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Abstract Glioblastoma (GBM) is the most common brain tumor and remains incurable. Primary GBM cultures are widely used tools for drug screening, but there is a lack of genomic and pharmacological characterization for these primary GBM cultures. Here, we collect 50 patient-derived glioma cell (PDGC) lines and characterize them by whole genome sequencing, RNA sequencing, and drug response screening. We identify three molecular subtypes among PDGCs: mesenchymal (MES), proneural (PN), and oxidative phosphorylation (OXPHOS). Drug response profiling reveals that PN subtype PDGCs are sensitive to tyrosine kinase inhibitors, whereas OXPHOS subtype PDGCs are sensitive to histone deacetylase inhibitors, oxidative phosphorylation inhibitors, and HMG-CoA reductase inhibitors. PN and OXPHOS subtype PDGCs stably form tumors in vivo upon intracranial transplantation into immunodeficient mice, whereas most MES subtype PDGCs fail to form tumors in vivo. In addition, PDGCs cultured by serum-free medium, especially long-passage PDGCs, carry MYC/MYCN amplification, which is rare in GBM patients. Our study provides a valuable resource for understanding primary glioma cell cultures and clinical translation and highlights the problems of serum-free PDGC culture systems that cannot be ignored.

Abstract Temozolomide (TMZ) is a standard treatment for glioblastoma (GBM) patients. However, TMZ has moderate therapeutic effects due to chemoresistance of GBM cells through less clarified mechanisms. Here, we demonstrate that TMZ-derived 5-aminoimidazole-4-carboxamide (AICA) is converted to AICA ribosyl-5-phosphate (AICAR) in GBM cells. This conversion is catalyzed by hypoxanthine phosphoribosyl transferase 1 (HPRT1), which is highly expressed in human GBMs. As the bona fide activator of AMP-activated protein kinase (AMPK), TMZ-derived AICAR activates AMPK to phosphorylate threonine 52 (T52) of RRM1, the catalytic subunit of ribonucleotide reductase (RNR), leading to RNR activation and increased production of dNTPs to fuel the repairment of TMZ-induced-DNA damage. RRM1 T52A expression, genetic interruption of HPRT1-mediated AICAR production, or administration of 6-mercaptopurine (6-MP), a clinically approved inhibitor of HPRT1, blocks TMZ-induced AMPK activation and sensitizes brain tumor cells to TMZ treatment in mice. In addition, HPRT1 expression levels are positively correlated with poor prognosis in GBM patients who received TMZ treatment. These results uncover a critical bifunctional role of TMZ in GBM treatment that leads to chemoresistance. Our findings underscore the potential of combined administration of clinically available 6-MP to overcome TMZ chemoresistance and improve GBM treatment.

Abstract Male breast cancer (MBC) is a rare but aggressive malignancy with cellular and immunological characteristics that remain unclear. Here, we perform transcriptomic analysis for 111,038 single cells from tumor tissues of six MBC and thirteen female breast cancer (FBC) patients. We find that that MBC has significantly lower infiltration of T cells relative to FBC. Metastasis-related programs are more active in cancer cells from MBC. The activated fatty acid metabolism involved with FASN is related to cancer cell metastasis and low immune infiltration of MBC. T cells in MBC show activation of p38 MAPK and lipid oxidation pathways, indicating a dysfunctional state. In contrast, T cells in FBC exhibit higher expression of cytotoxic markers and immune activation pathways mediated by immune-modulatory cytokines. Moreover, we identify the inhibitory interactions between cancer cells and T cells in MBC. Our study provides important information for understanding the tumor immunology and metabolism of MBC.

Abstract Targeted cancer therapies have revolutionized treatment but their efficacies are limited by the development of resistance driven by clonal evolution within tumors. We developed “CAPTURE”, a single-cell barcoding approach to comprehensively trace clonal dynamics and capture live lineage-coupled resistant cells for in-depth multi-omics analysis and functional exploration. We demonstrate that heterogeneous clones, either preexisting or emerging from drug-tolerant persister cells, dominated resistance to vemurafenib in BRAFV600E melanoma. Further integrative studies uncovered diverse resistance mechanisms. This includes a previously unrecognized and clinically relevant mechanism, chromosome 18q21 gain, which leads to vulnerability of the cells to BCL2 inhibitor. We also identified targetable common dependencies of captured resistant clones, such as oxidative phosphorylation and E2F pathways. Our study provides new therapeutic insights into overcoming therapy resistance in BRAFV600E melanoma and presents a platform for exploring clonal evolution dynamics and vulnerabilities that can be applied to study treatment resistance in other cancers.

Abstract Background Circulating tumor DNA (ctDNA) has been proven as a marker for detecting minimal residual diseases following systemic therapies in mid-to-late-stage non-small-cell lung cancers (NSCLCs) by multiple studies. However, fewer studies cast light on ctDNA-based MRD monitoring in early-to-mid-stage NSCLCs that received surgical resection as the standard of care. Methods We prospectively recruited 128 patients with stage I–III NSCLCs who received curative surgical resections in our Lung Cancer Tempo-spatial Heterogeneity prospective cohort. Plasma samples were collected before the surgery, 7 days after the surgery, and every 3 months thereafter. Targeted sequencing was performed on a total of 628 plasma samples and 645 matched tumor samples using a panel covering 425 cancer-associated genes. Tissue clonal phylogeny of each patient was reconstructed and used to guide ctDNA detection. Results The results demonstrated that ctDNA was more frequently detected in patients with higher stage diseases pre- and postsurgery. Positive ctDNA detection at as early as 7 days postsurgery identified high-risk patients with recurrence (HR = 3.90, P

No clinical assay currently exists to classify glioma tumours based on gene expression. Here, the authors develop a DNA methylation-based classifier, Unified Diagnostic Pipeline (UniD) that identifies genomic alterations and gene expression subtypes of gliomas.

The mechanism(s) of immune checkpoint inhibitor (ICI)-induced myasthenia gravis (MG), an immune-related adverse event (irAE) that is fatal and limits subsequent ICI use, remain unexplored. Here, through comparative genomic analysis, we identified a pathogenic p.S467C germline variant in SLC22A5 in a thymoma case with ICI-induced MG, which was found to be associated with fatty acid oxidation through its regulation on L-carnitine levels. Remarkably, ICI rechallenge with L-carnitine pretreatment led to durable response without MG-related symptoms. Thus, we provide the first clinical evidence of genetic test-directed irAE management, which integrates individualized ICI treatment into the evolving paradigm of cancer management.

Abstract Background Males and females differ in their immunological responses to foreign pathogens. However, most of the current COVID-19 clinical practices and trials do not take the sex factor into consideration. Methods We performed a sex-based comparative analysis for the clinical outcomes, peripheral immune cells, and severe acute respiratory syndrome coronavirus (SARS-CoV-2) specific antibody levels of 1558 males and 1499 females COVID-19 patients from a single center. The lymphocyte subgroups were measured by Flow cytometry. The total antibody, Spike protein (S)-, receptor binding domain (RBD)-, and nucleoprotein (N)- specific IgM and IgG levels were measured by chemiluminescence. Results We found that male patients had approximately two-fold rates of ICU admission (4.7% vs. 2.7% in males and females, respectively, P = 0.005) and mortality (3% vs. 1.4%, in males and females, respectively, P = 0.004) than female patients. Survival analysis revealed that the male sex is an independent risk factor for death from COVID-19 (adjusted hazard ratio [HR] = 2.22, 95% confidence interval [CI]: 1.3–3.6, P = 0.003). The level of inflammatory cytokines in peripheral blood was higher in males during hospitalization. The renal (102/1588 [6.5%] vs. 63/1499 [4.2%], in males and females, respectively, P = 0.002) and hepatic abnormality (650/1588 [40.9%] vs. 475/1499 [31.7%], P = 0.003) were more common in male patients than in female patients. By analyzing dynamic changes of lymphocyte subsets after symptom onset, we found that the percentage of CD19+ B cells and CD4+ T cells was generally higher in female patients during the disease course of COVID-19. Notably, the protective RBD-specific IgG against SARS-CoV-2 sharply increased and reached a peak in the fourth week after symptom onset in female patients, while gradually increased and reached a peak in the seventh week after symptom onset in male patients. Conclusions Males had an unfavorable prognosis, higher inflammation, a lower percentage of lymphocytes, and indolent antibody responses during SARS-CoV-2 infection and recovery. Early medical intervention and close monitoring are important, especially for male COVID-19 patients.

Treatment-resistant glioma stem cells are thought to propagate and drive growth of malignant gliomas, but their markers and our ability to target them specifically are not well understood. We demonstrate that podoplanin (PDPN) expression is an independent prognostic marker in gliomas across multiple independent patient cohorts comprising both high- and low-grade gliomas. Knockdown of PDPN radiosensitized glioma cell lines and glioma-stem-like cells (GSCs). Clonogenic assays and xenograft experiments revealed that PDPN expression was associated with radiotherapy resistance and tumor aggressiveness. We further demonstrate that knockdown of PDPN in GSCs in vivo is sufficient to improve overall survival in an intracranial xenograft mouse model. PDPN therefore identifies a subset of aggressive, treatment-resistant glioma cells responsible for radiation resistance and may serve as a novel therapeutic target.

Abstract Background COVID-19 has resulted in high mortality worldwide. Information regarding cardiac markers for precise risk-stratification is limited. We aim to discover sensitive and reliable early-warning biomarkers for optimizing management and improving the prognosis of COVID-19 patients. Methods A total of 2954 consecutive COVID-19 patients who were receiving treatment from the Wuhan Huoshenshan Hospital in China from February 4 to April 10 were included in this retrospective cohort. Serum levels of cardiac markers were collected after admission. Coronary artery disease diagnosis and survival status were recorded. Single-cell RNA-sequencing and bulk RNA-sequencing from different cohorts of non-COVID-19 were performed to analyze SARS-CoV-2 receptor expression. Results Among 2954 COVID-19 patients in the analysis, the median age was 60 years (50–68 years), 1461 (49.5%) were female, and 1515 (51.3%) were severe/critical. Compared to mild/moderate (1439, 48.7%) patients, severe/critical patients showed significantly higher levels of cardiac markers within the first week after admission. In severe/critical COVID-19 patients, those with abnormal serum levels of BNP (42 [24.6%] vs 7 [1.1%]), hs-TNI (38 [48.1%] vs 6 [1.0%]), α- HBDH (55 [10.4%] vs 2 [0.2%]), CK-MB (45 [36.3%] vs 12 [0.9%]), and LDH (56 [12.5%] vs 1 [0.1%]) had a significantly higher mortality rate compared to patients with normal levels. The same trend was observed in the ICU admission rate. Severe/critical COVID-19 patients with pre-existing coronary artery disease (165/1,155 [10.9%]) had more cases of BNP (52 [46.5%] vs 119 [16.5%]), hs-TNI (24 [26.7%] vs 9.6 [%], α- HBDH (86 [55.5%] vs 443 [34.4%]), CK-MB (27 [17.4%] vs 97 [7.5%]), and LDH (65 [41.9%] vs 382 [29.7%]), when compared with those without coronary artery disease. There was enhanced SARS-CoV-2 receptor expression in coronary artery disease compared with healthy controls. From regression analysis, patients with five elevated cardiac markers were at a higher risk of death (hazards ratio 3.4 [95% CI 2.4–4.8]). Conclusions COVID-19 patients with pre-existing coronary artery disease represented a higher abnormal percentage of cardiac markers, accompanied by high mortality and ICU admission rate. BNP together with hs-TNI, α- HBDH, CK-MB and LDH act as a prognostic biomarker in COVID-19 patients with or without pre-existing coronary artery disease.

Accurate detection and location of tumor lesions are essential for improving the diagnosis and personalized cancer therapy. However, the diagnosis of lesions with fuzzy histology is mainly dependent on experiences and with low accuracy and efficiency. Here, we developed a logistic regression model based on mutational signatures (MS) for each cancer type to trace the tumor origin. We observed MS could distinguish cancer from inflammation and healthy individuals. By collecting extensive datasets of samples from ten tumor types in the training cohort (5,001 samples) and independent testing cohort (2,580 samples), cancer-type-specific MS patterns (CTS-MS) were identified and had a robust performance in distinguishing different types of primary and metastatic solid tumors (AUC:0.76 ∼ 0.93). Moreover, we validated our model in an Asian population and found that the AUC of our model in predicting the tumor origin of the Asian population was higher than 0.7. The metastatic tumor lesions inherited the MS pattern of the primary tumor, suggesting the capability of MS in identifying the tissue-of-origin for metastatic cancers. Furthermore, we distinguished breast cancer and prostate cancer with 90% accuracy by combining somatic mutations and CTS-MS from cfDNA, indicating that the CTS-MS could improve the accuracy of cancer-type prediction by cfDNA. In summary, our study demonstrated that MS was a novel reliable biomarker for diagnosing solid tumors and provided new insights into predicting tissue-of-origin.

Understanding antibody responses to Sars-CoV-2 proteins over time is complicated by many variables. Here the authors survey IgM and IgG antibodies against S protein, RBD and nucleoprotein in a large cohort of infected and recovering severe vs. moderate COVID-19 patients, comparing against clinical parameters and immunological readouts.

Abstract Background Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. Results We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCOhigh TAMs induce a phenotypic shift towards mesenchymal cellular state of glioma stem cells, promoting both invasive and proliferative activities, as well as therapeutic resistance to irradiation. MARCOhigh TAMs also significantly accelerate tumor engraftment and growth in vivo. Moreover, both MA-TAM master regulators and their target genes are significantly correlated with poor clinical outcomes and are often associated with genomic aberrations in neurofibromin 1 (NF1) and phosphoinositide 3-kinases/mammalian target of rapamycin/Akt pathway (PI3K-mTOR-AKT)-related genes. We further demonstrate the origination of MA-TAMs from peripheral blood, as well as their potential association with tumor-induced polarization states and immunosuppressive environments. Conclusions Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy.

Objective: To distinguish COVID-19 patients and non-COVID-19 viral pneumonia patients and classify COVID-19 patients into low-risk and high-risk at admission by laboratory indicators.Materials and methods: In this retrospective cohort, a total of 3,563 COVID-19 patients and 118 non-COVID-19 pneumonia patients were included. There are two cohorts of COVID-19 patients, including 548 patients in the training dataset, and 3,015 patients in the testing dataset. Laboratory indicators were measured during hospitalization for all patients. Based on laboratory indicators, we used the support vector machine and joint random sampling to risk stratification for COVID-19 patients at admission. Based on laboratory indicators detected within the 1st week after admission, we used logistic regression and joint random sampling to develop the survival mode. The laboratory indicators of COVID-10 and non-COVID-19 were also compared.Results: We first identified the significant laboratory indicators related to the severity of COVID-19 in the training dataset. Neutrophils percentage, lymphocytes percentage, creatinine, and blood urea nitrogen with AUC >0.7 were included in the model. These indicators were further used to build a support vector machine model to classify patients into low-risk and high-risk at admission in the testing dataset. Results showed that this model could stratify the patients in the testing dataset effectively (AUC = 0.89). Our model still has good performance at different times (Mean AUC: 0.71, 0.72, 0.72, respectively for 3, 5, and 7 days after admission). Moreover, laboratory indicators detected within the 1st week after admission were able to estimate the probability of death (AUC = 0.95). We identified six indicators with permutation p < 0.05, including eosinophil percentage (p = 0.007), white blood cell count (p = 0.045), albumin (p = 0.041), aspartate transaminase (p = 0.043), lactate dehydrogenase (p = 0.002), and hemoglobin (p = 0.031). We could diagnose COVID-19 and differentiate it from other kinds of viral pneumonia based on these laboratory indicators.Conclusions: Our risk-stratification model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage of COVID-19. In addition, laboratory findings could be used to distinguish COVID-19 and non-COVID-19.

The identification of asymptomatic, non-severe presymptomatic, and severe presymptomatic coronavirus disease 2019 (COVID-19) in patients may help optimize risk-stratified clinical management and improve prognosis. This single-center case series from Wuhan Huoshenshan Hospital, China, included 2,980 patients with COVID-19 who were hospitalized between February 4, 2020 and April 10, 2020. Patients were diagnosed as asymptomatic (n = 39), presymptomatic (n = 34), and symptomatic (n = 2,907) upon admission. This study provided an overview of asymptomatic, presymptomatic, and symptomatic COVID-19 patients, including detection, demographics, clinical characteristics, and outcomes. Upon admission, there was no significant difference in clinical symptoms and CT image between asymptomatic and presymptomatic patients for diagnosis reference. The mean area under the receiver operating characteristic curve (AUC) of the differential diagnosis model to discriminate presymptomatic patients from asymptomatic patients was 0.89 (95% CI, 0.81-0.98). Importantly, the severe and non-severe presymptomatic patients can be further stratified (AUC = 0.82). In conclusion, the two-step risk-stratification model based on 10 laboratory indicators can distinguish among asymptomatic, severe presymptomatic, and non-severe presymptomatic COVID-19 patients on admission. Moreover, single-cell data analyses revealed that the CD8+T cell exhaustion correlated to the progression of COVID-19.

BackgroundCoronavirus disease 2019 (COVID-19) has rapidly spread worldwide. Systematic analysis of lung cancer survivors at molecular and clinical levels is warranted to understand the disease course and clinical characteristics.MethodsA single-center, retrospective cohort study was conducted in 65 patients with COVID-19 from Wuhan Huoshenshan Hospital, of which 13 patients were diagnosed with lung cancer. The study was conducted from February 4 to April 11, 2020.ResultsDuring the course of treatment, lung cancer survivors infected with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) had shorter median time from symptom onset to hospitalization (P = 0.016) and longer clinical symptom remission time (P = 0.020) than non-cancer individuals. No differences were observed among indicators such as time from symptom onset to hospitalization and symptom remission time between medium-term and short-term survivors. The expression of ACE2 (P = 0.013) and TMPRSS2 (P

The distinct genomic and epidemiological features of Chinese lung cancer patients suggest the presence of alternative causal mechanisms. Here, the authors present the genomic landscape of 149 Chinese NSCLC patients and reveal distinct mutational signatures associated with inflammatory microenvironments.

Genes normally expressed in the testis but aberrantly expressed in cancer are termed cancer testis antigens. In this study, the authors catalogue the expression of these genes in 19 different cancer types and correlate expression with some somatically mutated oncogenes.

We have previously reported that overexpression of Programmed Death -1 Homolog (PD-1H) in human monocytes leads to activation and spontaneous secretion of multiple pro inflammatory cytokines. Here we evaluate changes in monocytes gene expression after enforced PD-1H expression by gene array. The results show that there are significant alterations in 51 potential candidate genes that relate to immune response, cell adhesion and metabolism. Genes corresponding to pro-inflammatory cytokines showed the highest upregulation, 7, 3.2, 3.0, 5.8, 4.4 and 3.1 fold upregulation of TNF-α, IL-1 β, IFN-α, γ, λ and IL-27 relative to vector control. The data are in agreement with cytometric bead array analysis showing induction of proinflammatory cytokines, IL-6, IL-1β and TNF-α by PD-1H. Other genes related to inflammation, include transglutaminase 2 (TG2), NF-κB (p65 and p50) and toll like receptors (TLR) 3 and 4 were upregulated 5, 4.5 and 2.5 fold, respectively. Gene set enrichment analysis (GSEA) also revealed that signaling pathways related to inflammatory response, such as NFκB, AT1R, PYK2, MAPK, RELA, TNFR1, MTOR and proteasomal degradation, were significantly upregulated in response to PD-1H overexpression. We validated the results utilizing a standard inflammatory sepsis model in humanized BLT mice, finding that PD-1H expression was highly correlated with proinflammatory cytokine production. We therefore conclude that PD-1H functions to enhance monocyte activation and the induction of a pro-inflammatory gene expression profile.

MicroRNAs (miRNAs) are a group of small non-coding RNAs that play important regulatory roles at the post-transcriptional level. Although several computational methods have been developed to compare miRNAs, it is still a challenging and a badly needed task with the availability of various biological data resources. In this study, we proposed a novel graph theoretic property based computational framework and method, called miRFunSim, for quantifying the associations between miRNAs based on miRNAs targeting propensity and proteins connectivity in the integrated protein-protein interaction network. To evaluate the performance of our method, we applied the miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs whose target genes have been experimentally supported and found that the functional similarity scores of miRNAs in the same family or in the same cluster are significantly higher compared with other miRNAs which are consistent with prior knowledge. Further validation analysis on experimentally verified miRNA-disease associations suggested that miRFunSim can effectively recover the known miRNA pairs associated with the same disease and achieve a higher AUC of 83.1%. In comparison with similar methods, our miRFunSim method can achieve more effective and more reliable performance for measuring the associations of miRNAs. We also conducted the case study examining liver cancer based on our method, and succeeded in uncovering the candidate liver cancer related miRNAs such as miR-34 which also has been proven in the latest study.

MicroRNAs (miRNAs) are a class of small (19-25 nt) non-coding RNAs. This important class of gene regulator downregulates gene expression through sequence-specific binding to the 3'untranslated regions (3'UTRs) of target mRNAs. Several computational target prediction approaches have been developed for predicting miRNA targets. However, the predicted target lists often have high false positive rates. To construct a workable target list for subsequent experimental studies, we need novel approaches to properly rank the candidate targets from traditional methods. We performed a systematic analysis of experimentally validated miRNA targets using functional genomics data, and found significant functional associations between genes that were targeted by the same miRNA. Based on this finding, we developed a miRNA target prioritization method named mirTarPri to rank the predicted target lists from commonly used target prediction methods. Leave-one-out cross validation has proved to be successful in identifying known targets, achieving an AUC score up to 0. 84. Validation in high-throughput data proved that mirTarPri was an unbiased method. Applying mirTarPri to prioritize results of six commonly used target prediction methods allowed us to find more positive targets at the top of the prioritized candidate list. In comparison with other methods, mirTarPri had an outstanding performance in gold standard and CLIP data. mirTarPri was a valuable method to improve the efficacy of current miRNA target prediction methods. We have also developed a web-based server for implementing mirTarPri method, which is freely accessible at http://bioinfo.hrbmu.edu.cn/mirTarPri.

One of the challenging problems in the etiology of diseases is to explore the relationships between initiation and progression of diseases and abnormalities in local regions of metabolic pathways. To gain insight into such relationships, we applied the "k-clique" subpathway identification method to all disease-related gene sets. For each disease, the disease risk regions of metabolic pathways were then identified and considered as subpathways associated with the disease. We finally built a disease-metabolic subpathway network (DMSPN). Through analyses based on network biology, we found that a few subpathways, such as that of cytochrome P450, were highly connected with many diseases, and most belonged to fundamental metabolisms, suggesting that abnormalities of fundamental metabolic processes tend to cause more types of diseases. According to the categories of diseases and subpathways, we tested the clustering phenomenon of diseases and metabolic subpathways in the DMSPN. The results showed that both disease nodes and subpathway nodes displayed slight clustering phenomenon. We also tested correlations between network topology and genes within disease-related metabolic subpathways, and found that within a disease-related subpathway in the DMSPN, the ratio of disease genes and the ratio of tissue-specific genes significantly increased as the number of diseases caused by the subpathway increased. Surprisingly, the ratio of essential genes significantly decreased and the ratio of housekeeping genes remained relatively unchanged. Furthermore, the coexpression levels between disease genes and other types of genes were calculated for each subpathway in the DMSPN. The results indicated that those genes intensely influenced by disease genes, including essential genes and tissue-specific genes, might be significantly associated with the disease diversity of subpathways, suggesting that different kinds of genes within a disease-related subpathway may play significantly differential roles on the diversity of diseases caused by the corresponding subpathway.

Given the extremely high inter-patient heterogeneity of acute myeloid leukemia (AML), the identification of biomarkers for prognostic assessment and therapeutic guidance is critical. Cell surface markers (CSMs) have been shown to play an important role in AML leukemogenesis and progression. In the current study, we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas (TCGA) based on differential gene expression analysis and univariable Cox proportional hazards regression analysis. By using multi-model analysis, including Adaptive LASSO regression, LASSO regression, and Elastic Net, we constructed a 9-CSMs prognostic model for risk stratification of the AML patients. The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels. Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients. The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores. Notably, single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance. Furthermore, PI3K inhibitors were identified as potential treatments for these high-risk patients. In conclusion, we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy. [ABSTRACT FROM AUTHOR]