves, univariate and multivariate Cox and subgroup analysis were employed to assess the accuracy of our signature. Immune cell infiltration, checkpoint and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were used to investigate the immune environment and immunotherapy of BLCA patients. Furthermore, we confirmed the role of TFRC in bladder cancer cell lines T24 and UMUC-3 through cell experiments.

RESULTS

A combined hypoxia and immune signature containing 8 genes were successfully established. High-risk group in both training and test cohorts had significantly poorer OS than low-risk group. Univariate and multivariate Cox analysis indicated our signature could be regarded as an independent prognostic factor. Different checkpoint was differently expressed between two groups, including CTLA4, HAVCR2, LAG3, PD-L1 and PDCD1. TIDE analysis indicated high-risk patients had poor response to immunotherapy and easier to have immune escape. The drug sensitivity analysis showed that high-risk group patients were more potentially sensitive to many drugs. Meanwhile, TFRC could inhibit the proliferation and invasion ability of T24 and UMUC-3 cells.

CONCLUSION

A combined hypoxia and immune-related gene could be a novel predictive model for OS and immunotherapy estimation of BLCA patients and TFRC could be used as a potential therapeutic target in the future.

Circulating cell-free DNA-based methylation pattern in plasma for early diagnosis of esophagus cancer

With the increased awareness of early tumor detection, the importance of detecting and diagnosing esophageal cancer in its early stages has been underscored. Studies have consistently demonstrated the crucial role of methylation levels in circulating cell-free DNA (cfDNA) in identifying and diagnosing early-stage cancer. cfDNA methylation pertains to the methylation state within the genomic scope of cfDNA and is strongly associated with cancer development and progression. Several research teams have delved into the potential application of cfDNA methylation in identifying early-stage esophageal cancer and have achieved promising outcomes. Recent research supports the high sensitivity and specificity of cfDNA methylation in early esophageal cancer diagnosis, providing a more accurate and efficient approach for early detection and improved clinical management. Accordingly, this review aims to present an overview of methylation-based cfDNA research with a focus on the latest developments in the early detection of esophageal cancer. Additionally, this review summarizes advanced analytical technologies for cfDNA methylation that have significantly benefited from recent advancements in separation and detection techniques, such as methylated DNA immunoprecipitation sequencing (MeDIP-seq). Recent findings suggest that biomarkers based on cfDNA methylation may soon find successful applications in the early detection of esophageal cancer. However, large-scale prospective clinical trials are required to identify the potential of these biomarkers.

Epigenetic targeting of autophagy for cancer: DNA and RNA methylation

Autophagy, a crucial cellular mechanism responsible for degradation and recycling of intracellular components, is modulated by an intricate network of molecular signals. Its paradoxical involvement in oncogenesis, acting as both a tumor suppressor and promoter, has been underscored in recent studies. Central to this regulatory network are the epigenetic modifications of DNA and RNA methylation, notably the presence of N6-methyldeoxyadenosine (6mA) in genomic DNA and N6-methyladenosine (m6A) in eukaryotic mRNA. The 6mA modification in genomic DNA adds an extra dimension of epigenetic regulation, potentially impacting the transcriptional dynamics of genes linked to autophagy and, especially, cancer. Conversely, m6A modification, governed by methyltransferases and demethylases, influences mRNA stability, processing, and translation, affecting genes central to autophagic pathways. As we delve deeper into the complexities of autophagy regulation, the importance of these methylation modifications grows more evident. The interplay of 6mA, m6A, and autophagy points to a layered regulatory mechanism, illuminating cellular reactions to a range of conditions. This review delves into the nexus between DNA 6mA and RNA m6A methylation and their influence on autophagy in cancer contexts. By closely examining these epigenetic markers, we underscore their promise as therapeutic avenues, suggesting novel approaches for cancer intervention through autophagy modulation.

Genitourinary cancer neoadjuvant therapies: current and future approaches

Neoadjuvant therapies can improve tolerability, reduce tumor volume to facilitate surgery, and assess subsequent treatment response. Therefore, there is much enthusiasm for expanding the benefits of cancer therapies to the neoadjuvant setting to reduce recurrence and improve survival in patients with localized or locally advanced genitourinary (GU) cancer. This approach is clinically pertinent because these treatments are administered primarily to treatment-naive patients and can elicit the greatest drug response. In addition, the results are not impacted by other anticancer treatments. While neoadjuvant therapies have been the standard treatment for bladder cancer in the past, they are presently restricted to clinical trials for renal and prostate cancer (PCa); however, changes are imminent. Precision neoadjuvant therapies will be ushered in by biomarker-stratified neoadjuvant trials with appropriate survival endpoints and comprehensive correlative and imaging studies. This review discusses neoadjuvant studies in GU malignancies and how they inform future study design considerations.

Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor with an unsatisfactory prognosis. This study aims to identify the expression patterns of disulfidptosis-related genes (DRGs), develop a prognostic model, and predict immunological profiles.

METHODS

First, we identified differentially expressed DRGs in TCGA-KIRC cohort and analyzed their mutational profiles, methylation levels, and interaction networks. Subsequently, we identified disulfidptosis-associated molecular subtypes and investigated their prognostic and immunological characteristics. Simultaneously, a disulfidptosis-related prognostic signature (DRPS) was developed using a two-stage stacking framework consisting of 5 machine learning models. The effect of DRPS on immune cell infiltration levels was explored using seven different algorithms, and the status and function of T cells for distinct risk-score groups were evaluated based on T cell exhaustion and dysfunction scores. Additionally, the study also examined differences in clinical characteristics and therapy efficacy between high- and low-risk groups.

RESULTS

We found two disulfidptosis-associated clusters, one of which had a poor prognosis and was linked to high immune cell infiltration but impaired T cell function. DRPS showed excellent predictive performance in all four cohorts and could accurately identified disulfidptosis-related molecular subtypes. The DRPS-based risk score was positively associated with poor prognosis, malignant pathological features, high immune cell infiltration levels, and T cell exhaustion or dysfunction, and better respond to immunotherapy and targeted therapy. Additionally, we have identified a close association between ISG20 and disulfidptosis as well as tumor immunity.

CONCLUSION

Our study identified distinct disulfidptosis-related subtypes in ccRCC patients, and constructed the highly accurate and robust DRPS based on an ensemble learning framework, which has critical reference value in clinical decision-making and individualized treatment. And this work also revealed ISG20 exhibits promising potential as a therapeutic target for ccRCC.

ITIH5, as a predictor of prognosis and immunotherapy response for P53-like bladder cancer, is related to cell proliferation and invasion

p53-like bladder cancer (BLCA) is a bladder cancer subtype that is resistant to cisplatin-based chemotherapy. The ideal treatment modality for such tumors remains poorly defined, and immunotherapy seems to be a potential approach. Therefore, it is significant to understand the risk stratification of p53-like BLCA and identify novel therapeutic targets. ITIH5 is a member of the inter-α-trypsin inhibitory (ITI) gene family, and the effect of ITIH5 on p53-like BLCA remains elusive. In this study, TCGA data and in vitro experiments were used to explore the prognostic value of ITIH5 for p53-like BLCA and its effect on tumor cell proliferation, migration, and invasion. The impact of ITIH5 on the level of immune cell infiltration was explored using seven different algorithms, and the predictive value of ITIH5 on the efficacy of immunotherapy for p53-like BLCA was explored in combination with an independent immunotherapy cohort. The results showed that patients with high ITIH5 expression had a better prognosis, and overexpression of ITIH5 could inhibit the proliferation, migration, and invasion of tumor cells. Two or more algorithms consistently showed that ITIH5 promoted the infiltration of antitumor immune cells, such as B cells, CD4+ T cells, and CD8+ T cells. In addition, ITIH5 expression was positively correlated with the expression levels of many immune checkpoints, and the high ITIH5 expression group showed better response rates to PD-L1 and CTLA-4 therapies. In short, ITIH5 is a predictor of prognosis and the immunotherapy response for p53-like BLCA and is correlated with tumor immunity.

Bladder cancer intrinsic LRFN2 drives anticancer immunotherapy resistance by attenuating CD8+ T cell infiltration and functional transition

BACKGROUND

Immune checkpoint inhibitor (ICI) therapy improves the survival of patients with advanced bladder cancer (BLCA); however, its overall effectiveness is limited, and many patients still develop immunotherapy resistance. The leucine-rich repeat and fibronectin type-III domain-containing protein (LRFN) family has previously been implicated in regulating brain dysfunction; however, the mechanisms underlying the effect of LRFN2 on the tumor microenvironment (TME) and immunotherapy remain unclear.

METHODS

Here we combined bulk RNA sequencing, single-cell RNA sequencing, ProcartaPlex multiple immunoassays, functional experiments, and TissueFAXS panoramic tissue quantification assays to demonstrate that LRFN2 shapes a non-inflammatory TME in BLCA.

RESULTS

First, comprehensive multiomics analysis identified LRFN2 as a novel immunosuppressive target specific to BLCA. We found that tumor-intrinsic LRFN2 inhibited the recruitment and functional transition of CD8+ T cells by reducing the secretion of pro-inflammatory cytokines and chemokines, and this mechanism was verified in vitro and in vivo. LRFN2 restrained antitumor immunity by inhibiting the infiltration, proliferation, and differentiation of CD8+ T cells in vitro. Furthermore, a spatial exclusivity relationship was observed between LRFN2+ tumor cells and CD8+ T cells and cell markers programmed cell death-1 (PD-1) and T cell factor 1 (TCF-1). Preclinically, LRFN2 knockdown significantly enhanced the efficacy of ICI therapy. Clinically, LRFN2 can predict immunotherapy responses in real-world and public immunotherapy cohorts. Our results reveal a new role for LRFN2 in tumor immune evasion by regulating chemokine secretion and inhibiting CD8+ T-cell recruitment and functional transition.

CONCLUSIONS

Thus, LRFN2 represents a new target that can be combined with ICIs to provide a potential treatment option for BLCA.

A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited. Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts. Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram. The high glycosylation risk score group exhibited higher tumor immune cell infiltration, enrichment scores in immune therapy-related pathways, and a tendency towards a basal subtype. Conversely, the low-risk score group had minimal immune cell infiltration and tended to have a luminal subtype. These findings were consistent in our real-world Xiangya cohort. Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.

m5C methylation modification guides the prognostic value and immune landscapes in acute myeloid leukemia

The development, incidence, and metastasis of tumors are all intimately correlated with 5-methylcytosine (m5C). However, uncertainty surrounds the function of m5C in acute myeloid leukemia (AML). In this study, multicenter AML data were collected and analyzed comprehensively to grasp the gene expression level, clinicopathological characteristics, prognostic significance of m5C in AML and its relationship with the tumor microenvironment (TME). The m5C gene-mediated scoring system (m5CSS) was created using principal component analysis, and multiple cox regression analyses were utilized to determine the prognostic relevance of the m5C score. The investigation of the correlation among m5C, immune characteristics, clinical characteristics, immune infiltration level, as well as drug reaction at immune checkpoints, and immunotherapy efficacy confirmed that the change of the characteristics of immune cell infiltration and patient prognosis are linked with the m5C gene. Moreover, the m5CSS was employed to assess the pattern of m5C modification. Further analyses showed that the m5C score can served as a reliable indicator of AML prognosis. Crucially, the prognostic value of the m5C score was validated in terms of drug resistance and immunotherapy. This work reveals that AML diversity and the generation of complex TMEs are both impacted by m5C modifications. Therefore, understanding the m5C modification pattern will improve grasp of TME infiltration characteristics and assist exploring more efficient immunotherapeutic approaches.

Therapeutic potential of vasculogenic mimicry in urological tumors

Angiogenesis is an essential process in the growth and metastasis of cancer cells, which can be hampered by an anti-angiogenesis mechanism, thereby delaying the progression of tumors. However, the benefit of this treatment modality could be restricted, as most patients tend to develop acquired resistance during treatment. Vasculogenic mimicry (VM) is regarded as a critical alternative mechanism of tumor angiogenesis, where studies have demonstrated that patients with tumors supplemented with VM generally have a shorter survival period and a poorer prognosis. Inhibiting VM may be an effective therapeutic strategy to prevent cancer progression, which could prove helpful in impeding the limitations of lone use of anti-angiogenic therapy when performed concurrently with other anti-tumor therapies. This review summarizes the mechanism of VM signaling pathways in urological tumors, i.e., prostate cancer, clear cell renal cell carcinoma, and bladder cancer. Furthermore, it also summarizes the potential of VM as a therapeutic strategy for urological tumors.

A cancer-associated fibroblast subtypes-based signature enables the evaluation of immunotherapy response and prognosis in bladder cancer

Bladder cancer (BLCA) is one of the most prevalent and heterogeneous urinary malignant tumors. Previous researches have reported a significant association between cancer-associated fibroblasts (CAFs) and poor prognosis of tumor patients. However, uncertainty surrounds the role of CAFs in the BLCA tumor microenvironment, necessitating further investigation into the CAFs-related gene signatures in BLCA. In this study, we identified three CAF subtypes in BLCA according to single-cell RNA-seq data and constructed CAFs-related risk score (CRRS) by screening 102,714 signatures. The survival analysis, ROC curves, and nomogram suggested that CRRS was a valuable predictor in 2,042 patients from 9 available public datasets and Xiangya real-world cohort. We further revealed the significant correlation between CRRS and clinicopathological characteristics, genome alterations, and epithelial-mesenchymal transition (EMT). A high CRRS indicated a non-inflamed phenotype and a lower remission rate of immunotherapy in BLCA. In conclusion, the CRRS had the potential to predict the prognosis and immunotherapy response of BLCA patients.

Development and validation of prognostic index based on purine metabolism genes in patients with bladder cancer

Background

Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and is associated with significant morbidity and mortality worldwide. Dysregulation of tumor metabolic pathways is closely linked to the initiation and proliferation of BLCA. Tumor cells exhibit distinct metabolic activities compared to normal cells, and the purine metabolism pathway, responsible for providing essential components for DNA and RNA synthesis, is believed to play a crucial role. However, the precise involvement of Purine Metabolism Genes (PMGs) in the defense mechanism against BLCA remains elusive.

Methods

The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of PMGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and PMGs, unraveling their functional relevance and potential implications in BLCA.

Results

PMGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including CLDN6, CES1, SOST, SPRR2A, MYBPH, CGB5, and KRT1, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CRTAC1 was identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.

Conclusion

PMGs have been implicated in the etiology and progression of bladder cancer (BLCA). Prognostic models corresponding to this malignancy aid in the accurate prediction of patient outcomes. Notably, exploring the potential therapeutic targets within the tumor microenvironment (TME) such as PMGs and immune cell infiltration holds promise for effective BLCA management, albeit necessitating further research. Moreover, the identification of a gene signature associated with purine Metabolism presents a credible and alternative approach for predicting BLCA, signifying a burgeoning avenue for targeted therapeutic investigations in the field of BLCA.

Predicting the risk of distant metastasis in patients with locally advanced rectal cancer using model based on pre-treatment T2WI-based radiomic features plus postoperative pathological stage

Objective

To assess the prognostic value of a model based on pre-treatment T2WI-based radiomic features and postoperative pathological staging in patients with locally advanced rectal cancer who have undergone neoadjuvant chemoradiotherapy.

Methods

Radiomic features were derived from T2WI, and a radiomic signature (RS) was established and validated for the prediction of distant metastases (DM). Subsequently, we designed and validated a nomogram model that combined the radiomic signature and postoperative pathological staging for enhanced DM prediction. Performance measures such as the concordance index (C-index) and area under the curve (AUC) were computed to assess the predictive accuracy of the models.

Results

A total of 260 patients participated in this study, of whom 197 (75.8%) were male, and the mean age was 57.2 years with a standard deviation of 11.2 years. 15 radiomic features were selected to define the radiomic signature. Patients with a high-risk radiomic signature demonstrated significantly shorter distant metastasis-free survival (DMFS) in both the development and validation cohorts. A nomogram, incorporating the radiomic signature, pathological T stage, and N stage, achieved an area under the curve (AUC) value of 0.72 (95% CI, 0.60-0.83) in the development cohort and 0.83 (95% CI, 0.73-0.92) in the validation cohort.

Conclusion

A radiomic signature derived from T2WI-based radiomic features can effectively distinguish patients with varying risks of DM. Furthermore, a nomogram integrating the radiomic signature and postoperative pathological stage proves to be a robust predictor of DMFS.

S100A5 Attenuates Efficiency of Anti-PD-L1/PD-1 Immunotherapy by Inhibiting CD8+ T Cell-Mediated Anti-Cancer Immunity in Bladder Carcinoma

Although immune checkpoint blockade (ICB) therapies have been approved for bladder cancer (BLCA), only a minority of patients respond to these therapies, and there is an urgent need to explore combined therapies. Systematic multi-omics analysis identified S100A5 as a novel immunosuppressive target for BLCA. The expression of S100A5 in malignant cells inhibited CD8+ T cell recruitment by decreasing pro-inflammatory chemokine secretion. Furthermore, S100A5 attenuated effector T cell killing of cancer cells by inhibiting CD8+ T cell proliferation and cytotoxicity. In addition, S100A5 acted as an oncogene, thereby promoting tumor proliferation and invasion. Targeting S100A5 synergized with the efficacy of anti-PD-1 treatment by enhancing infiltration and cytotoxicity of CD8+ T cells in vivo. Clinically, there was a spatially exclusive relationship between S100A5+ tumor cells and CD8+ T cells in tissue microarrays. Moreover, S100A5 negatively correlated with immunotherapy efficacy in our real-world and several public immunotherapy cohorts. In summary, S100A5 shapes a non-inflamed tumor microenvironment in BLCA by inhibiting the secretion of pro-inflammatory chemokines and the recruitment and cytotoxicity of CD8+ T cells. Targeting S100A5 converts cold tumors into hot tumors, thus enhancing the efficacy of ICB therapy in BLCA.

Molecular classification of urothelial bladder carcinoma

Urothelial bladder carcinoma (UC) ranks among the top ten most commonly diagnosed cancers worldwide on an annual basis. The standardized classification system for urothelial bladder tumors is the Tumor, Node, Metastasis classification, which reflects differences between non-muscle-invasive bladder carcinoma (NMIBC) and muscle-invasive bladder carcinoma (MIBC) and it depends on the extent to which tumor has infiltrated the bladder wall and other tissues and organs. NMIBC and MIBC exhibit great intrinsic heterogeneity regarding different prognoses, survival, progression, and treatment outcomes. In recent years, studies based on mRNA expression profiling revealed the existence of biologically relevant molecular subtypes of UC, which show variant molecular features that can provide more precise stratification of UC patients. Here, we present a complex classification of UC based on mRNA expression studies and molecular subtypes of NMIBC and MIBC in detail with regard to different mRNA expression profiles, mutational signatures, and infiltration by non-tumor cells. The possible impact of molecular subtyping on treatment decisions and patients' outcomes is outlined, too.

Construction and validation of a signature for T cell-positive regulators related to tumor microenvironment and heterogeneity of gastric cancer

Background

Positive regulators of T cell function play a vital role in the proliferation and differentiation of T cells. However, their functions in gastric cancer have not been explored so far.

Methods

The TCGA-STAD dataset was utilized to perform consensus clustering in order to identify subtypes related to T cell-positive regulators. The prognostic differentially expressed genes of these subtypes were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. To validate the robustness of the identified signature, verification analyses were conducted across the TCGA-train, TCGA-test, and GEO datasets. Additionally, a nomogram was constructed to enhance the clinical efficacy of this predictive tool. Transwell migration, colony formation, and T cell co-culture assays were used to confirm the function of the signature gene in gastric cancer and its influence on T cell activation.

Results

Two distinct clusters of gastric cancer, related to T cell-positive regulation, were discovered through the analysis of gene expression. These clusters exhibited notable disparities in terms of survival rates (P = 0.028), immune cell infiltration (P< 0.05), and response to immunotherapy (P< 0.05). Furthermore, a 14-gene signature was developed to classify gastric cancer into low- and high-risk groups, revealing significant differences in survival rates, tumor microenvironment, tumor mutation burden, and drug sensitivity (P< 0.05). Lastly, a comprehensive nomogram model was constructed, incorporating risk factors and various clinical characteristics, to provide an optimal predictive tool. Additionally, an assessment was conducted on the purported molecular functionalities of low- and high-risk gastric cancers. Suppression of DNAAF3 has been observed to diminish the migratory and proliferative capabilities of gastric cancer, as well as attenuate the activation of T cells induced by gastric cancer within the tumor microenvironment.

Conclusion

We identified an ideal prognostic signature based on the positive regulators of T cell function in this study.

Prognosis analysis and validation of lipid metabolism-associated lncRNAs and tumor immune microenvironment in bladder cancer

BACKGROUND

Numerous types of research revealed that long noncoding RNAs (lncRNAs) played a significant role in immune response and the tumor microenvironment of bladder cancer (BLCA). Dysregulated lipid metabolism is considered to be one of the major risk factors for BLCA, the study aimed to detect the lipid metabolism-related lncRNAs (LMRLs) along with their potential prognostic values and immune correlations in BLCA.

METHODS

We collected lipid metabolism-related genes, expression profiles, and clinical information on BLCA from the Molecular Signature Database (MSigDB) and the TCGA database, respectively. Differentially expressed lipid metabolism genes (DE-LMRGs) and differentially expressed long non-coding RNAs (DE-lncRNAs) were selected using the limma package. Spearman correlation analysis was employed to explore the correlations between DE-lncRNAs and DE-LMRGs and to further develop protein-protein interaction (PPI) networks and perform mutational analysis. The least absolute shrinkage and selection operator (LASSO) and univariate Cox analysis were then employed to construct a prognostic risk model. The performance of the model was evaluated using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and consistency indices. In addition, we downloaded the GSE31684 dataset for external validation of the prognostic signature. Moreover, we explored the association of the risk model with immune cell infiltration and chemotherapy response analysis to reveal the tumor immune microenvironment of BLCA. Finally, RT-qPCR was utilized to validate the expression of prognostic genes.

RESULTS

A total of 48 DE-LncRNAs and 33 DE-LMRGs were found to be robustly correlated, and were used to construct a lncRNA-mRNA co-expression network, in which ACACB, ACOX2, and BCHE showed high mutation rates. Then, a risk model based on three LMRLs (RP11-465B22.8, MIR100HG, and LINC00865) was constructed. The risk model effectively distinguished between the clinical outcomes of BLCA patients, with high-risk scores indicating a worse prognosis and with substantial prognostic prediction accuracy. The model's results were consistent in the GSE31684 dataset. In addition, a nomogram was constructed based on the risk score, age, pathological T-stage, and pathological N-stage, which showed robust predictive power. Immune landscape analysis indicated that the risk model was significantly associated with T-cell CD4 memory activation, M1 macrophage, M2 macrophage, dendritic cell activation, and T-cell regulatory. We predicted that 49 drugs would perform satisfactorily in the high-risk group. Additionally, we found five m6A regulators associated with the high- and low-risk groups, suggesting that upstream regulation of LncRNA could be a novel target for BLCA treatment. Finally, RT-qPCR showed that RP11-465B22.8 was highly expressed in BLCA, while MIR100HG and LINC00865 were downregulated in BLCA.

CONCLUSION

Our findings suggest that the three LMRLs may serve as potential prognostic and immunotherapeutic biomarkers in BLCA. In addition, our study provides new ideas for understanding the pathogenic mechanisms and developing therapeutic strategies for BLCA patients.

CT-based radiomic phenotypes of lung adenocarcinoma: a preliminary comparative analysis with targeted next-generation sequencing

Objectives

This study aimed to explore the relationship between computed tomography (CT)-based radiomic phenotypes and genomic profiles, including expression of programmed cell death-ligand 1 (PD-L1) and the 10 major genes, such as epidermal growth factor receptor (EGFR), tumor protein 53 (TP53), and Kirsten rat sarcoma viral oncogene (KRAS), in patients with lung adenocarcinoma (LUAD).

Methods

In total, 288 consecutive patients with pathologically confirmed LUAD were enrolled in this retrospective study. Radiomic features were extracted from preoperative CT images, and targeted genomic data were profiled through next-generation sequencing. PD-L1 expression was assessed by immunohistochemistry staining (chi-square test or Fisher's exact test for categorical data and the Kruskal-Wallis test for continuous data). A total of 1,013 radiomic features were obtained from each patient's CT images. Consensus clustering was used to cluster patients on the basis of radiomic features.

Results

The 288 patients were classified according to consensus clustering into four radiomic phenotypes: Cluster 1 (n = 11) involving mainly large solid masses with a maximum diameter of 5.1 ± 2.0 cm; Clusters 2 and 3 involving mainly part-solid and solid masses with maximum diameters of 2.1 ± 1.4 cm and 2.1 ± 0.9 cm, respectively; and Cluster 4 involving mostly small ground-glass opacity lesions with a maximum diameter of 1.0 ± 0.9 cm. Differences in maximum diameter, PD-L1 expression, and TP53, EGFR, BRAF, ROS1, and ERBB2 mutations among the four clusters were statistically significant. Regarding targeted therapy and immunotherapy, EGFR mutations were highest in Cluster 2 (73.1%); PD-L1 expression was highest in Cluster 1 (45.5%).

Conclusion

Our findings provide evidence that CT-based radiomic phenotypes could non-invasively identify LUADs with different molecular characteristics, showing the potential to provide personalized treatment decision-making support for LUAD patients.

Four types of adenine-related RNA modification writers -mediated molecular subtypes contribute to predicting clinical outcomes and treatment options in bladder cancer

Rationale

RNA modifications, containing m6A, m1A, alternative polyadenylation and adenosine-to-inosine RNA editing, involve in critical cancerous immunity and cancerous processes. However, the functional roles of RNA modification writers in bladder cancer (BLCA) are largely unknown.

Methods

In this study, unsupervised clustering was used to identify novel RNA modification writers -mediated molecular subtypes in BLCA. A corresponding quantitative indicator called WriterScore was developed using univariate Cox and Least absolute shrinkage and selection operator (LASSO) analysis. Then, we systematically analyzed the correlation between RNA modification writer-related clusters (WriterScore) and immunological characteristics, classical molecular subtypes, clinicopathologic features and treatment options in BLCA. Finally, we validated the WriterScore in multiple other external BLCA datasets, clinical sample dataset in Shengjing Hospital and pancancer.

Results

Two RNA modification writer-related clusters and three DEGclusters were obtained. These RNA modification writer-related clusters (WriterScore) were strongly associated with immunological characteristics, classical molecular subtypes, clinicopathologic features of BLCA. Moreover, WriterScore can properly predict the clinical outcomes and immunotherapy of BLCA patients.

Conclusion

Our study systematically investigated the role of RNA modification writers and developed a significant WriterScore to guide several treatment options in BLCA, which might bring some potential benefits for BLCA patients.

Neutrophil extracellular traps-related signature predicts the prognosis and immune infiltration in gastric cancer

Introduction

Gastric cancer (GC) is the fifth most prevalent cancer globally, with the third highest case fatality rate. Neutrophil extracellular traps (NETs) are a reticulated structure of DNA, histones, and antimicrobial peptides produced by active neutrophils that trap pathogens. Even though NETs are associated with poorer recurrence-free survival (RFS) and overall survival (OS), the specifics of this interaction between NETs and cancer cells are yet unknown.

Methods

The keywords "neutrophil extracellular traps and gastric cancer" were used in the GEO database for retrieval, and the GSE188741 dataset was selected to obtain the NETs-related gene. 27 NETs-related genes were screened by univariate Cox regression analysis (p < 0.05). 27 NETs-related genes were employed to identify and categorize NETs-subgroups of GC patients under the Consensus clustering analysis. 808 GC patients in TCGA-STAD combined with GES84437 were randomly divided into a training group (n = 403) and a test group (n = 403) at a ratio of 1:1 to validate the NETs-related signature.

Results

Based on Multivariate Cox regression and LASSO regression analysis to develop a NETs-related prognosis model. We developed a very specific nomogram to improve the NETs-clinical score's usefulness. Similarly, we also performed a great result in pan-cancer study with NETs-score. Low NETs scores were linked to higher MSI-H (microsatellite instability-high), mutation load, and immune activity. The cancer stem cell (CSC) index and chemotherapeutic treatment sensitivity were also connected to the NET score. Our comprehensive analysis of NETs in GC suggests that NETs have a role in the tumor microenvironment, clinicopathological features, and prognosis.

Discussion

The NETs-score risk model provides a basis for better prognosis and therapy outcomes in GC patients.

Functional status analysis of RNH1 in bladder cancer for predicting immunotherapy response

Bladder cancer (BLCA) typically has a poor prognosis due to high rates of relapse and metastasis. Although the emergence of immunotherapy brings hope for patients with BLCA, not all patients will benefit from it. Identifying some markers to predict treatment response is particularly important. Here, we aimed to determine the clinical value of the ribonuclease/angiogenin inhibitor 1 (RNH1) in BLCA therapy based on functional status analysis. First, we found that RNH1 is aberrantly expressed in multiple cancers but is associated with prognosis in only a few types of cancer. Next, we determined that low RNH1 expression was significantly associated with enhanced invasion and metastasis of BLCA by assessing the relationship between RNH1 and 17 functional states. Moreover, we identified 95 hub genes associated with invasion and metastasis among RNH1-related genes. Enrichment analysis revealed that these hub genes were also significantly linked with immune activation. Consistently, BLCA can be divided into two molecular subtypes based on these hub genes, and the differentially expressed genes between the two subtypes are also significantly enriched in immune-related pathways. This indicates that the expression of RNH1 is also related to the tumour immune response. Subsequently, we confirmed that RNH1 shapes an inflammatory tumour microenvironment (TME), promotes activation of the immune response cycle steps, and has the potential to predict the immune checkpoint blockade (ICB) treatment response. Finally, we demonstrated that high RNH1 expression was significantly associated with multiple therapeutic signalling pathways and drug targets in BLCA. In conclusion, our study revealed that RNH1 could provide new insights into the invasion of BLCA and predict the immunotherapy response in patients with BLCA.

Up-regulated serum lactate dehydrogenase could become a poor prognostic marker in patients with bladder cancer by an evidence-based analysis of 2,182 patients

Background

A growing number of studies have considered serum lactate dehydrogenase (LDH) as an indicator of bladder cancer (BC) prognosis. However, a meta-analysis of the serum LDH's influence on BC prognosis is still missing.

Methods

PubMed, EMBASE, Web of Science and Cochrane Library were exhaustively searched for studies comparing oncological outcomes between high-LDH and low-LDH patients. Standard cumulative analyses using hazard ratios (HR) with 95% confidence intervals (CI) were performed using Review Manager (version 5.3) for overall survival (OS) in patients with BC.

Results

Six studies involving 2,182 patients were selected according to predefined eligibility criteria. The results showed that serum LDH level was significantly associated with OS (HR = 1.86, 95%CI = 1.54-2.25, p<0.0001) in BC. Sensitivity analysis showed the stability of the results. Subgroup analysis revealed that the levels of serum LDH had a significant impact on the OS of BC patients among different groups including publication time, research country, sample size, tumor stage, LDH cut-off value, therapy and follow-up time (all HR>1 and p<0.05), revealing that the ability of serum LDH is not affected by other factors.

Conclusion

Our findings indicated that a high level of serum LDH was associated with inferior OS in patients with BC. However, caution must be taken before recommendations are given because this interpretation is based upon very few clinical studies and a small sample.

Machine learning-based integration develops a neutrophil-derived signature for improving outcomes in hepatocellular carcinoma

Introduction

The heterogeneity of tumor immune microenvironments is a major factor in poor prognosis among hepatocellular carcinoma (HCC) patients. Neutrophils have been identified as playing a critical role in the immune microenvironment of HCC based on recent single-cell studies. However, there is still a need to stratify HCC patients based on neutrophil heterogeneity. Therefore, developing an approach that efficiently describes "neutrophil characteristics" in HCC patients is crucial to guide clinical decision-making.

Methods

We stratified two cohorts of HCC patients into molecular subtypes associated with neutrophils using bulk-sequencing and single-cell sequencing data. Additionally, we constructed a new risk model by integrating machine learning analysis from 101 prediction models. We compared the biological and molecular features among patient subgroups to assess the model's effectiveness. Furthermore, an essential gene identified in this study was validated through molecular biology experiments.

Results

We stratified patients with HCC into subtypes that exhibited significant differences in prognosis, clinical pathological characteristics, inflammation-related pathways, levels of immune infiltration, and expression levels of immune genes. Furthermore, A risk model called the "neutrophil-derived signature" (NDS) was constructed using machine learning, consisting of 10 essential genes. The NDS's RiskScore demonstrated superior accuracy to clinical variables and correlated with higher malignancy degrees. RiskScore was an independent prognostic factor for overall survival and showed predictive value for HCC patient prognosis. Additionally, we observed associations between RiskScore and the efficacy of immune therapy and chemotherapy drugs.

Discussion

Our study highlights the critical role of neutrophils in the tumor microenvironment of HCC. The developed NDS is a powerful tool for assessing the risk and clinical treatment of HCC. Furthermore, we identified and analyzed the feasibility of the critical gene RTN3 in NDS as a molecular marker for HCC.

Prognostic significance of albumin-to-globulin ratio in patients with renal cell carcinoma: a meta-analysis

Background

Whether the albumin-to-globulin ratio (AGR) predicts the prognosis of renal cell carcinoma (RCC) remains controversial. Herein, we performed a meta-analysis to critically evaluate the relationship between the AGR and RCC prognosis, as well as the association between the AGR and the clinicopathological characteristics of RCC.

Methods

The PubMed, Web of Science, Embase, and Cochrane Library databases were thoroughly and comprehensively searched from their inception until 24 June 2023. To determine the predictive significance of the AGR, hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated from the pooled data. The relationship between the AGR and the clinicopathological features of RCC was evaluated by estimating odds ratios (ORs) and 95% CIs in subgroup analyses.

Results

The meta-analysis included nine articles involving 5,671 RCC cases. A low AGR significantly correlated with worse overall survival (OS) (HR = 1.82, 95% CI = 1.37-2.41, p <0.001) and progression-free survival (PFS) (HR = 2.44, 95% CI = 1.61-3.70, p <0.001). Analysis of the pooled data also revealed significant associations between a low AGR and the following: female sex (OR = 1.48, 95% CI = 1.31-1.67, p <0.001), pT stage T3-T4 (OR = 4.12, 95% CI = 2.93-5.79, p <0.001), pN stage N1 (OR = 3.99, 95% CI = 2.40-6.64, p <0.001), tumor necrosis (OR = 3.83, 95% CI = 2.23-6.59, p <0.001), and Fuhrman grade 3-4 (OR = 1.82, 95% CI = 1.34-2.42, p <0.001). The AGR was not related to histology (OR = 0.83, 95% CI = 0.60-1.15, p = 0.267).

Conclusion

In patients with RCC, a low AGR strongly predicted poor OS and PFS and significantly correlated with clinicopathological features indicative of disease progression.

TAF1D Functions as a Novel Biomarker in Osteosarcoma

Background: The most frequent primary bone cancer in teenagers, osteosarcoma (OS), is particularly aggressive with a high mortality rate. Methods: By combining public databases, OS and non-cancer samples were obtained. The Wilcoxon test and standardized mean difference (SMD) were utilized to evaluate the mRNA expression level of TATA-box binding protein associated factor, RNA polymerase 1 subunit D (TAF1D). The potential of TAF1D to discriminate OS samples from non-cancer samples was revealed by summary receiver operating characteristic curve (sROC). To investigate the prognostic significance, Kaplan‒Meier curve and univariate Cox analysis were performed. Immunohistochemistry (IHC) was used to determine the TAF1D protein expression level. ESTIMATE algorithm and TIMER2.0 database were used to reveal the association between TAF1D expression and the immune microenvironment. Enrichment analysis and potential drug prediction were performed to clarify the underlying molecular mechanisms and possible therapeutic directions of TAF1D. Ultimately, the transcription factors (TFs) and the TAF1D binding site were predicted based on the Cistrome and JASPAR databases. Results: TAF1D was upregulated in OS at the mRNA and protein levels and possessed robust discriminatory power. TAF1D upregulation was suggestive of worse prognosis and enhancement of tumor purity in OS patients. The cell cycle was the most significantly enriched pathway, and NU.1025 was considered to be the potential target agent. Finally, MYC was identified as a TF that regulates the expression of TAF1D. Conclusions: Altogether, TAF1D has the potential to serve as a biological marker and therapeutic target in OS, which could offer new perspectives for OS treatment.

ACSM6 overexpression indicates a non-inflammatory tumor microenvironment and predicts treatment response in bladder cancer: results from multiple real-world cohorts

Background: ACSMs play critical roles in lipid metabolism; however, their immunological function within the tumor microenvironment (TME) remains unclear, especially that of ACSM6. In this study, we investigate the latent effect of ACSM6 on bladder cancer (BLCA). Methods: Several real-world cohorts, including the Xiangya (in-house), The Cancer Genome Atlas (TCGA-BLCA), and IMvigor210 cohorts, with TCGA-BLCA cohort serving as the discovery cohort were compared. We investigated the potential immunological effects of ACSM6 in regulating the BLCA tumor microenvironment by analyzing its correlation with immunomodulators, anti-cancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, we assessed the precision of ACSM6 in predicting BLCA molecular subtypes and responses to several treatments using ROC analysis. To ensure the robustness of our findings, all results were confirmed in two independent external cohorts: the IMvigor210 and Xiangya cohorts. Results: ACSM6 expression was markedly upregulated in BLCA. Our analysis suggests that ACSM6 might have significant impact to promote the formation of a non-inflamed tumor microenvironment because of its negative correlation with immunomodulators, anticancer immune cycles, immune checkpoints, tumor-infiltrating immune cells, and the T-cell inflamed score (TIS). Additionally, high ACSM6 expression levels in BLCA may predict the luminal subtype, which is typically associated with resistance to chemotherapy, neoadjuvant chemotherapy, and radiotherapy. These findings were consistent across both the IMvigor210 and Xiangya cohorts. Conclusion: ACSM6 has the potential to serve as a valuable predictor of the tumor microenvironment phenotypes and treatment outcomes in BLCA, thereby contributing to more precise treatment.

BCAT2 Shapes a Noninflamed Tumor Microenvironment and Induces Resistance to Anti-PD-1/PD-L1 Immunotherapy by Negatively Regulating Proinflammatory Chemokines and Anticancer Immunity

To improve response rate of monotherapy of immune checkpoint blockade (ICB), it is necessary to find an emerging target in combination therapy. Through analyzing tumor microenvironment (TME)-related indicators, it is validated that BCAT2 shapes a noninflamed TME in bladder cancer. The outcomes of multiomics indicate that BCAT2 has an inhibitory effect on cytotoxic lymphocyte recruitment by restraining activities of proinflammatory cytokine/chemokine-related pathways and T-cell-chemotaxis pathway. Immunoassays reveal that secretion of CD8⁺ T-cell-related chemokines keeps a robust negative correlation with BCAT2, generating a decreasing tendency of CD8⁺ T cells around BCAT2⁺ tumor cells from far to near. Cotreatment of BCAT2 deficiency and anti-PD-1 antibody has a synergistic effect in vivo, implying the potential of BCAT2 in combination therapy. Moreover, the value of BCAT2 in predicting efficacy of immunotherapy is validated in multiple immunotherapy cohorts. Together, as a key molecule in TME, BCAT2 is an emerging target in combination with ICB and a biomarker of guiding precision therapy.

Molecular vasculogenic mimicry-Related signatures predict clinical outcomes and therapeutic responses in bladder cancer: Results from real-world cohorts

Bladder cancer (BLCA) is a heterogeneous disease, and there are many classical molecular subtypes that reflect tumor immune microenvironment (TME) heterogeneity but their clinical utility is limited and correct individual treatment and prognosis cannot be predicted based on them. To find reliable and effective biomarkers and tools for predicting patients' clinical responses to several therapies, we developed a new systemic indicator of molecular vasculogenic mimicry (VM)-related genes mediated by molecular subtypes based on the Xiangya cohort and additional external BLCA cohorts using a random forest algorithm. A correlation was then done between the VM_Score and classical molecular subtypes, clinical outcomes, immunophenotypes, and treatment options for BLCA. With the VM_Score, it is possible to predict classical molecular subtypes, immunophenotypes, prognosis, and therapeutic potential of BLCA with high accuracy. The VM_Scores of high levels indicate a more anticancer immune response but a worse prognosis due to a more basal and inflammatory phenotype. The VM_Score was also found associated with low sensitivity to antiangiogenic and targeted therapies targeting the FGFR3, β-catenin, and PPAR-γ pathways but with high sensitivity to cancer immunotherapy, neoadjuvant chemotherapy, and radiotherapy. A number of aspects of BLCA biology were reflected in the VM_Score, providing new insights into precision medicine. Additionally, the VM_Score may be used as an indicator of pan-cancer immunotherapy response and prognosis.

5mC modification patterns provide novel direction for early acute myocardial infarction detection and personalized therapy

Background

Most deaths from coronary artery disease (CAD) are due to acute myocardial infarction (AMI). There is an urgent need for early AMI detection, particularly in patients with stable CAD. 5-methylcytosine (5mC) regulatory genes have been demonstrated to involve in the progression and prognosis of cardiovascular diseases, while little research examined 5mC regulators in CAD to AMI progression.

Method

Two datasets (GSE59867 and GSE62646) were downloaded from Gene Expression Omnibus (GEO) database, and 21 m5C regulators were extracted from previous literature. Dysregulated 5mC regulators were screened out by "limma." The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm were employed to identify hub 5mC regulators in CAD to AMI progression, and 43 clinical samples (Quantitative real-time PCR) were performed for expression validation. Then a logistic model was built to construct 5mC regulator signatures, and a series of bioinformatics algorithms were performed for model validation. Besides, 5mC-associated molecular clusters were studied via unsupervised clustering analysis, and correlation analysis between immunocyte and 5mC regulators in each cluster was conducted.

Results

Nine hub 5mC regulators were identified. A robust model was constructed, and its prominent classification accuracy was verified via ROC curve analysis (area under the curve [AUC] = 0.936 in the training cohort and AUC = 0.888 in the external validation cohort). Besides, the clinical effect of the model was validated by decision curve analysis. Then, 5mC modification clusters in AMI patients were identified, along with the immunocyte infiltration levels of each cluster. The correlation analysis found the strongest correlations were TET3-Mast cell in cluster-1 and TET3-MDSC in cluster-2.

Conclusion

Nine hub 5mC regulators (DNMT3B, MBD3, UHRF1, UHRF2, NTHL1, SMUG1, ZBTB33, TET1, and TET3) formed a diagnostic model, and concomitant results unraveled the critical impact of 5mC regulators, providing interesting epigenetics findings in AMI population vs. stable CAD.

Identification of Immune-Related Subtypes and Construction of a Novel Prognostic Model for Bladder Urothelial Cancer

The purpose of this study was to explore the relationship between bladder urothelial cancer (BLCA) and immunity, to screen prognosis-related immune genes (PIGs), and to construct an immune-related prognosis model (IRPM). We processed the relevant data of The Cancer Genome Atlas (TCGA-BLCA) and GSE13507 using R software and Perl. We divided BLCA into high-immunity and low-immunity subtypes. There were significant differences in the two subtypes. In addition, we identified 13 PIGs of BLCA by jointly analyzing the gene expression data and survival information of GSE13507 and TCGA-BLCA, and constructed IRPM through nine of them. The low-risk group had better survival outcome than the high-risk group. We also constructed a nomogram based on clinicopathological information and risk scores of the patients. Moreover, the prognosis of BLCA patients was significantly impacted by the expression of almost every gene used to calculate the risk score. The result of real-time fluorescence quantitative polymerase chain reaction revealed that all the genes used to calculate the risk score were differentially expressed between BLCA and adjacent normal tissues, except PDGFRA. Our research provided potential targets for the treatment of BLCA and a reference for judging the prognosis of BLCA.

Neoadjuvant immunotherapy, chemotherapy, and combination therapy in muscle-invasive bladder cancer: A multi-center real-world retrospective study

To parallelly compare the efficacy of neoadjuvant immunotherapy (tislelizumab), neoadjuvant chemotherapy (gemcitabine and cisplatin), and neoadjuvant combination therapy (tislelizumab + GC) in patients with muscle-invasive bladder cancer (MIBC) and explore the efficacy predictors, we perform a multi-center, real-world cohort study that enrolls 253 patients treated with neoadjuvant treatments (combination therapy: 98, chemotherapy: 107, and immunotherapy: 48) from 15 tertiary hospitals. We demonstrate that neoadjuvant combination therapy achieves the highest complete response rate and pathological downstaging rate compared with neoadjuvant immunotherapy or chemotherapy. We develop and validate an efficacy prediction model consisting of pretreatment clinical characteristics, which can pinpoint candidates to receive neoadjuvant combination therapy. We also preliminarily reveal that patients who achieve pathological complete response after neoadjuvant treatments plus maximal transurethral resection of the bladder tumor may be safe to receive bladder preservation therapy. Overall, this study highlights the benefit of neoadjuvant combination therapy based on tislelizumab for MIBC.

Artificial Intelligence Meets Whole Slide Images: Deep Learning Model Shapes an Immune-Hot Tumor and Guides Precision Therapy in Bladder Cancer

Background

To construct and validate a deep learning cluster from whole slide images (WSI) for depicting the immunophenotypes and functional heterogeneity of the tumor microenvironment (TME) in patients with bladder cancer (BLCA) and to explore an artificial intelligence (AI) score to explore the underlying biological pathways in the developed WSI cluster.

Methods

In this study, the WSI cluster was constructed based on a deep learning procedure. Further rerecognition of TME features in pathological images was applied based on a neural network. Then, we integrated the TCGA cohort and several external testing cohorts to explore and validate this novel WSI cluster and a corresponding quantitative indicator, the AI score. Finally, correlations between the AI cluster (AI score) and classical BLCA molecular subtypes, immunophenotypes, functional heterogeneity, and potential therapeutic method in BLCA were assessed.

Results

The WSI cluster was identified associated with clinical survival (P < 0.001) and was proved as an independent predictor (P = 0.031), which could also predict the immunology and the clinical significance of BLCA. Rerecognition of pathological images established a robust 3-year survival prediction model (with an average classification accuracy of 86%, AUC of 0.95) for BLCA patients combining TME features and clinical features. In addition, an AI score was constructed to quantify the underlying logic of the WSI cluster (AUC = 0.838). Finally, we hypothesized that high AI score shapes an immune-hot TME in BLCA. Thus, treatment options including immune checkpoint blockade (ICB), chemotherapy, and ERBB therapy can be used for the treatment of BLCA patients in WSI cluster1 (high AI score subtype).

Conclusions

In general, we showed that deep learning can predict prognosis and may aid in the precision medicine for BLCA directly from H&E histology, which is more economical and efficient.

Role of 5-methylcytosine in determining the prognosis, tumor microenvironment, and applicability of precision medicine in patients with hepatocellular carcinoma

Background: 5-methylcytosine has a profound impact on the development and progression of hepatocellular carcinoma. The aim of this study was to investigate the usefulness of 5-methylcytosine in determining the prognosis, tumor microenvironment, and applicability of precision medicine in hepatocellular carcinoma.

Methods: We collected data of seven hepatocellular carcinoma cohorts (The Cancer Genome Atlas, International Cancer Genome Consortium, GSE14520, GSE6764, GSE9843, GSE63898, GSE76427). An unsupervised clustering method was used to identify novel subtypes of hepatocellular carcinoma based on the expression 5-methylcytosine gene signatures. The 5-methylcytosine score was determined using the least absolute shrinkage and selection operator method based on the differential expression of genes in the identified subtypes. Subsequently, we investigated the association between 5-methylcytosine-based clusters (according to the 5-methylcytosine score) and clinical outcomes, immunophenotypes, classical molecular subtypes, and therapeutic opportunities in hepatocellular carcinoma. Finally, we examined the sensitivity of patients with high 5-methylcytosine score to drugs.

Results: We identified two hepatocellular carcinoma-specific, 5-methylcytosine-based subtypes (clusters 1 and 2). Cluster 1 exhibited significantly higher 5-methylcytosine scores versus cluster 2. The 5-methylcytosine-based subtypes accurately predicted classical molecular subtypes, immunophenotypes, prognosis, and therapeutic opportunities for patients with hepatocellular carcinoma. Cluster 1 (high 5-methylcytosine score) was characterized by lower anticancer immunity and worse prognosis versus cluster 2 (low 5-methylcytosine score). Moreover, cluster 1 (high 5-methylcytosine score) exhibited low sensitivity to cancer immunotherapy, but high sensitivity to radiotherapy and targeted therapy with lenvatinib.

Conclusion: The novel 5-methylcytosine-based subtypes (according to the 5-methylcytosine score) may reflect the prognosis, tumor microenvironment, and applicability of precision medicine in patients with hepatocellular carcinoma.

m7G regulator-mediated molecular subtypes and tumor microenvironment in kidney renal clear cell carcinoma

Background: RNA methylation modification plays an important role in immune regulation. m7G RNA methylation is an emerging research hotspot in the RNA methylation field. However, its role in the tumor immune microenvironment of kidney renal clear cell carcinoma (KIRC) is still unclear.

Methods: We analyzed the expression profiles of 29 m7G regulators in KIRC, integrated multiple datasets to identify a novel m7G regulator-mediated molecular subtype, and developed the m7G score. We evaluated the immune tumor microenvironments in m7G clusters and analyzed the correlation of the m7G score with immune cells and drug sensitivity. We tested the predictive power of the m7G score for prognosis of patients with KIRC and verified the predictive accuracy of the m7G score by using the GSE40912 and E-MTAB-1980 datasets. The genes used to develop the m7G score were verified by qRT-PCR. Finally, we experimentally analyzed the effects of WDR4 knockdown on KIRC proliferation, migration, invasion, and drug sensitivity.

Results: We identified three m7G clusters. The expression of m7G regulators was higher in cluster C than in other clusters. m7G cluster C was related to immune activation, low tumor purity, good prognosis, and low m7G score. Cluster B was related to drug metabolism, high tumor purity, poor survival, and high m7G score. Cluster A was related to purine metabolism. The m7G score can well-predict the prognosis of patients with KIRC, and its prediction accuracy based on the m7G score nomogram was very high. Patients with high m7G scores were more sensitive to rapamycin, gefitinib, sunitinib, and vinblastine than other patients. Knocking down WDR4 can inhibit the proliferation, migration, and invasion of 786-0 and Caki-1 cells and increase sensitivity to sorafenib and sunitinib.

Conclusion: We proposed a novel molecular subtype related to m7G modification and revealed the immune cell infiltration characteristics of different subtypes. The developed m7G score can well-predict the prognosis of patients with KIRC, and our research provides a basis for personalized treatment of patients with KIRC.

Construction of a 5-methylcytosine-Related Molecular Signature to Inform the Prognosis and Immunotherapy of Lung Squamous Cell Carcinoma

BACKGROUND

Methylation of cytosine residues resulting in 5-methylcytosine (5-mC) is an important epigenetic modification associated with tumorigenesis. The present study explored the relationship between methylation, prognosis, and immunotherapy of patients suffering from lung squamous cell carcinoma (LUSC).

METHODS

RNA sequencing data and corresponding clinical information were downloaded, and preprocessed, and unsupervised consistent cluster analysis was used to identify 5-mC-related clusters and gene clusters. 5-mC scores were calculated using principal component analysis, and a Boruta algorithm was used to evaluate the relationship between tumor mutation burden (TMB), immune checkpoint inhibitor response, and prognosis of individual LUSC patients.

RESULTS

: Two 5-mC clusters and three gene clusters with different prognoses were identified. Patients with higher 5-mC scores showed worse prognoses, which was confirmed in multiple cohorts. Some immune-related biological functions and pathways were enriched in the high-5-mC score subtype.

CONCLUSION

The 5-mC score is a potential biomarker independent of TMB, which can be a decisive factor regarding immune treatment responses. Further, patients with low 5-mC scores may respond better to immunotherapy. The 5-mC score can thus be used as a potential biomarker for the prognosis of LUSC patients and their response to immunotherapy.

m5C Regulator-mediated methylation modification clusters contribute to the immune microenvironment regulation of multiple myeloma

Background: Multiple myeloma (MM) is a hematological malignancy in which plasma cells proliferate abnormally. 5-methylcytosine (m5C) methylation modification is the primary epigenetic modification and is involved in regulating the occurrence, development, invasion, and metastasis of various tumors; however, its immunological functions have not been systematically described in MM. Thus, this study aimed to clarify the significance of m5C modifications and how the immune microenvironment is linked to m5C methylation in MM.

Method: A total of 483 samples (60 healthy samples, 423 MM samples) from the Gene Expression Omnibus dataset were acquired to assess the expression of m5C regulators. A nomogram model was established to predict the occurrence of MM. We investigated the impact of m5C modification on immune microenvironment characteristics, such as the infiltration of immunocytes and immune response reactions. We then systematically evaluated three different m5C expression patterns to assess immune characteristics and metabolic functional pathways and established m5C-related differentially expressed genes (DEGs). In addition, biological process analysis was performed and an m5C score was constructed to identify potentially significant immunological functions in MM.

Result: Differential expressions of m5C regulators were identified between healthy and MM samples. The nomogram revealed that m5C regulators could predict higher disease occurrence of MM. We identified three distinct m5C clusters with unique immunological and metabolic characteristics. Among the three different m5C clusters, cluster C had more immune characteristics and more metabolism-related pathways than clusters A and B. We analyzed 256 m5C-related DEGs and classified the samples into three different m5C gene clusters. Based on the m5C and m5C gene clusters, we calculated m5C scores and classified each patient into high- and low-m5C score groups.

Conclusion: Our study demonstrated that m5C modification is involved in and contributes to the diversity and complexity of the immune microenvironment, which offers promise for the development of accurate therapeutic strategies.

Robust pyroptosis risk score guides the treatment options and predicts the prognosis of bladder carcinoma

Background

Bladder carcinoma (BLCA) is a heterogeneous disease that makes it difficult to achieve proper individual treatment and predict prognosis. This study aimed to develop a risk score from a new perspective of pyroptosis and guide accurate treatment and prognosis prediction for BLCA.

Methods

The TCGA-BLCA cohort data were downloaded from The Cancer Genome Atlas database. Two external validation cohorts were collected from the Gene Expression Omnibus. Another independent validation cohort (the Xiangya cohort) was recruited from our hospital. The least absolute shrinkage and selector operation (LASSO) algorithm and Cox regression models were used to establish the pyroptosis risk score. Thereafter, we correlated the pyroptosis risk score with prognosis, tumor microenvironment (TME) immune hallmarks, and multiple treatments, including anticancer immunotherapy, chemotherapy, radiotherapy, and targeted therapy.

Results

The pyroptosis risk score was an independent prognostic predictor of BLCA. We found that the activities of multiple steps of the anticancer immune response cycle, such as the release of cancer cell antigens, CD8 T cell recruitment, and NK cell recruitment, were significantly higher in the high-risk score group than in the low-risk score group. In addition, the infiltration levels of the corresponding tumor-infiltrating immune cells (TIICs), such as CD8 T cells and NK cells, were positively correlated with the pyroptosis risk score. Thus, BLCA with a high-risk score may be associated with inflamed phenotypes. Simultaneously, the expression of multiple immune checkpoints (such as PD-L1, CTLA-4, and PD-1) and enrichment scores of gene signatures positively correlated with immunotherapy response were positively correlated with the pyroptosis risk score. Therefore, patients with a high pyroptosis risk score may be more sensitive to immunotherapy. In addition, patients with high pyroptosis risk scores may be more sensitive to chemotherapeutic drugs, such as cisplatin, docetaxel, and paclitaxel. In addition, the pyroptosis risk score accurately predicted the molecular subtypes of BLCA, which were cross-validated in several independent systems.

Conclusions

This study developed and validated a robust pyroptosis risk score that can predict the clinical outcomes and TME immune phenotypes of BLCA. In summary, the pyroptosis risk score helps drive precision therapy in patients with BLCA.

TRPV1 is a potential biomarker for the prediction and treatment of multiple cancers based on a pan-cancer analysis

BACKGROUND

Transient receptor potential cation channel subfamily V member 1 (TRPV1) was considered to play pivotal roles in multiple cancers; however, the expression and clinical significance of the TRPV1 remain unclear, which were explored in this study.

RESULTS

The pan-cancer analysis was performed based on 10,236 samples in 32 cancers. Differential TRPV1 expression levels were detected in 12 cancers (p < 0.05). TRPV1 demonstrated its conspicuous prognosis significance and prediction effects for some cancers (e.g., lung adenocarcinoma), indicating its potential as a valuable and novel biomarker in treating and predicting cancers. TRPV1 expression was relevant to DNA methyltransferases, mismatch repair genes, tumor mutational burden, and microsatellite instability. TRPV1 expression was associated with the immune microenvironment of some cancers, and its roles in different cancers may be mediated by affecting various immune cells. Gene set enrichment analysis discloses the significant relevance of TRPV1 expression with a series of metabolic and immunoregulatory-related pathways.

CONCLUSIONS

This study provided a comprehensive workflow of the expression, clinical significance, and underlying mechanisms of TRPV1 in pan-cancer. TRPV1 may be an underlying biomarker for predicting and treating multiple cancer.

Comprehensive Analysis of 5-Methylcytosine (m5C) Regulators and the Immune Microenvironment in Pancreatic Adenocarcinoma to Aid Immunotherapy

Background

Pancreatic adenocarcinoma (PAAD) is one of the most malignant cancers and has a poor prognosis. As a critical RNA modification, 5-methylcytosine (m⁵C) has been reported to regulate tumor progression, including PAAD progression. However, a comprehensive analysis of m⁵C regulators in PAAD is lacking.

Methods

In the present study, PAAD datasets were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and ArrayExpress databases. The expression pattern of m⁵C regulators were analyzed and patients were divided into different m⁵C clusters according to consensus clustering based on m⁵C regulators. Additionally, m⁵C differentially expressed genes (DEGs) were determined using Limma package. Based on m⁵C DEGs, patients were divided into m⁵C gene clusters. Moreover, m⁵C gene signatures were derived from m⁵C DEGs and a quantitative indicator, the m⁵C score, was developed from the m⁵C gene signatures.

Results

Our study showed that m⁵C regulators were differentially expressed in patients with PAAD. The m⁵C clusters and gene clusters based on m⁵C regulators and m⁵C DEGs were related to immune cell infiltration, immune-related genes and patient survival status, indicating that m⁵C modification play a central role in regulating PAAD development partly by modulating immune microenvironment. Additionally, a quantitative indicator, the m⁵C score, was also developed and was related to a series of immune-related indicators. Moreover, the m⁵C score precisely predicted the immunotherapy response and prognosis of patients with PAAD.

Conclusion

In summary, we confirmed that m⁵C regulators regulate PAAD development by modulating the immune microenvironment. In addition, a quantitative indicator, the m⁵C score, was developed to predict immunotherapy response and prognosis and assisted in identifying PAAD patients suitable for tailored immunotherapy strategies.

Ogt Demonstrated Conspicuous Clinical Significance in Cancers, from Pan-Cancer to Small-Cell Lung Cancer

The clinical progression of small-cell lung cancer (SCLC) remains pessimistic. The aim of the present study was to promote the understanding of the clinical significance and mechanism of O-linked N-acetylglucosamine (GlcNAc) transferase (OGT) in SCLC. Wilcoxon tests, standardized mean difference (SMD), and Kruskal–Wallis tests were utilized to compare OGT level differences among the experimental and control groups. The univariate Cox regression analysis, Kaplan–Meier curves, and receiver operating characteristic curves were applied to determine OGT's clinical relevance in cancers. The Spearman correlation analysis and enrichment analysis were utilized to explore the underlying mechanisms of OGT in cancers. For the first time in the field, we provide an overview of OGT in 32 cancers using a large number of samples (n = 21,196), determining distinct OGT expression in 25 cancers and its prognosis effects in 12 cancers. Furthermore, using 950 samples from multiple sources, upregulated OGT was found in both mRNA and protein levels in SCLC (SMD = 0.93, 95% CI [0.24, 1.63]). Higher OGT levels represented a more unfavorable disease-free interval for SCLC patients (p < 0.001). The research also identified OGT expression as a potential marker for SCLC prediction (sensitivity = 0.79, specificity = 0.86, and AUC = 0.88). The high expression of OGT in SCLC may result from the positive regulation of two transcription factors—DEK and XRN2. We primarily investigated the underlying mechanisms of OGT in SCLC. Herein, based on the analyses from pan-cancer to SCLC, OGT demonstrated conspicuous clinical significance. OGT may be an underlying biomarker for the treatment and identification of some cancers, including SCLC.

Targeted therapies for cancer

Targeted therapy is the key for improving overall survival while decreasing the undesirable adverse effects of cancer treatment. Patients who received matched targeted therapies showed dramatically improved overall survival (OS) and progression-free survival (PFS) compared to those without matched therapies. However, each patient responds to targeted therapy differently due to their unique genomic profile. The discrepancy of treatment response between clinical trials and real-world clinical practice highlights an unmet need to develop tailored therapies for individual patients. The development of cutting-edge technologies, such as next-generation sequencing, has enabled us to identify more actionable targets. In this special issue of BMC Medicine, a collection of highly translational and clinical oncology papers presented a series of studies on targeted therapies for a variety of cancer types, aiming to bridge the gap between genomic testing and precision medicine and spark innovations on improving the efficacy of targeted therapies.

Identification of the Expression Patterns and Potential Prognostic Role of 5-Methylcytosine Regulators in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer with a poor prognosis. 5-methylcytosine (m5C) modification plays a nonnegligible role in tumor pathogenesis and progression. However, little is known about the role of m5C regulators in HCC.

Methods: Based on 9 m5C regulators, the m5C modification patterns of HCC samples extracted from public databases were systematically evaluated and correlated with tumor immune and prognosis characteristics. An integrated model called the “m5Cscore” was constructed using principal component analysis, and its prognostic value was evaluated.

Results: Almost all m5C regulators were differentially expressed between HCC and normal tissues. Through unsupervised clustering, three different m5Cclusters were ultimately uncovered; these clusters were characterized by differences in prognosis, immune cell infiltration, and pathway signatures. The m5Cscore was constructed to quantify the m5C modifications of individual patients. Subsequent analysis revealed that the m5Cscore was an independent prognostic factor of HCC and could be a novel indicator to predict the prognosis of HCC.

Conclusion: This study comprehensively explored and systematically profiled the features of m5C modification in HCC. m5C modification patterns play a crucial role in the tumor immune microenvironment (TIME) and prognosis of HCC. The m5Cscore provides a more holistic understanding of m5C modification in HCC and provides a practical tool for predicting the prognosis of HCC. This study will help clinicians identify effective indicators of HCC to improve the poor prognosis of this disease.