Identification of a glutamine metabolism reprogramming signature for predicting prognosis, immunotherapy efficacy, and drug candidates in bladder cancer

Identification of an Amino Acid Metabolism Reprogramming Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Colon Cancer

Colon cancer ranked third among the most frequently diagnosed cancers worldwide. Amino acid metabolic reprogramming was related to the occurrence and development of colon cancer. We looked for the amino acid metabolism genes (AMGs) associated with amino acid metabolism from molecular signatures database as prognostic markers and constructed amino acid metabolism scoring model (AMS). According to AMS, the patients were divided into high AMS and low AMS groups, and the prognostic characteristics, molecular phenotypes, somatic cell mutation characteristics, immune cell infiltration characteristics, and immunotherapy effect of the two groups were systematically analyzed. Finally, the compounds targeting AMGs were also screened. We screen out 6 prognostic AMGs (P < 0.05) and construct an AMS model based on them. K-M curve indicated that OS in low AMS group was significantly higher than that in high group (P < 0.05), which were validated in multiple datasets. And different AMS groups had different molecular phenotypes, somatic cell mutation characteristics and immune cell infiltration characteristics. Low AMS group had a better effect for immunotherapy. In addition, we predicted potential therapeutic compounds that could bind to AMGs target proteins. AMS model can be used as a hierarchical tool to evaluate the prognosis, immune infiltration characteristics and immunotherapy response ability of colon cancer. And the compounds screened based on AMGs may become new anti-tumor drugs.

Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis

Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. But the effective biomarkers for HCC metastasis remain underexplored. Here we integrated GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets to screen candidate genes for hepatocellular carcinoma metastasis, a consensus metastasis-derived prognostic signature (MDPS) was constructed by machine learning. Based on the risk scores, HCC patients were stratified into high-risk and low-risk groups. Comprehensive analyses were conducted to investigate various aspects including survival outcomes, clinical characteristics, immune cell infiltration, as well as in vitro experiments. Together, we develop a comprehensive machine learning-based program for constructing a consensus MDPS including four genes (SPP1, TYMS, HMMR and MYCN). Our findings revealed that four genes could serve as efficient prognostic biomarkers and therapeutic targets in HCC. In addition, in vitro experiments showed that HMMR overregulation exacerbated tumor progression, including proliferation, migration and invasion.

A Machine Learning Computational Framework Develops a Multiple Programmed Cell Death Index for Improving Clinical Outcomes in Bladder Cancer

Comprehensive action patterns of programmed cell death (PCD) in bladder cancer (BLCA) have not yet been thoroughly investigated. Here, we collected 19 different PCD patterns, including 1911 PCD-related genes, and developed a multiple programmed cell death index (MPCDI) based on a machine learning computational framework. We found that in the TCGA-BLCA training cohort and the independently validated GSE13507 cohort, the patients with high-MPCDI had a worse prognosis, whereas patients with low-MPCDI had a better prognosis. By combining clinical characteristics with the MPCDI, we constructed a nomogram. The C-index demonstrated that the nomogram was significantly more accurate compared to other variables, including MPCDI, age, gender, and clinical stage. The results of the decision curve analysis demonstrated that the nomogram had a better net clinical benefit compared to other clinical variables. Subsequently, we revealed the heterogeneity of BLCA patients, with significant differences in terms of overall immune infiltration abundance, immunotherapeutic response, and drug sensitivity in the two MPCDI groups. Encouragingly, the high-MPCDI patients showed better efficacy for commonly used chemotherapeutic drugs than the low-MPCDI patients, which suggests that MPCDI scores have a guiding role in chemotherapy for BLCA patients. In conclusion, the MPCDI developed and verified in this study is not only an emerging clinical classifier for BLCA patients, but it also serves as a reliable forecaster for both chemotherapy and immunotherapy, which can guide clinical management and clinical decision-making for BLCA patients.

Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a malignancy affecting the respiratory system. Most patients are diagnosed with advanced or metastatic lung cancer due to the fact that most of their clinical symptoms are insidious, resulting in a bleak prognosis. Given that abnormal reprogramming of asparagine metabolism (AM) has emerged as an emerging therapeutic target for anti-tumor therapy. However, the clinical significance of abnormal reprogramming of AM in LUAD patients is unclear. In this study, we collected 864 asparagine metabolism-related genes (AMGs) and used a machine-learning computational framework to develop an asparagine metabolism immunity index (AMII) for LUAD patients. Through the utilization of median AMII scores, LUAD patients were segregated into either a low-AMII group or a high-AMII group. We observed outstanding performance of AMII in predicting survival prognosis in LUAD patients in the TCGA-LUAD cohort and in three externally independently validated GEO cohorts (GSE72094, GSE37745, and GSE30219), and poorer prognosis for LUAD patients in the high-AMII group. The results of univariate and multivariate analyses showed that AMII can be used as an independent risk factor for LUAD patients. In addition, the results of C-index analysis and decision analysis showed that AMII-based nomograms had a robust performance in terms of accuracy of prognostic prediction and net clinical benefit in patients with LUAD. Excitingly, LUAD patients in the low-AMII group were more sensitive to commonly used chemotherapeutic drugs. Consequently, AMII is expected to be a novel diagnostic tool for clinical classification, providing valuable insights for clinical decision-making and personalized management of LUAD patients.

The deubiquitinating enzymes-related signature predicts the prognosis and immunotherapy response in breast cancer

BACKGROUND

Breast cancer is a prevalent disease that has a dismal prognosis for patients and a bad outlook for treatments. Ubiquitination is a reversible biological process that regulates protein production and degradation, as well as plays a vital role in protein transport, localization, and biological activity.

METHODS

We obtained the breast cancer patient sample data and used a machine learning technique to create a novel index called Deubiquitinating enzyme related index (DUBRI) by gathering genes associated to deubiquitinating enzymes. Based on DUBRI, we systematically analyze patients' prognosis, clinical characteristics, tumor immune microenvironment, chemotherapy response and immunotherapy response. Finally, the function of OTUB2 was explored in breast cancer cells.

RESULTS

DUBRI, which consists of five deubiquitinating enzyme genes (OTUB2, USP41, MINDY2, YOD1, and PSMD7), is a reliable predictor of survival in breast cancer patients. We found that the high DUBRI group presented higher levels of immune cell infiltration. We performed molecular docking prediction of core target proteins in deubiquitinating enzymes. In vitro experiments verified that knockdown of OTUB2 could inhibit the proliferation and migration of breast cancer.

CONCLUSIONS

The DUBRI discovered in this research may effectively evaluate the outlook of breast cancer patients and identify groups of patients who would gain advantages from immunotherapy, offering vital knowledge for the future targeted treatment of breast cancer patients.

Deciphering glutamine metabolism patterns for malignancy and tumor microenvironment in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer characterized by metabolic reprogramming. Glutamine metabolism is pivotal in metabolic reprogramming, contributing to the significant heterogeneity observed in ccRCC. Consequently, developing prognostic markers associated with glutamine metabolism could enhance personalized treatment strategies for ccRCC patients. This study obtained RNA sequencing and clinical data from 763 ccRCC cases sourced from multiple databases. Consensus clustering of 74 glutamine metabolism related genes (GMRGs)- profiles stratified the patients into three clusters, each of which exhibited distinct prognosis, tumor microenvironment, and biological characteristics. Then, six genes (SMTNL2, MIOX, TMEM27, SLC16A12, HRH2, and SAA1) were identified by machine-learning algorithms to develop a predictive signature related to glutamine metabolism, termed as GMRScore. The GMRScore showed significant differences in clinical prognosis, expression profile of immune checkpoints, abundance of immune cells, and immunotherapy response of ccRCC patients. Besides, the nomogram incorporating the GMRScore and clinical features showed strong predictive performance in prognosis of ccRCC patients. ALDH18A1, one of the GRMGs, exhibited elevated expression level in ccRCC and was related to markedly poorer prognosis in the integrated cohort, validated by proteomic profiling of 232 ccRCC samples from Fudan University Shanghai Cancer Center (FUSCC). Conducting western blotting, CCK-8, transwell, and flow cytometry assays, we found the knockdown of ALDH18A1 in ccRCC significantly promoted apoptosis and inhibited proliferation, invasion, and epithelial-mesenchymal transition (EMT) in two human ccRCC cell lines (786-O and 769-P). In conclusion, we developed a glutamine metabolism-related prognostic signature in ccRCC, which is tightly linked to the tumor immune microenvironment and immunotherapy response, potentially facilitating precision therapy for ccRCC patients. Additionally, this study revealed the key role of ALDH18A1 in promoting ccRCC progression for the first time.

Implications of c-Myc in the pathogenesis and treatment efficacy of urological cancers

Urological cancers, including prostate, bladder, and renal cancers, are significant causes of death and negatively impact the quality of life for patients. The development and progression of these cancers are linked to the dysregulation of molecular pathways. c-Myc, recognized as an oncogene, exhibits abnormal levels in various types of tumors, and current evidence supports the therapeutic targeting of c-Myc in cancer treatment. This review aims to elucidate the role of c-Myc in driving the progression of urological cancers. c-Myc functions to enhance tumorigenesis and has been documented to increase growth and metastasis in prostate, bladder, and renal cancers. Furthermore, the dysregulation of c-Myc can result in a diminished response to therapy in these cancers. Non-coding RNAs, β-catenin, and XIAP are among the regulators of c-Myc in urological cancers. Targeting and suppressing c-Myc therapeutically for the treatment of these cancers has been explored. Additionally, the expression level of c-Myc may serve as a prognostic factor in clinical settings.

Pan-cancer analysis reveals potential immunological and prognostic roles of COA6 in human cancers and preliminary exploration of COA6 in bladder cancer

BACKGROUND

Cytochrome C oxidase assembly factor 6 (COA6) is significantly involved in the progression of cancer and is aberrantly expressed in disease. Nevertheless, the comprehensive analysis of COA6 using many omics techniques, and its impact on the prognosis and immunological microenvironment of cancer patients, remains unexplored.

METHODS

We gathered data from 33 cancer cases and conducted a thorough analysis of abnormalities in COA6 gene expression. This analysis included examining its relevance to disease, its diagnostic and prognostic value, pathway enrichment, the immune microenvironment, its association with immune checkpoints, and its ability to predict patient response to immunotherapy and natural small molecule drugs that target the COA6 protein. Ultimately, we examined the function of COA6 in bladder cancer by in vitro research.

RESULTS

Our study revealed significant variations in gene expression and identified COA6 as a potential diagnostic biomarker for cancer. COA6 was also discovered to have a crucial function in pan-cancer involving the tumor microenvironment. COA6 has a strong correlation with well-known immunological checkpoints, including TMB and MSI. Molecular docking identified natural small chemical inhibitors that specifically target the COA6 protein. Ultimately, scientific evidence has verified that suppressing the expression of the COA6 gene hinders the growth and infiltration of bladder cancer cells.

CONCLUSIONS

Our study emphasizes the significant potential of COA6 as a predictive and immunotherapeutic response biomarker. This finding may lead to future investigation into the mechanism of tumor infiltration and the therapeutic possibilities of COA6 in cancer.

Integration of multi-omics and clinical treatment data reveals bladder cancer therapeutic vulnerability gene combinations and prognostic risks

Background

Bladder cancer (BCa) is a common malignancy of the urinary tract. Due to the high heterogeneity of BCa, patients have poor prognosis and treatment outcomes. Immunotherapy has changed the clinical treatment landscape for many advanced malignancies, opening new avenues for the precise treatment of malignancies. However, effective predictors and models to guide clinical treatment and predict immunotherapeutic outcomes are still lacking.

Methods

We downloaded BCa sample data from The Cancer Genome Atlas to identify anti-PD-L1 immunotherapy-related genes through an immunotherapy dataset and used machine learning algorithms to build a new PD-L1 multidimensional regulatory index (PMRI) based on these genes. PMRI-related column-line graphs were constructed to provide quantitative tools for clinical practice. We analyzed the clinical characteristics, tumor immune microenvironment, chemotherapy response, and immunotherapy response of patients based on PMRI system. Further, we performed function validation of classical PMRI genes and their correlation with PD-L1 in BCa cells and screening of potential small-molecule drugs targeting PMRI core target proteins through molecular docking.

Results

PMRI, which consists of four anti-PD-L1 immunotherapy-associated genes (IGF2BP3, P4HB, RAC3, and CLK2), is a reliable predictor of survival in patients with BCa and has been validated using multiple external datasets. We found higher levels of immune cell infiltration and better responses to immunotherapy and cisplatin chemotherapy in the high PMRI group than in the low PMRI group, which can also be used to predict immune efficacy in a variety of solid tumors other than BCa. Knockdown of IGF2BP3 inhibited BCa cell proliferation and migration, and IGF2BP3 was positively correlated with PD-L1 expression. We performed molecular docking prediction for each of the core proteins comprising PMRI and identified 16 small-molecule drugs with the highest affinity to the target proteins.

Conclusions

Our PD-L1 multidimensional expression regulation model based on anti-PD-L1 immunotherapy-related genes can accurately assess the prognosis of patients with BCa and identify patient populations that will benefit from immunotherapy, providing a new tool for the clinical management of intermediate and advanced BCa.

Roles of non-coding RNAs in the metabolism and pathogenesis of bladder cancer

Bladder cancer (BC) is featured as the second most common malignancy of the urinary tract worldwide with few treatments leading to high incidence and mortality. It stayed a virtually intractable disease, and efforts to identify innovative and effective therapies are urgently needed. At present, more and more evidence shows the importance of non-coding RNA (ncRNA) for disease-related study, diagnosis, and treatment of diverse types of malignancies. Recent evidence suggests that dysregulated functions of ncRNAs are closely associated with the pathogenesis of numerous cancers including BC. The detailed mechanisms underlying the dysregulated role of ncRNAs in cancer progression are still not fully understood. This review mainly summarizes recent findings on regulatory mechanisms of the ncRNAs, long non-coding RNAs, microRNAs, and circular RNAs, in cancer progression or suppression and focuses on the predictive values of ncRNAs-related signatures in BC clinical outcomes. A deeper understanding of the ncRNA interactive network could be compelling framework for developing biomarker-guided clinical trials.