Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization.

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Single-cell dissection reveals the role of aggrephagy patterns in tumor microenvironment components aiding predicting prognosis and immunotherapy on lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the leading malignant cancers. Aggrephagy plays a critical role in key genetic events for various cancers; yet, how aggrephagy functions within the tumor microenvironment (TME) in LUAD remains to be elucidated. METHODS: In this study, by sequential non-negative matrix factorization (NMF) algorithm, pseudotime analysis, cell-cell interaction analysis, and SCENIC analysis, we have shown that aggrephagy genes demonstrated various patterns among different cell types in LUAD TME. LUAD and Immunotherapy cohorts from public repository were used to determine the prognosis and immune response of aggrephagy TME subtypes. The aggrephagy-deprived prognostic score (ADPS) was quantified based on machine learning algorithms. RESULTS: The cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and CD8+ T cells have various aggrephagy patterns, which enhance the intensity of intercellular communication and transcription factor activation. Furthermore, based on the signatures of the newly defined aggrephagy cell subtypes and expression profiles of large cohorts in LUAD patients, we determine that DYNC1I2+CAF-C1, DYNLL1+CAF-C2, PARK7+CAF-C3, VIM+Mac-C1, PARK7+Mac-C2, VIM+CD8+T_cells-C1, UBA52+CD8+T_cells-C2, TUBA4A+CD8+T_ cells-C3, and TUBA1A+CD8+T_cells-C4 are crucial prognostic factors for LUAD patients. The developed ADPS could predict survival outcomes and immunotherapeutic response across ten cohorts (n = 1838), and patients with low ADPS owned a better prognosis, lower genomic alterations, and are more sensitive to immunotherapy. Meanwhile, based on PRISM, CTRP, and CMAP databases, PLK inhibitor BI-2536, may be a potential agent for patients with high ADPS. CONCLUSIONS: Taken together, our novel and systematic single-cell analysis has revealed the unique role of aggrephagy in remodeling the TME of LUAD. As a newly demonstrated biomarker, the ADPS facilitates the clinical management and individualized treatment of LUAD.

Characterization of cuproptosis-related lncRNA landscape for predicting the prognosis and aiding immunotherapy in lung adenocarcinoma patients.

Cuproptosis is a newly discovered mechanism of regulated cell death, which serves as a novel target for cancer therapy. Long non-coding RNAs (lncRNAs) play an important role in the initiation and progression of cancer cells; however, the relationship between cuproptosis and lncRNAs in tumorigenesis and cancer treatment has not been well established in lung adenocarcinoma (LUAD). Thus, it is important to clarify and characterize the cuproptosis-related lncRNA landscape in LUAD. In this study, cuproptosis-related lncRNAs was screened by Pearson correlation analysis. Then, univariate, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression were conducted to identify 6 cuproptosis-related lncRNAs (AC090541.1, AC009226.1, NIFK-AS1, AC027097.2, AC026355.2, and AC106028.2) which were used to construct a cuproptosis-related lncRNA signature (CRLS). Multi-dimensional assessments including Kaplan-Meier analysis, receiver operating characteristics (ROC) curves, and principal component analysis (PCA) verified that the CRLS could reliably predict the prognosis and survival of LUAD patients. We further compared the immune cell infiltration, somatic mutation landscape, and functional enrichment pathways between the high and low CRLS groups. Patients with low CRLS scores had prolonged survival and were sensitive to immunotherapy, whereas patients with high CRLS scores might benefit better from chemotherapy. We further analyzed the individualized immunotherapeutic strategies and the candidate compounds for the potential clinical treatment. Moreover, the expression level of these 6 lncRNAs was examined experimentally in vitro by using quantitative real-time polymerase chain reaction (RT-qPCR). Additionally, one of the significantly differentially expressed lncRNAs, NIFK-AS1, was confirmed to suppress the proliferation and migration of LUAD by Cell Counting Kit-8 Assays (CCK-8), wound healing assay, and colony formation assays. Taken together, we established a CRLS that might be a promising tool for predicting the prognosis, guiding individualized treatment, and serving as a promising therapeutic target for patients with LUAD.

Cuproptosis associated genes affect prognosis and tumor microenvironment infiltration characterization in lung adenocarcinoma.

Cuproptosis, a newly discovered mechanism of programmed cell death, is important for detailing the metabolic aspects of cancer progression and thereby guiding cancer therapy. An exciting era of translational medicine has led to the rapid development of countless immunotherapeutic strategies. The existing successful cancer immunotherapies have sparked new hope for patients with solid and hematologic malignancies. Hence, it is important to characterize the link between the cuproptosis process and the immunity status in the tumor microenvironment (TME) in Lung Adenocarcinoma (LUAD), which may be able to predict patient's prognosis. In this study, we systematically assessed 10 cuproptosis-associated genes (CAGs) and comprehensively characterized the relationship between cuproptosis and the molecular characteristics and immune cell infiltration of tumor tissue, prognosis and clinical treatment of patients. Subsequently, the CAG_score for predicting overall survival (OS) was established and its reliable predictive ability in LUAD patients was confirmed. Next, we created a highly reliable nomogram to facilitate the clinical viability of the CAG_score. The low CAG_score group, with lower immune cell infiltration, and mutation burden, had a significantly superior OS, which was associated with a better response to immunotherapy. The present study revealed that cuproptosis play a significant role in TME regulation in LUAD. Collectively, we identified a prognostic CAGs-related signature for LUAD patients. This signature may contribute to clarifying the characteristics of TME and enable the exploration of more potent immunotherapy strategies.

Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma.

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in SKCM at present. In this study, data on Skin Cutaneous Melanoma (SKCM) patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied Univariate/multivariate and LASSO Cox regression algorithms, and finally identified 5 cuproptosis-related lncRNAs for constructing prognosis prediction models (VIM-AS1, AC012443.2, MALINC1, AL354696.2, HSD11B1-AS1). The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of SKCM patients. Next, immune microenvironment, immunotherapy analysis, and functional enrichment analysis were also performed. In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in SKCM and aims to open up new directions for SKCM therapy.

A novel glycosyltransferase-related lncRNA signature correlates with lung adenocarcinoma prognosis.

Background: Lung adenocarcinoma (LUAD) is one of the most fatal cancers in the world. Previous studies have shown the increase in glycosylation level, and abnormal expressions of related enzymes are closely related to various cancers. Long non-coding RNAs (lncRNAs) play an important role in the proliferation, metabolism, and migration of cancer cells, but the underlying role of glycosyltransferase (GT)-related lncRNAs in LUAD remains to be elucidated. Methods: We abstracted 14,056 lncRNAs from The Cancer Genome Atlas (TCGA) dataset and 257 GT-related genes from the Gene Set Enrichment Analysis (GSEA) database. Univariate, LASSO-penalized, and multivariate Cox regression analyses were conducted to construct a GT-related lncRNA prognosis model. Results: A total of 2,726 GT-related lncRNAs were identified through Pearson's correlation analysis, and eight of them were utilized to construct a GT-related lncRNA model. The overall survival (OS) of the low-risk group continued to be superior to that of the high-risk group according to the subgroups classified by clinical features. The risk model was proved to have independent prognostic characteristics for LUAD by univariate and multivariate Cox regression analyses. The status of the tumor immune microenvironment and the relevant immunotherapy response was significantly different between the two risk groups. The candidate drugs aimed at LUAD subtype differentiation were identified. Conclusion: We constructed a risk model comprising eight GT-related lncRNAs which was identified as an independent predictor of prognoses to predict patient survival and guide-related treatments for patients with LUAD.

Molecular characteristics, clinical significance, and cancer immune interactions of cuproptosis and ferroptosis-associated genes in colorectal cancer.

Objective: To systematically analyze the expression of cuproptosis and ferroptosis genes and their impact on the development, prognosis, tumor microenvironment (TME), and treatment response in colorectal cancer (CRC) patients. Methods: We systematically evaluated 33 cuproptosis and ferroptosis-related genes and comprehensively identified the correlations between cuproptosis and ferroptosis-related genes and transcriptional patterns, prognosis, and clinical features. Three distinct subgroups were identified in CRC using the TCGA database and the GEO database. We next assessed the relationship between the molecular features, prognostic significance, and clinical indicators of the prognostic genes in the cuproptosis and ferroptosis-related gene clusters. In addition, a PAC_score, which accurately predicted the prognosis of CRC patients and the efficacy of immunomodulatory mAbs, was obtained. Results: Patients in the low expression group (low expression of cuproptosis and ferroptosis-related genes) had a longer survival compared to the high expression group. We identified two distinct prognosis-associated molecular subtypes and observed an association between clinical information and prognosis. The enrichment analysis of differential genes associated with prognosis showed that the main enrichment was related to biological processes such as metastasis and metabolism. Next, the PCA_score for predicting overall survival (OS) was established and its reliable predictive value in CRC patients was confirmed. Furthermore, highly reliable nomogram was created to facilitate the clinical feasibility of the PCA_score. It was found that the immunomodulatory mAbs, PD-L1 and CTLA4 were highly expressed in the low PCA_score score group with statistically significance. Conclusion: Overall, the PCA scores of prognostic differential genes in the cuproptosis and ferroptosis-related gene clusters were strongly associated with clinical characteristics, prognosis, and immunotherapy in CRC patients. This data may promote further exploration of more effective immunotherapy strategies for CRC.

Oxidative Stress-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Responses in Patients With LUAD.

Lung adenocarcinoma is increasingly harmful to society and individuals as cancer with an inferior prognosis and insensitive to chemotherapy. Previous studies have demonstrated that oxidative stress and lncRNAs play a vital role in many biological processes. Therefore, we explored the role of lncRNAs associated with oxidative stress in the prognosis and survival of LUAD patients. We examined the expression profiles of lncRNAs and oxidative stress genes in this study. A prognosis prediction model and a nomogram were built based on oxidative stress-related lncRNAs. Functional and drug sensitivity analyses were also performed depending on oxidative stress-related lncRNA signature. Moreover, we investigated the relationship between immune response and immunotherapy. The results showed that a risk scoring model based on 16 critical oxidative stress lncRNAs was able to distinguish the clinical status of LUAD and better predict the prognosis and survival. Additionally, the model demonstrated a close correlation with the tumor immune system, and these key lncRNAs also revealed the relationship between LUAD and chemotherapeutic drug sensitivity. Our work aims to provide new perspectives and new ideas for the treatment and management of LUAD.

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients.

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.