Tumor Microenvironmental Competitive Endogenous RNA Network and Immune Cells Act as Robust Prognostic Predictor of Acute Myeloid Leukemia

Molecular Characterization of Cuproptosis-related lncRNAs: Defining Molecular Subtypes and a Prognostic Signature of Ovarian Cancer

Cuproptosis, a newly discovered form of programmed cell death, relies on mitochondrial respiration, the chain of which has been found to be altered in ovarian cancer (OC). The current work probed into the effects of Cuproptosis on the prognosis, immune microenvironment and therapeutic response of OC based on Cuproptosis-related lncRNAs. Data on OC gene expression and clinical characteristics were collected from TCGA, ICGC and GEO databases, and mRNA and lncRNA were distinguished. Cuproptosis-related lncRNAs were screened for consensus clustering analysis. Differentially expressed lncRNAs (DElncRNAs) were identified between clusters, and least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were performed to establish a prognostic signature. Its potential value in OC was evaluated by Gene Set Enrichment Analysis (GSEA), tumor cell mutation and immune microenvironment analysis, and response to immunotherapy and antineoplastic drugs. According to the classification scheme of Cuproptosis-related lncRNAs, OC was divided into four molecular subtypes, which were different in survival time, immune characteristics and somatic mutation. The prognostic signature between subtypes included 10 lncRNAs, which were significantly correlated with the prognosis, immune microenvironment related indexes, the expression of immune checkpoint molecules and the sensitivity of antineoplastic drug Paclitaxel and Gefitinib of OC. We examined the expression of ten LncRNAs in OC cell lines and found that LINC00189, ZFHX4-AS1, RPS6KA2-IT1 and C9orf106 were expressed elevated in OC cell lines, and LINC00861, LINC00582, DEPDC1-AS1, LINC01556, LEMD1-AS1, TYMSOS expression was decreased in OC cell lines. The results of CCK8 showed that the cell viability of OC cells decreased after inhibition of C9orf106, whereas the cell viability of OC cells increased after inhibition of LEMD1-AS1. This work revealed new Cuproptosis-related lncRNA molecular subtypes exhibiting tumor microenvironment (TME) heterogeneity for OC and proposed a prognostic signature that may have benefits in understanding the prognosis, pathological features and immune microenvironment of OC patients.

Utilizing exosomes as sparking clinical biomarkers and therapeutic response in acute myeloid leukemia

Acute myeloid leukemia (AML) is a malignant clonal tumor originating from immature myeloid hematopoietic cells in the bone marrow with rapid progression and poor prognosis. Therefore, an in-depth exploration of the pathogenesis of AML can provide new ideas for the treatment of AML. In recent years, it has been found that exosomes play an important role in the pathogenesis of AML. Exosomes are membrane-bound extracellular vesicles (EVs) that transfer signaling molecules and have attracted a large amount of attention, which are key mediators of intercellular communication. Extracellular vesicles not only affect AML cells and normal hematopoietic cells but also have an impact on the bone marrow microenvironment and immune escape, thereby promoting the progression of AML and leading to refractory relapse. It is worth noting that exosomes and the various molecules they contain are expected to become the new markers for disease monitoring and prognosis of AML, and may also function as drug carriers and vaccines to enhance the treatment of leukemia. In this review, we mainly summarize to reveal the role of exosomes in AML pathogenesis, which helps us elucidate the application of exosomes in AML diagnosis and treatment.

A comprehensive prognostic and immunological analysis of telomere-related lncRNAs in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is one of the most prevalent malignant tumors of the urinary system, with a high recurrence and metastasis rate. Telomeres and long non-coding RNAs (lncRNAs) have been documented playing critical roles in cancer progression. However, the prognostic significance of telomere-related lncRNA (TRLs) in KIRC is less well-defined. The Cancer Genome Atlas database was applied to retrieve the expression profiles and corresponding clinical information of KIRC patients. To create the TRLs prognostic signature, univariate Cox regression, least absolute shrinkage and selection operator analyses were performed. The prognostic signature, comprised of nine prognostic TRLs, was developed and demonstrated superior prognostic ability for KIRC patients. Additionally, the risk score acted as an independent prognostic indicator. A nomogram incorporating age, grade, stage, and signature-based risk scores was also developed and exhibited excellent predictive accuracy. Several immune activities were associated with the signature, as determined by gene function analysis. Further analysis revealed differences in the status of immunity and the tumor microenvironment between low- and high-risk groups. Notably, KIRC patients with high-risk scores were more responsive to immunotherapy and chemotherapy. To summarize, our study developed a new prognostic signature consisting of nine telomere-related lncRNA that can precisely predict the prognosis of KIRC patients. The signature was shown to be of substantial value for the tumor microenvironment and tumor mutation burden, thereby contributing to a framework for the individualized treatment of patients.

Identification of MIR600HG/hsa-miR-342-3p/ANLN network as a potential prognosis biomarker associated with lmmune infiltrates in pancreatic cancer

Pancreatic cancer is one of the tumors with the worst prognosis, causing serious harm to human health. The RNA network and immune response play an important role in tumor progression. While a systematic RNA network linked to the tumor immune response remains to be further explored in pancreatic cancer. Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, the MIR600HG/hsa-miR-342-3p/ANLN network was determined. WB and IHC were used to confirm the high expression of ANLN in pancreatic cancer. The prognostic model based on the RNA network could effectively predict the survival prognosis of patients. The analysis of immune infiltration showed that the MIR600HG/hsa-miR-342-3p/ANLN network altered the level of infiltration of T helper 2 (Th2) and effector memory T (Tem) cells. Furthermore, we found that the chemokines chemokine ligand (CCL) 5 and CCL14 may play a key role in immune cell infiltration mediated by the RNA network. In conclusion, this study constructed a prognostic model based on the MIR600HG/hsa-miR-342-3p/ANLN network and found that it may function in tumor immunity.

MiR-148a-3p Promotes Colorectal Cancer Cell Ferroptosis by Targeting SLC7A11

Ferroptosis, an iron-dependent form of cell death, and dysregulated microRNA (miRNA) expression correlate with colorectal cancer (CRC) development and progression. The tumor suppressor ability of miR-148a-3p has been reported for several cancers. Nevertheless, the role of miR-148a-3p in CRC remains largely undetermined. Here, we aim at investigating the molecular mechanisms and regulatory targets of miR-148a-3p in the CRC cell death mechanism(s). To this end, miR-148a-3p expression was evaluated in SW480 and SW620 cells and normal colon epithelial CCD 841 CoN cells with quantitative real-time polymerase chain reaction (qRT-PCR). Data reported a reduction of miR-148a-3p expression in SW480 and SW620 cells compared to non-tumor cells (p < 0.05). Overexpression of miR-148a selectively inhibited CRC cell viability (p < 0.001), while weakly affecting normal CCD 841 CoN cell survival (p < 0.05). At the cellular level, miR-148a-3p mimics promoted apoptotic cell death via caspase-3 activation (p < 0.001), accumulation of mitochondrial reactive oxygen species (ROS) (p < 0.001), and membrane depolarization (p < 0.001). Moreover, miR-148a-3p overexpression induced lipid peroxidation (p < 0.01), GPX4 downregulation (p < 0.01), and ferroptosis (p < 0.01), as revealed by intracellular and mitochondrial iron accumulation and ACSL4/TFRC/Ferritin modulation. In addition, levels of SLC7A11 mRNA and protein, the cellular targets of miR-148a-3p predicted by bioinformatic tools, were suppressed by miR-148a-3p's overexpression. On the contrary, the downregulation of miR-148a-3p boosted SLC7A11 gene expression and suppressed ferroptosis. Together, these in vitro findings reveal that miR-148a-3p can function as a tumor suppressor in CRC by targeting SLC7A11 and activating ferroptosis, opening new perspectives for the rationale of therapeutic strategies through targeting the miR-148a-3p/SLC7A11 pathway.

The identification of N6-methyladenosine-related miRNAs predictive of hepatocellular carcinoma prognosis and immunotherapy efficacy

BACKGROUND

Hepatocellular carcinoma (HCC) has a high degree of malignancy and poor prognosis. N6-methyladenosine (m6A) modifications and microRNAs (miRNAs) play pivotal roles in tumorigenesis and development. However, the role of m6A-related miRNAs in HCC has not been clarified yet. This study aimed to identify the role of m6A-miRNAs in HCC prognosis through bioinformatics analysis.

METHODS

The clinicopathological information and RNA sequencing data of 369 HCC tumor tissues and 49 tumor-adjacent tissues were downloaded from the TCGA database. A total of 23 m6A regulators were extracted to evaluated the m6A-related miRNAs using Pearson's correlation analysis. Then, we selected prognosis-related m6A-miRNAs using a univariate Cox regression model and used the consensus cluster analysis to explore the characteristics of the m6A-miRNAs. The coefficient of the least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct a prognostic risk score model. The receiver operated characteristic (ROC) analysis was applied to evaluate the prognostic value of the signature. The biological functions of targeted genes were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Then, to validate the potential predictive value for prognosis, the miRNA expression profiles from the GSE76903 and GSE6857 were used. Single sample Gene Set Enrichment Analysis (ssGSEA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) were applied to assess the immune microenvironment of HCC. Additionally, a meta-analysis was used to verify the prognostic value of the m6A-microRNAs. RT-PCR was applied to validated the expression of miRNAs in HCC tissues. Cell viability, transwell assay and RNA m6A dot blot assays of HCC cells was applied to access the function of miR-17-5p.

RESULTS

The expression of 48 m6A-related miRNAs was identified and 17 prognostic m6A-miRNAs was discovered. The expression profile of those 17 miRNAs was divided into three clusters, and these clusters were associated with the tumor microenvironment (TME) and prognosis. The nine m6A-related miRNA signature was associated with the prognosis of HCC, the AUC of the ROC was 0.771(TCGA dataset), 0.788(GSE76903) and 0.646(GSE6857). The TME and the expression of immune checkpoint molecules were associated with the risk score. The meta-analysis also validated the prognostic value of the m6A-related miRNAs (miR182-5p (HR:1.58, 95%CI:1.04-2.40) and miR-17-5p (HR:1.58, 95%CI: 1.04-2.40)). The expression of miR-17-5p was upregulated in HCC tissues and miR-17-5p showed an oncogenic role in HCC cells.

CONCLUSION

The clinical innovation is the use of m6A-miRNAs as biomarkers for predicting prognosis regarding immunotherapy response in HCC patients.

Identification of the prognostic, diagnostic, and biological significance of the miR-148a-3p/cathepsin A axis in hepatocellular carcinoma

A comprehensive analysis of the prognostic, diagnostic, and biological significance of miR-148a-3p and cathepsin A (CTSA) in hepatocellular carcinoma (HCC) was performed using bioinformatics algorithms with The Cancer Genome Atlas (TCGA) data. miR-148a-3p and CTSA gene expression in HCC tissues and nontumor specimens was analyzed using TCGA database with R software. CTSA staining analysis was validated using the Human Protein Atlas database. Prognostic, diagnostic, gene set enrichment, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and immune infiltration analyses were implemented using the TCGA database with R software. Based on TCGA data and our cohort populations, CTSA expression was significantly elevated in HCC tissues compared with nontumor specimens. A significant negative correlation between miR-148a-3p and CTSA was observed in the TCGA data and our cohort population. Mechanistically, CTSA was a direct gene target of miR-148a-3p. Both CTSA and miR-148a-3p could serve as prognostic and diagnostic indicators in HCC. miR-148a-3p expression was significantly and negatively correlated with the StromalScore, ImmuneScore, and ESTIMATEScore in patients with liver cancer. miR-148a-3p mimic-mediated apoptosis and the inhibition of HCC cell growth and migration were counteracted by CTSA overexpression. The miR-148a-3p/CTSA axis was implicated in immune cell infiltration and carcinogenesis of HCC. miR-148a-3p and CTSA might be prospective molecular targets to enhance the potency of immunotherapy in HCC.

SLC11A1 associated with tumor microenvironment is a potential biomarker of prognosis and immunotherapy efficacy for colorectal cancer

Colorectal cancer (CRC) is one of the most lethal cancers of the digestive system. The tumor microenvironment (TME) plays a central role in the initiation and development of CRC. However, little is known about the modulation mechanism of the TME in CRC. In our study, we attempted to identify a biomarker related to the TME modulation that could serve as a potential prognostic biomarker for CRC. We identified differentially expressed genes between the ImmuneScore high/low and StromalScore high/low groups. Using univariate COX regression analysis and hub gene analysis (cytoHubba), SLC11A1 was identified as the only candidate gene for subsequent analysis. CIBERSORT, EPIC, MCPcounter, and immunogenic cell death were performed to evaluate the effect of SLC11A1 on the TME. We also collected samples and performed Real-time quantitative PCR to verify the expression levels of SLC11A1 in CRC and adjacent normal tissues. The IMvigor210 cohort, TIDE score, and immunophenoscore (IPS) were used to analyze the association between SLC11A1 and immunotherapy efficacy. SLC11A1 was highly expressed in CRC tissues compared with its expression in normal colorectal tissues and was associated with poor prognosis and advanced clinicopathological stages. Gene set enrichment analysis showed that TGF-β pathways, JAK-STAT pathways, and angiogenesis were significantly enriched in the high-SLC11A1 group. Single-cell analysis validated the correlation between SLC11A1 and the TME. Using CIBERSORT, EPIC, and MCPcounter algorithms, we found that there was more macrophage and fibroblast infiltration in the SLC11A1 high-expression group. Meanwhile, high-SLC11A1 patients had lower IPS scores, higher TIDE scores, and fewer immunotherapy benefits than those of low-SLC11A1 patients. In conclusion, SLC11A1 plays a crucial role in the TME and could serve as a potential biomarker for poor prognosis and immunotherapy efficacy in CRC.

Identification of a novel ceRNA network related to prognosis and immunity in HNSCC based on integrated bioinformatic investigation

Head and neck squamous cell carcinoma (HNSCC) is characterized by an immunosuppression environment and necessitates the development of new immunotherapy response predictors. The study aimed to build a prognosis-related competing endogenous RNA (ceRNA) network based on immune-related genes (IRGs) and analyze its immunological signatures. Differentially expressed IRGs were identified by bioinformatics analysis with Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and ImmPort databases. Finally, via upstream prognosis-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) prediction and co-expression analysis, we built an immune-related ceRNA network (LINC00052/hsa-miR-148a-3p/PLAU) related to HNSCC patient prognosis. CIBERSORT analysis demonstrated that there were substantial differences in 11 infiltrating immune cells in HNSCC, and PLAU was closely correlated with 10 type cells, including T cells CD8+ (R =  - 0.329), T cells follicular helper (R = - 0.342) and macrophage M0 (R = 0.278). Methylation and Tumor Immune Dysfunction and Exclusion (TIDE) analyses revealed that PLAU upregulation was most likely caused by hypomethylation and that high PLAU expression may be associated with tumor immune evasion in HNSCC, respectively.

Pan-Cancer Methylated Dysregulation of Long Non-coding RNAs Reveals Epigenetic Biomarkers

Different cancer types not only have common characteristics but also have their own characteristics respectively. The mechanism of these specific and common characteristics is still unclear. Pan-cancer analysis can help understand the similarities and differences among cancer types by systematically describing different patterns in cancers and identifying cancer-specific and cancer-common molecular biomarkers. While long non-coding RNAs (lncRNAs) are key cancer modulators, there is still a lack of pan-cancer analysis for lncRNA methylation dysregulation. In this study, we integrated lncRNA methylation, lncRNA expression and mRNA expression data to illuminate specific and common lncRNA methylation patterns in 23 cancer types. Then, we screened aberrantly methylated lncRNAs that negatively regulated lncRNA expression and mapped them to the ceRNA relationship for further validation. 29 lncRNAs were identified as diagnostic biomarkers for their corresponding cancer types, with lncRNA AC027601 was identified as a new KIRC-associated biomarker, and lncRNA ACTA2-AS1 was regarded as a carcinogenic factor of KIRP. Two lncRNAs HOXA-AS2 and AC007228 were identified as pan-cancer biomarkers. In general, the cancer-specific and cancer-common lncRNA biomarkers identified in this study may aid in cancer diagnosis and treatment.

Tumor Microenvironment in Acute Myeloid Leukemia: Adjusting Niches

Acute myeloid leukemias (AML) comprise a wide array of different entities, which have in common a rapid expansion of myeloid blast cells leading to displacement of normal hematopoietic cells and also disruption of the microenvironment in the bone marrow niches. Based on an insight into the complex cellular interactions in the bone marrow niches in non-neoplastic conditions in general, this review delineates the complex relationship between leukemic cells and reactive cells of the tumor microenvironment (TME) in AML. A special focus is directed on niche cells and various T-cell subsets as these also provide a potential therapeutic rationale considering e.g. immunomodulation. The TME of AML on the one hand plays a vital role for sustaining and promoting leukemogenesis but - on the other hand - it also has adverse effects on abnormal blasts developing into overt leukemia hindering their proliferation and potentially removing such cells. Thus, leukemic cells need to and develop strategies in order to manipulate the TME. Interference with those strategies might be of particular therapeutic potential since mechanisms of resistance related to tumor cell plasticity do not apply to it.

Deciphering the Non-Coding RNA Landscape of Pediatric Acute Myeloid Leukemia

Pediatric acute myeloid leukemia (pedAML) is a heterogeneous blood cancer that affects children. Although survival rates have significantly improved over the past few decades, 20-30% of children will succumb due to treatment-related toxicity or relapse. The molecular characterization of the leukemic stem cell, shown to be responsible for relapse, is needed to improve treatment options and survival. Recently, it has become clear that non-coding RNAs, including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), play a role in the development of human diseases, including pediatric cancer. Nevertheless, non-coding RNA expression data in pedAML are scarce. Here, we explored lncRNA (n = 30,168) and miRNA (n = 627) expression in pedAML subpopulations (leukemic stem cells (LSCs) and leukemic blasts (L-blasts)) and their normal counterparts (hematopoietic stem cells and control myeloblasts). The potential regulatory activity of differentially expressed lncRNAs in LSCs (unique or shared with the L-blast comparison) on miRNAs was assessed. Moreover, pre-ranked gene set enrichment analyses of (anti-) correlated protein-coding genes were performed to predict the functional relevance of the differentially upregulated lncRNAs in LSCs (unique or shared with the L-blast comparison). In conclusion, this study provides a catalog of non-coding RNAs with a potential role in the pathogenesis of pedAML, paving the way for further translational research studies.

Role of Circular RNAs in the Regulation of Immune Cells in Response to Cancer Therapies

Circular RNAs (CircRNAs) are a class of small endogenous noncoding RNA that are formed by means of either the spliceosome or lariat-type splicing. CircRNAs have multiple regulatory functions and have been detected in different cell types, like normal, tumor and immune cells. CircRNAs have been suggested to regulate T cell functions in response to cancer. CircRNAs can enter into T cells and promote the expression of molecules that either trigger antitumoral responses or promote suppression and the consequent evasion to the immune response. Additionally, circRNAs may promote tumor progression and resistance to anticancer treatment in different types of neoplasias. In this minireview we discuss the impact of circRNAs and its function in the regulation of the T-cells in immune response caused by cancer therapies.

CYP1B1-AS1 Is a Novel Biomarker in Glioblastoma by Comprehensive Analysis

Objective

Growing evidence shows that enhancer RNAs (eRNAs) are pivotal for tumor progression. In this research, our team aimed to identify the survival-related eRNAs and further explore their potential function in glioblastoma (GBM).

Methods

RNA-sequencing data in 31 tumor types were acquired from TCGA datasets. The survival-related eRNAs were identified by the use of Kaplan-Meier survival analyses and Spearman's correlation analyses. KEGG pathway enrichment analysis was completed to investigate the underlying signal paths of the critical eRNA. Pancancer assays were applied to explore the association between CYP1B1-AS1 and CYP1B1.

Results

We identified 74 survival-related eRNAs and focused on CYP1B1-AS1 which displayed the greatest cor value. CYP1B1 was identified as a regulatory target of CYP1B1-AS1. KEGG analyses suggested that CYP1B1-AS1 might play an essential role through CK-CKR mutual effect, complement and coagulation cascades, TNF signal path, and JAK-STAT signal path. The pancancer verification outcomes revealed that CYP1B1-AS1 was related to survival in 4 cancers, i.e., LIHC, KIRP, KICH, and KIRC. Association was discovered between CYP1B1-AS1 and the targeted gene, CYP1B1, in 29 cancer types.

Conclusion

The outcomes herein provided the first evidence that overexpression of CYP1B1-AS1 might be a potential molecular biomarker for predicting the prognosis of patients with GBM.

Deconvolution of Bulk Gene Expression Profiles with Single-Cell Transcriptomics to Develop a Cell Type Composition-Based Prognostic Model for Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is one of the malignant hematologic cancers with rapid progress and poor prognosis. Most AML prognostic stratifications focused on genetic abnormalities. However, none of them was established based on the cell type compositions (CTCs) of peripheral blood or bone marrow aspirates from patients at diagnosis. Here we sought to develop a novel prognostic model for AML in adults based on the CTCs. First, we applied the CIBERSORT algorithm to estimate the CTCs for patients from two public datasets (GSE6891 and TCGA-LAML) using a custom gene expression signature reference constructed by an AML single-cell RNA sequencing dataset (GSE116256). Then, a CTC-based prognostic model was established using least absolute shrinkage and selection operator Cox regression, termed CTC score. The constructed prognostic model CTC score comprised 3 cell types, GMP-like, HSC-like, and T. Compared with the low-CTC-score group, the high-CTC-score group showed a 1.57-fold [95% confidence interval (CI), 1.23 to 2.00; p = 0.0002] and a 2.32-fold (95% CI, 1.53 to 3.51; p < 0.0001) higher overall mortality risk in the training set (GSE6891) and validation set (TCGA-LAML), respectively. When adjusting for age at diagnosis, cytogenetic risk, and karyotype, the CTC score remained statistically significant in both the training set [hazard ratio (HR) = 2.25; 95% CI, 1.20 to 4.24; p = 0.0119] and the validation set (HR = 7.97; 95% CI, 2.95 to 21.56; p < 0.0001]. We further compared the performance of the CTC score with two gene expression-based prognostic scores: the 17-gene leukemic stem cell score (LSC17 score) and the AML prognostic score (APS). It turned out that the CTC score achieved comparable performance at 1-, 2-, 3-, and 5-years timepoints and provided independent and additional prognostic information different from the LSC17 score and APS. In conclusion, the CTC score could serve as a powerful prognostic marker for AML and has great potential to assist clinicians to formulate individualized treatment plans.

Identification and validation of a novel redox-related lncRNA prognostic signature in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the main causes of cancer deaths globally. Redox is emerging as a crucial contributor to the pathophysiology of LUAD, which can be regulated by long non-coding RNAs (lncRNAs). The aim of our research is to identify a novel redox-related lncRNA prognostic signature (redox-LPS) for better prediction of LUAD prognosis. 535 LUAD samples from The Cancer Genome Atlas (TCGA) database and 226 LUAD samples from the Gene Expression Omnibus (GEO) database were included in our study. 67 redox genes and 313 redox-related lncRNAs were identified. After performing LASSO-Cox regression analysis, a redox-LPS consisting of four lncRNAs (i.e., CRNDE, CASC15, LINC01137, and CYP1B1-AS1) was developed and validated. Our redox-LPS was superior to another three established models in predicting survival probability of LUAD patients. Univariate and multivariate Cox regression analysis revealed that risk score and stage were independent prognostic indicators. A nomogram plot including risk score and stage was constructed to predict survival probability of LUAD patients; this was further verified by calibration curves. Functional enrichment analysis and gene set enrichment analysis, were performed to determine the differences in cellular processes and signaling pathways between the high – and low-risk subgroups. A variety of algorithms (such as single-sample gene set enrichment analysis and CIBERSOFT) were conducted to uncover the landscape of tumor immune microenvironment in the high- and low-risk subgroups. In conclusion, a novel independent redox-LPS was constructed and validated for LUAD patients, which might provide new insights for clinical decision-making and precision medicine.