Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma

Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning

BACKGROUND

Gastric cancer (GC), a common and deadly malignancy worldwide, is a serious burden on society and individuals. However, available diagnostic biomarkers for GC are very limited. The current study aimed to identify potential diagnostic biomarkers for GC and analyze the activity of infiltrating immune cells in this pathology.

METHODS

Microarray data for GC were acquired from the Gene Expression Omnibus (GEO) database. The limma package was utilized to normalize these data, thus identifying differentially expressed genes (DEGs). For normalized data of samples, we established a weighted gene co-expression network (WGCNA) to reveal key genes in the significant module. Afterward, we obtained overlapping genes by intersecting the DEGs and the key genes from the WGCNA module. Next, after applying the three algorithms (LASSO, RandomForest, and SVM-RFE) to analyze these overlapping genes and take the intersection, we established a GC diagnosis. The diagnostic significances of these identified genes were evaluated with receiver operating characteristic (ROC) curves and validated in the external dataset. Furthermore, ssGSEA and CIBERSORT were employed for evaluating the infiltrating immune cells and the association of the immune cells and diagnostic biomarkers.

RESULTS

Herein, we identified 49 overlapping genes, and the results of enrichment analysis demonstrated that these genes may be involved in the signaling transduction-related process. Finally, BANF1, DUSP14, and VMP1 were regarded as key biomarkers in GC patients based on the overlapping genes that we found, and these three biomarkers demonstrated great diagnostic significance. Additionally, the hub biomarkers had different levels of association with macrophages, neutrophils, memory B cells, and plasma cells.

CONCLUSIONS

BANF1, DUSP14, and VMP1 are promising diagnostic biomarkers for GC, and infiltrating immune cells may dramatically affect gastric carcinogenesis and progression.

FAM111B and FANCD2, a dual expression signature, defines a distinct phenotype of pancreatic cancer

BACKGROUND

Despite various treatment strategies, the incidence and mortality of pancreatic cancer (PC) are among the highest for malignant tumors. Furthermore, there is a lack of effective molecular typing and targeted therapy to treat PC subtypes.

METHODS

Multiplex immunofluorescence experiments were performed to explore the roles of FAM111B, FANCD2, KRAS and TP53 in human PC tissues. Kaplan-Meier survival curves were generated and a nomogram was prepared for prognostic prediction. Protein correlations were analyzed using human PC tissues and TCGA and GEO data. Pathways analysis, immunoanalysis, and drug susceptibility analysis were performed based on information in the TCGA database.

RESULTS

Our results indicate that expression of FAM111B and FANCD2 is correlated in human PC tissues and comprises a dual expression signature with predictive value for the prognosis of PC. Using information in public databases, we confirmed the oncogenic relevance of FAM111B and FANCD2 in PC and identified a positive correlation between FAM111B, FANCD2, TP53 and KRAS.FAM111B and FANCD2 jointly regulate ferroptosis, mitotic nuclear division, and nuclear division pathways. Both proteins were demonstrated to be positively correlated with markers of CD4 + Th2 cells and PD-L1 in the tumor microenvironment. Furthermore, drug sensitivity analysis suggested that patients with high FAM111B or FANCD2 expression were highly sensitive to chemotherapeutic and targeted drugs, indicating that these proteins may serve as predictors of treatment efficacy.

CONCLUSION

Elevated dual expression of FAM111B and FANCD2 is indicative of poor prognosis, alters the immune microenvironment, and exhibits sensitivity to certain therapeutic agents. Consequently, the high FAM111B/FANCD2 expression subtype may represent a novel and distinct phenotype of PC.

Multi-omics analysis and experiments uncover the link between cancer intrinsic drivers, stemness, and immunotherapy in ovarian cancer with validation in a pan-cancer census

Background

Although immune checkpoint inhibitors (ICIs) represent a substantial breakthrough in cancer treatment, it is crucial to acknowledge that their efficacy is limited to a subset of patients. The heterogeneity and stemness of cancer render its response to immunotherapy variable, warranting the identification of robust biomarkers for evaluation.

Methods

Publicly available Ovarian Cancer (OV) single-cell RNA (scRNA) sequence dataset was collected and analyzed to elucidate the intrinsic driver gene of OV cancer cells. Through genome-scale CRISPR screening of RNA sequencing data from Project Achilles, essential genes specific to OV were identified. A novel cancer stem cell index (CSCI) was developed and validated using multiple advanced algorithms and large-scale datasets, as well as corresponding clinical features, including 14 OV transcriptomic datasets, 7 pan-cancer ICI transcriptomic cohorts and one melanoma scRNA dataset derived from PD-1 treated patients.

Results

Chromosomal 20q gain, 8q gain, and 5q loss have been identified as ovarian cancer-specific driving variations. By analyzing large-scale datasets of ovarian cancer transcriptomics, including scRNA and CRISPR cell line datasets, we have identified a gene set that influences tumor intrinsic drivers and stemness properties. We then developed the CSCI to predict the prognosis and response to immunotherapy in ovarian cancer patients using advanced machine learning algorithms. When applied to PD1/PD-L1 ICI transcriptomic cohorts, CSCI consistently and accurately predicts tumor progression and immunotherapy benefits, with a mean AUC greater than 0.8. Notably, compared to previously established signatures, CSCI demonstrates better predictive performance across multiple ovarian cancer datasets. Intriguingly, we discovered that amplification of CSE1L enhances the stemness of tumor-initiating cells, facilitates angiogenesis, and the formation of ovarian cancer, which can serve as a potential therapeutic target. Finally, experiments validated that CSE1L promotes progression, migration, and proliferation of ovarian cancer.

Conclusions

Our study has uncovered a robust correlation between variations in cancer intrinsic drivers and stemness, as well as resistance to immunotherapy. This finding provides valuable insights for potential strategies to overcome immune resistance by targeting genes associated with stemness.

P4HA1 is highly expressed in gastric cancer and promotes proliferation and metastasis of gastric cancer cells

BACKGROUND

Gastric cancer (GC), a prevalent aggressive form of tumor, imposes a significant burden in terms of morbidity and mortality. Prolyl 4-hydroxylase, alpha polypeptide I (P4HA1), a key enzyme in collagen synthesis, comprises two identical alpha subunits and two beta subunits. Studies on the expression and impact of P4HA1 in GC cells are limited.

METHODS

The expression and prognosis of P4HA1 in GC were analyzed using bioinformatics. To confirm the P4HA1 level in GC tissues and cells, Western blot (WB) and RT-qPCR experiments were conducted. The signaling pathways related to P4HA1 in GC were examined using the DAVID database. Moreover, the expression of P4HA1 was downregulated by transfecting GC cell lines (HGC-27 and SGC-7901) with siRNA technology. Furthermore, GC proliferation, migration, and invasion were detected via plate cloning, CCK-8, and Transwell assays. The epithelial-mesenchymal transition (EMT) genes (E-cadherin, N-cadherin, Vimentin) and the stemness marker CD44 protein expression in GC cells were detected using WB. The sphere-forming ability of GC cells was analyzed using a sphere-forming assay to determine the effect of P4HA1.

RESULTS

Bioinformatics and experimental analyses demonstrated that P4HA1 expression was extensively detected in GC tissues and cells, and strongly related to a poor prognosis for GC. In vitro studies demonstrated that P4HA1 suppression hindered the proliferation, migration, and invasion of GC cells and suppressed EMT characteristics. Both sphere-forming and WB assays revealed that the sphere-forming potential of GC cells and the level of CD44 protein decreased after knocking down the expression of P4HA1, indicating that suppression of P4HA1 could inhibit the stemness of GC cells.

CONCLUSION

The study concluded that P4HA1 has the potential to be expressed substantially in GC tissues and cells and is capable of enhancing the proliferation, metastasis, and stemness of GC.

Molecular subtyping combined with multiomics analysis to study correlation between TACE refractoriness and tumor stemness in hepatocellular carcinoma

BACKGROUND

Transarterial chemoembolization (TACE) refractoriness is a significant challenge in treating intermediate to advanced-stage hepatocellular carcinoma (HCC). A few studies suggest that liver cancer stem cells (LCSCs) may be associated with TACE refractoriness. This study aims to explore the potential correlation between TACE refractoriness and HCC stemness, highlighting its clinical significance.

METHODS

This research encompassed the analysis of diverse HCC datasets, including RNA-sequencing, microarray, single-cell RNA-sequencing, and clinical cohorts. We identified common genes between TACE refractoriness and tumor stemness (TSGs). Unsupervised clustering was employed to classify HCC patients into different clusters based on TSGs (TRS clusters). The study explored the differences in clinical prognosis, biological characteristics, genomic variations, immune landscapes, and treatment responses among the TRS clusters.

RESULTS

Patients with TACE-refractoriness demonstrated significantly higher tumor stemness. Our study identified 33 TSGs and established two TRS clusters, including C1 and C2. C1 was associated with TACE refractoriness, elevated tumor stemness, and poorer prognosis. Genomic alterations were found to be significantly different between the TRS clusters. The C1 exhibited signs of immunosuppression and lower activity of immune effector cells, while the C2 had a more robust immune response and higher level of immune cell presence. Single-cell RNA-seq revealed distinct cell type characteristics in each subtypes, with the C1 showing a higher proportion of stem cells and malignant cells.

CONCLUSION

Our findings establish a connection between TACE refractoriness and tumor stemness in HCC, proposing a novel subtype classification to guide personalized treatment. Insights gained may facilitate overcoming TACE refractoriness and the development of innovative therapies.

Overexpression of ornithine decarboxylase 1 mediates the immune-deserted microenvironment and poor prognosis in diffuse large B-cell lymphoma

Background

Previous researches mainly focused on whether cancer stem cells exist in diffuse large B-cell lymphoma (DLBCL). However, subgroups with dismal prognosis and stem cell-like characteristics have been overlooked.

Methods

Using large scale data (n = 2133), we conducted machine learning algorithms to identify a high risk DLBCL subgroup with stem cell-like features, and then investigated the potential mechanisms in shaping this subgroup using transcriptome, genome and single-cell RNA-seq data, and in vitro experiments.

Results

We identified a high-risk subgroup (25.6 % of DLBCL) with stem cell-like characteristics and dismal prognosis. This high-risk group (HRG) was featured by upregulation of key enzyme (ODC1) in polyamine metabolism and cold tumor microenvironment (TME), and had a poor prognosis with lower 3-year overall survival (OS) (54.3 % vs. 83.6 %, P < 0.0001) and progression-free survival (PFS) (42.8 % vs. 74.7 %, P < 0.0001) rates compared to the low-risk group. HRG also exhibited malignant proliferative phenotypes similar to Burkitt lymphoma. Patients with MYC rearrangement, double-hit, double-expressors, or complete remission might have either favorable or poor prognosis, which could be further distinguished by our risk stratification model. Genomic analysis revealed widespread copy number losses in the chemokine and interferon coding regions 8p23.1 and 9p21.3 in HRG. We identified ODC1 as a therapeutic vulnerability for HRG-DLBCL. Single-cell analysis and in vitro experiments demonstrated that ODC1 overexpression enhanced DLBCL cell proliferation and drove macrophage polarization towards the M2 phenotype. Conversely, ODC1 inhibition reduced DLBCL cell proliferation, induced cell cycle arrest and apoptosis, and promoted macrophage polarization towards the M1 phenotype. Finally, we developed a comprehensive database of DLBCL for clinical application.

Conclusions

Our study effectively advances the precise risk stratification of DLBCL and reveals that ODC1 and immune-deserted microenvironment jointly shape a group of DLBCL patients with stem cell-like features. Targeting ODC1 regulates immunotherapies in DLBCL, offering new insights for DLBCL treatment.

Molecular characterization of EBV-associated primary pulmonary lymphoepithelial carcinoma by multiomics analysis

BACKGROUND

Primary pulmonary lymphoepithelial carcinoma (pLEC) is a subtype of non-small cell lung cancer (NSCLC) characterized by Epstein-Barr virus (EBV) infection. However, the molecular pathogenesis of pLEC remains poorly understood.

METHODS

In this study, we explored pLEC using whole-exome sequencing (WES) and RNA-whole-transcriptome sequencing (RNA-seq) technologies. Datasets of normal lung tissue, other types of NSCLC, and EBV-positive nasopharyngeal carcinoma (EBV+-NPC) were obtained from public databases. Furthermore, we described the gene signatures, viral integration, cell quantification, cell death and immune infiltration of pLEC.

RESULTS

Compared with other types of NSCLC and EBV+-NPC, pLEC patients exhibited a lower somatic mutation burden and extensive copy number deletions, including 1p36.23, 3p21.1, 7q11.23, and 11q23.3. Integration of EBV associated dysregulation of gene expression, with CNV-altered regions coinciding with EBV integration sites. Specifically, ZBTB16 and ERRFI1 were downregulated by CNV loss, and the FOXD family genes were overexpressed with CNV gain. Decreased expression of the FOXD family might be associated with a favorable prognosis in pLEC patients, and these patients exhibited enhanced cytotoxicity.

CONCLUSION

Compared with other types of NSCLC and NPC, pLEC has distinct molecular characteristics. EBV integration, the aberrant expression of genes, as well as the loss of CNVs, may play a crucial role in the pathogenesis of pLEC. However, further research is needed to assess the potential role of the FOXD gene family as a biomarker.

Multi-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses

Background

The heterogeneity of cancer makes it challenging to predict its response to immunotherapy, highlighting the need to find reliable biomarkers for assessment. The sophisticated role of cancer stemness in mediating resistance to immune checkpoint inhibitors (ICIs) is still inadequately comprehended.

Methods

Genome-scale CRISPR screening of RNA sequencing data from Project Achilles was utilized to pinpoint crucial genes unique to Ovarian Cancer (OV). Thirteen publicly accessible OV transcriptomic datasets, seven pan-cancer ICI transcriptomic cohorts, and one single-cell RNA dataset from melanoma patients treated with PD-1 were utilized to scale a novel cancer stemness index (CSI). An OV single-cell RNA dataset was amassed and scrutinized to uncover the role of Small Nuclear Ribonucleoprotein Polypeptide E (SNRPE) in the tumor microenvironment (TME). Vitro experiments were performed to validate the function of SNRPE in promoting proliferation and migration of ovarian cancer.

Results

Through the analysis of extensive datasets on ovarian cancer, a specific gene set that impacts the stemness characteristics of tumors has been identified and we unveiled a negative correlation between cancer stemness, and benefits of ICI treatment in single cell ICI cohorts. This identified gene set underpinned the development of the CSI, a groundbreaking tool leveraging advanced machine learning to predict prognosis and immunotherapy responses in ovarian cancer patients. The accuracy of the CSI was further confirmed by applying PD1/PD-L1 ICI transcriptomic cohorts, with a mean AUC exceeding 0.8 for predicting tumor progression and immunotherapy benefits. Remarkably, when compared to existing immunotherapy and prognosis markers, CSI exhibited superior predictive capabilities across various datasets. Interestingly, our research unveiled that the amplification of SNRPE contribute to remodeling the TME and promoting the evasion of malignant cells from immune system recognition and SNRPE can server as a novel biomarker for predicting immunotherapy response.

Conclusions

A strong relationship between cancer stemness and the response to immunotherapy has been identified in our study. This finding provides valuable insights for devising efficient strategies to address immune evasion by targeting the regulation of genes associated with cellular stemness.

Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4

BACKGROUND

Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 (BEX4) in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, BEX4's comprehensive impact on aneuploidy incidence across different cancer types remains unexplored.

METHODS

Patients from The Cancer Genome Atlas (TCGA) were stratified into high-score (training) and low-score (control) groups based on the aneuploidy score. Mfuzz expression pattern clustering and functional enrichment were applied to genes with BEX4 as the core to explore their regulatory mechanisms. Various machine learning techniques were employed to screen aneuploidy-associated genes, after which aneuploidy characteristic subtypes were established in cancers. Moreover, the aneuploidy characteristics across multiple cancer types were investigated by integrating the extent of tumor cell stemness acquisition and a series of immune traits. Immunohistochemistry and proliferation assay mainly verified the anti-tumor effect of different BEX4 level.

RESULTS

Functional clustering results showed that aneuploidy and stemness were significantly associated in kidney chromophobe (KICH) and thyroid carcinoma (THCA). And cell metabolism and cell cycle had key effects. Residual analysis indicates superior screening performance by random forest (RF). An aneuploid feature gene set with BEX4 as the core was screened to construct a Nomogram model. BEX4, calmodulin regulated spectrin associated protein 2 (CAMSAP2), and myristoylated alanine rich protein kinase C substrate (MARCKS) were identified as aneuploidy characteristic hub genes. Molecular subtypes in thymoma (THYM), thyroid carcinoma (THCA), and kidney chromophobe (KICH) showed significant differences in tumor cell stemness among different subtypes. The competitive endogenous RNA (ceRNA)-Genes network revealed that hub genes, co-regulated by hsa-miR-425-5p, hsa-miR-200c-3p, and others, regulate microtubules, centrosomes, and microtubule cytoskeleton. Furthermore, elevated BEX4 emerged as a significant protective factor in Pancreatic adenocarcinoma (PAAD), KICH, kidney renal papillary cell carcinoma (KIRP), and kidney renal clear cell carcinoma (KIRC).

CONCLUSIONS

BEX4, CAMSAP2, and MARCKS specifically express in microtubules, centrioles, and cytoskeletons, influencing tumor chromosome division and inducing aneuploidy. Additionally, the relationship between the acquisition of tumor cell stemness and the severity of aneuploidy varies significantly across tumor types, displaying positive and negative correlations.

The role of the SOX2 gene in cervical cancer: focus on ferroptosis and construction of a predictive model

BACKGROUND

The intricate interplay between stemness markers and cell death pathways significantly influences the pathophysiology of cervical cancer. SOX2, a pivotal regulator of stem cell pluripotency, has recently been implicated in the modulation of ferroptosis, a specialized form of iron-dependent cell death, in cancer dynamics. This study delineates the role of SOX2 in the ferroptotic landscape of cervical carcinoma.

OBJECTIVE

To delineate the association between SOX2 expression and ferroptosis in cervical cancer and develop a robust, SOX2-centric model for predicting prognosis and enhancing personalized treatment.

METHODS

A multidimensional approach integrating advanced bioinformatics, comprehensive molecular profiling, and state-of-the-art machine learning algorithms was employed to assess SOX2 expression patterns and their correlation with ferroptosis marker expression patterns in cervical cancer tissues. A prognostic model incorporating the expression levels of SOX2 and ferroptosis indicators was meticulously constructed.

RESULTS

This investigation revealed a profound and intricate correlation between SOX2 expression and ferroptotic processes in cervical cancer, substantiated by robust molecular evidence. The developed predictive model based on SOX2 expression exhibited superior prognostic accuracy and may guide therapeutic decision-making.

CONCLUSION

This study underscores the critical role of SOX2 in orchestrating the ferroptosis pathway in cervical cancer and presents a novel prognostic framework. The SOX2-centric predictive model represents a significant advancement in prognosis evaluation, offering a gateway to personalized treatment for gynaecologic cancers.

The immunological and prognostic significance of the diabetes mellitus-related gene WFS1 in endometrial cancer

Background

Diabetes is associated with the incidence and prognosis of various malignancies, most notably endometrial cancer (EC). This study investigated the connection between diabetes and EC, with a specific focus on elucidating the biological implications of the diabetes mellitus (DM)-related gene WFS1.

Methods

Using the CTD, GeneCards, and GSEA databases, we identified WFS1 as a diabetes-related gene and then conducted an extensive investigation focusing on WFS1 in the context of EC. First, we identified WFS1 as the target gene and obtained EC data from the TCGA database. Then, comprehensive analyses and verification experiments, including differential expression analysis, prognostic modeling, functional enrichment analysis, gene mutation profiling, assessment of immune cell infiltration, immunophenoscore (IPS), tumor stemness index scoring, drug sensitivity analysis, single-cell transcriptomic analysis, glycolytic pathway analysis, and clinical verification, were performed to comprehensively evaluate the clinical value of WFS1 in EC.

Results

The EC group had significantly lower WFS1 expression, with an AUC of 0.857 for the ROC diagnostic curve. Overall survival analysis revealed that WFS1 was an independent risk factor for EC; low WFS1 expression was correlated with a poor prognosis. Stemness index analysis revealed that decreased WFS1 expression was associated with increased tumor grade and enhanced tumor stemness, suggesting increased malignancy of EC. In addition, WFS1 expression was correlated with tumor microenvironment features such as immune cell infiltration. WFS1 was also associated with tumor drug resistance.

Conclusion

EC patients with low WFS1 expression have a worse prognosis. WFS1 can be used as diagnostic and prognostic marker for EC.

Helicobacter pylori infection facilitates cell migration and potentially impact clinical outcomes in gastric cancer

Gastric cancer is a significant health concern worldwide. Helicobacter pylori (HP) infection is associated with gastric cancer risk, but differences between HP-infected and HP-free gastric cancer have not been studied sufficiently. The objective of this study was to investigate the effects of HP infection on the viability and migration of gastric cancer cells and identify potential underlying genetic mechanisms as well as their clinical relevance. Cell counting kit-8, lactate dehydrogenase, wound healing, and transwell assay were applied in the infection model of multiple clones of HP and multiple gastric cancer cell lines. Genes related to HP infection were identified using bioinformatics analysis and subsequently validated using real-time quantitative PCR. The association of these genes with immunity and drug sensitivity of gastric cancer was analyzed. Results showed that HP has no significant impact on viability but increases the migration of gastric cancer cells. We identified 1405 HP-upregulated genes, with their enriched terms relating to cell migration, drug, and immunity. Among these genes, the 82 genes associated with survival showed a significant impact on gastric cancer in consensus clustering and LASSO prognostic model. The top 10 hub HP-associated genes were further identified, and 7 of them were validated in HP-infected cells using real-time quantitative PCR, including ERBB4, DNER, BRINP2, KCTD16, MAPK4, THPO, and VSTM2L. The overexpression experiment showed that KCTD16 medicated the effect of HP on gastric cancer migration. Our findings suggest that HP infection may enhance the migratory potential of gastric cancer cells and these genes might be associated with immunity and drug sensitivity of gastric cancer. In human subjects with gastric cancer, HP presence in tumors may affect migration, immunity, and drug sensitivity.

Molecular subtypes based on immunologic and epithelial-mesenchymal transition gene sets reveal tumor immune microenvironment characteristics and implications for immunotherapy of patients with glioma

The tumor immune microenvironment (TIME) significantly influences cancer progression and treatment. This study sought to uncover novel TIME-related glioma biomarkers to advance antitumor immunotherapies by integrating data from sequencing of bulk RNA as well as scRNA. Immunologic and epithelial-mesenchymal transition (EMT) characteristics were used to classify glioma patients into two immune subtypes (ISs) and two EMT subtypes (ESs). Patients in IS1 and ES1, characterized by high immune infiltration and low stemness scores, exhibited poor clinical outcomes and limited responsiveness to immunotherapy. A new risk signature was developed using 16 genes and validated in independent glioma cohorts. Among these, HAVCR2, IL18, LAGLS9, and PTPN6 emerged as hub genes, with IL18 identified as a potential independent indicator. The upregulation of IL18 in high-grade gliomas and U-251 MG cells aligned with bioinformatics analysis. These insights deepen the understanding of TIME-related mechanisms in glioma and highlight potential therapeutic targets, offering a theoretical foundation for effective antitumor immunotherapies in glioma.

Construction of a pathomics model for predicting mRNAsi in lung adenocarcinoma and exploration of biological mechanism

Objective

This study aimed to predict the level of stemness index (mRNAsi) and survival prognosis of lung adenocarcinoma (LUAD) using pathomics model.

Methods

From The Cancer Genome Atlas (TCGA) database, 327 LUAD patients were randomly assigned to a training set (n = 229) and a validation set (n = 98) for pathomics model development and evaluation. PyRadiomics was used to extract pathomics features, followed by feature selection using the mRMR-RFE algorithm. In the training set, Gradient Boosting Machine (GBM) was utilized to establish a model for predicting mRNAsi in LUAD. The model's predictive performance was evaluated using ROC curves, calibration curves, and decision curve analysis (DCA). Prognostic analysis was conducted using Kaplan-Meier curves and cox regression. Additionally, gene enrichment analysis, tumor microenvironment analysis, and tumor mutational burden (TMB) analysis were performed to explore the biological mechanisms underlying the pathomics prediction model.

Results

Multivariable cox analysis (HR = 1.488, 95 % CI 1.012-2.187, P = 0.043) identified mRNAsi as a prognostic risk factor for LUAD. A total of 465 pathomics features were extracted from TCGA-LUAD histopathological images, and ultimately, the most representative 8 features were selected to construct the predictive model. ROC curves demonstrated the significant predictive value of the model for mRNAsi in both the training set (AUC = 0.769) and the validation set (AUC = 0.757). Calibration curves and Hosmer-Lemeshow goodness-of-fit test showed good consistency between the model's prediction of mRNAsi levels and the actual values. DCA indicated a good net benefit of the model. The prediction of mRNAsi levels by the pathomics model is represented using the pathomics score (PS). PS was strongly associated with the prognosis of LUAD (HR = 1.496, 95 % CI 1.008-2.222, P = 0.046). Signaling pathways related to DNA replication and damage repair were significantly enriched in the high PS group. Prediction of immune therapy response indicated significantly reduced Dysfunction in the high PS group (P < 0.001). The high PS group exhibited higher TMB values (P < 0.001).

Conclusions

The predictive model constructed based on pathomics features can forecast the mRNAsi and survival risk of LUAD. This model holds promise to aid clinical practitioners in identifying high-risk patients and devising more optimized treatment plans for patients by jointly employing therapeutic strategies targeting cancer stem cells (CSCs).

Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches

Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

SCD1 promotes the stemness of gastric cancer stem cells by inhibiting ferroptosis through the SQLE/cholesterol/mTOR signalling pathway

Cancer stem cells (CSCs) play a substantial role in cancer onset and recurrence. Anomalous iron and lipid metabolism have been documented in CSCs, suggesting that ferroptosis, a recently discovered form of regulated cell death characterised by lipid peroxidation, could potentially exert a significant influence on CSCs. However, the precise role of ferroptosis in gastric cancer stem cells (GCSCs) remains unknown. To address this gap, we screened ferroptosis-related genes in GCSCs using The Cancer Genome Atlas and corroborated our findings through quantitative polymerase chain reaction and western blotting. These results indicate that stearoyl-CoA desaturase (SCD1) is a key player in the regulation of ferroptosis in GCSCs. This study provides evidence that SCD1 positively regulates the transcription of squalene epoxidase (SQLE) by eliminating transcriptional inhibition of P53. This mechanism increases the cholesterol content and the elevated cholesterol regulated by SCD1 inhibits ferroptosis via the mTOR signalling pathway. Furthermore, our in vivo studies showed that SCD1 knockdown or regulation of cholesterol intake affects the stemness of GCSCs and their sensitivity to ferroptosis inducers. Thus, targeting the SCD1/squalene epoxidase/cholesterol signalling axis in conjunction with ferroptosis inducers may represent a promising therapeutic approach for the treatment of gastric cancer based on GCSCs.

Multi-omics evaluation of the prognostic value and immune signature of FCN1 in pan-cancer and its relationship with proliferation and apoptosis in acute myeloid leukemia

Background

The FCN1 gene encodes the ficolin-1 protein, implicated in the pathogenesis of various diseases, though its precise role in tumorigenesis remains elusive. This study aims to elucidate the prognostic significance, immune signature, and treatment response associated with FCN1 across diverse cancer types.

Methods

Employing multi-omics data, we conducted a comprehensive assessment, encompassing tissue-specific and single-cell-specific expression disparities, pan-cancer expression patterns, epigenetic modifications affecting FCN1 expression, and the immune microenvironment. Our investigation primarily focused on the clinical prognostic attributes, immune profiles, potential molecular mechanisms, and candidate therapeutic agents concerning FCN1 and acute myeloid leukemia (AML). Additionally, in vitro experiments were performed to scrutinize the impact of FCN1 knockdown on cell proliferation, apoptosis, and cell cycle dynamics within the AML cell line U937 and NB4.

Results

FCN1 expression exhibits widespread dysregulation across various cancers. Through both univariate and multivariate Cox regression analyses, FCN1 has been identified as an independent prognostic indicator for AML. Immunological investigations elucidate FCN1's involvement in modulating inflammatory responses within the tumor microenvironment and its correlation with treatment efficacy. Remarkably, the deletion of FCN1 influences the proliferation, apoptosis, and cell cycle dynamics of U937 cells and NB4 cells.

Conclusion

These findings underscore FCN1 as a promising pan-cancer biomarker indicative of macrophage infiltration, intimately linked with the tumor microenvironment and treatment responsiveness, and pivotal for cellular mechanisms within AML cell lines.

Identification of the cytoplasmic DNA-Sensing cGAS-STING pathway-mediated gene signatures and molecular subtypes in prostate cancer

BACKGROUND

Considering the age relevance of prostate cancer (PCa) and the involvement of the cGAS-STING pathway in aging and cancer, we aim to classify PCa into distinct molecular subtypes and identify key genes from the novel perspective of the cGAS-STING pathway. It is of significance to guide personalized intervention of cancer-targeting therapy based on genetic evidence.

METHODS

The 430 patients with PCa from the TCGA database were included. We integrated 29 key genes involved in cGAS-STING pathway and analyzed differentially expressed genes and biochemical recurrence (BCR)-free survival-related genes. The assessments of tumor stemness and heterogeneity and tumor microenvironment (TME) were conducted to reveal potential mechanisms.

RESULTS

PCa patients were classified into two distinct subtypes using AURKB, TREX1, and STAT6, and subtype 1 had a worse prognosis than subtype 2 (HR: 21.19, p < 0.001). The findings were validated in the MSKCC2010 cohort. Among subtype 1 and subtype 2, the top ten mutation genes were MUC5B, DNAH9, SLC5A10, ZNF462, USP31, SIPA1L3, PLEC, HRAS, MYOM1, and ITGB6. Gene set variation analysis revealed a high enrichment of the E2F target in subtype 1, and gene set enrichment analysis showed significant enrichment of base excision repair, cell cycle, and DNA replication in subtype 1. TME evaluation indicated that subtype 1 had a significantly higher level of T cells follicular helper and a lower level of plasma cells than subtype 2.

CONCLUSIONS

The molecular subtypes mediated by the cGAS-STING pathway and the genetic risk score may aid in identifying potentially high-risk PCa patients who may benefit from pharmacologic therapies targeting the cGAS-STING pathway.

Graphene therapy-related lncRNAs as prognostic and immune microenvironmental biomarkers in hepatocellular carcinoma

BACKGROUND

Graphene materials have the capacity to influence the tumor microenvironment and intracellular signaling responsiveness. However, the process of graphene-assisted liver cancer treatment still lacks specific biomarkers for assessing its efficacy.

METHODS

We identified graphene therapy-related lncRNAs (GTLncRNAs) through gene analysis and correlation tests. Multivariate COX and LASSO regression analyses yielded significant lncRNAs for a risk score model. We evaluated clinicopathological factors and tumor microenvironment using ssGSEA. We scrutinized the pathways of immune function, the evasion of tumor immunity, and the potential for immunotherapy. GTLncRNAs with differential expression were subjected to GO/KEGG analysis, and prospective chemotherapy drugs were discerned utilizing the pRRophetic algorithm. The prognostic model was authenticated through the examination of the Imvigor210 cohort, and an analysis of mRNA stemness was executed.

RESULTS

The researchers constructed a prognostic model based on 22 graphene therapy-related lncRNAs. Protective lncRNAs (AC010280.2, AL365361.1, and LINC01549) and negative lncRNAs (AC026412.3, AL031985.3, ELFN1-AS1, SNHG4, and EB2-AS1) were identified. Higher risk scores correlated with shorter survival. Low-risk immune pathways included Type_II_IFN_Reponse and cytolytic_activity. Subgroups differed significantly in TMB, TIDE, MDSC, exclusion, and dysfunction. Low TMB values correlated with longer survival. The high-risk subgroup showed increased sensitivity to screened compounds, and mRNAsi was higher in cancer tissue.

CONCLUSIONS

Our GTLncRNAs-based model accurately predicted survival of HCC patients and underscored the influence of graphene therapy-related genes on the tumor microenvironment. Potential treatment compounds were identified, and the mRNAsi index demonstrated prognostic value.

Common immunological and prognostic features of lung and bladder cancer via smoking-related genes: PRR11 gene as potential immunotherapeutic target

Smoking is a well-known risk factor for non-small-cell lung cancer (NSCLC) and bladder urothelial carcinoma (BLCA). Despite this, there has been no investigation into a prognostic marker based on smoking-related genes that could universally predict prognosis in these cancers and correlate with immune checkpoint therapy. This study aimed to identify smoking-related differential genes in NSCLC and BLCA, analyse their roles in patient prognosis and immune checkpoint therapy through subgroup analyses, and shed light on PRR11 as a crucial prognostic gene in both cancers. By examining PRR11 co-expressed genes, a prognostic model was constructed and its impact on immunotherapy for NSCLC and BLCA was evaluated. Molecular docking and tissue microarray analyses were conducted to explore the correlation between PRR11 and its reciprocal gene SPDL1. Additionally, miRNAs associated with PRR11 were analysed. The study confirmed a strong link between smoking-related genes, prognosis, and immune checkpoint therapy in NSCLC and BLCA. PRR11 was identified as a key smoking-associated gene that influences the efficacy of immune checkpoint therapy by modulating the stemness of these cancers. A prognostic model based on PRR11 co-expressed genes in BLCA was established and its prognostic value was validated in NSCLC. Furthermore, it was found that PRR11 regulates PDL1 via SPDL1, impacting immunotherapeutic efficacy in both cancers. The involvement of hsa-miR-200b-3p in the regulation of SPDL1 expression by PRR11 was also highlighted. Overall, the study elucidates that PRR11 modulates patient immunotherapy by influencing PDL1 expression through its interaction with SPDL1, with potential upstream regulation by hsa-miR-200b-3p.

Advancements in technology for characterizing the tumor immune microenvironment

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME. In recent years, major advances have already been made in depicting TIME at a considerably elevated degree of throughput, dimensionality and resolution, allowing dozens of markers to be labeled simultaneously, and analyzing the heterogeneity of tumour-immune infiltrates in detail at the single cell level, depicting the spatial landscape of the entire microenvironment, as well as applying artificial intelligence (AI) to interpret a large amount of complex data from TIME. In this review, we summarized emerging technologies that have made contributions to the field of TIME, and provided prospects for future research.

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy

The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response. In these subtypes, we observed mRNAsiH_ICCA subtype, the intersection between high mRNAsi and immune characteristics clustering A, had the worst prognosis. Specifically, immune characteristics clustering B (ICC_B) had high drug sensitivity in most drugs regardless of the value of mRNAsi. On the other hand, patients with low mRNAsi responded better to ten drugs including KU-55933 and NU7441, while patients with high mRNAsi might benefit from drugs like Leflunomide. By matching the specific characteristics of each combined subtype with the drug-induced cell line expression profile, we identified a group of potential therapeutic drugs that might regulate the expression of disease signature genes. We developed a feasible multiple combined typing strategy, hoping to guide therapeutic selection and promote the development of precision medicine.

Quantified pathway mutations associate epithelial-mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell carcinoma

BACKGROUND

Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape. Yet, there is a lack of score to accurately quantify pathway mutations.

MATERIAL AND METHODS

Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden (IWHMB, https://github.com/YuHongHuang-lab/IWHMB ) which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB-related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response.

RESULTS

We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures.

CONCLUSION

In summary, the novel pathway mutation scoring-IWHMB suggested that the elevated malignancy mediated by pathway mutations is a major cause of poor prognosis and immunotherapy failure in HNSCC, and is capable of identifying novel biomarkers to predict immunotherapy response.

Cuproptosis-related genes predict prognosis and trastuzumab therapeutic response in HER2-positive breast cancer

Breast cancer is the most common diagnosed cancer, the HER2-positive subtype account for 15% of all breast cancer. HER2-targeted therapy is the mainstay treatment for HER2-positive breast cancer. Cuproptosis is a novel form of programmed cell death, and is caused by mitochondrial lipoylation and destabilization of iron-sulfur proteins triggered by copper, which was considered as a key player in various biological processes. However, the roles of cuproptosis-related genes in HER2-positive breast cancer remain largely unknown. In the present study, we constructed a prognostic prediction model of HER2-positive breast cancer patients using TCGA database. Dysregulated genes for cells resistant to HER2-targeted therapy were analyzed in the GEO dataset. KEGG pathway, GO enrichment and GSEA was performed respectively. The immune landscape of DLAT was analyzed by CIBERSORT algorithm and TIDE algorithm. HER2-positive breast cancer patients with high CRGs risk score showed shorter OS. DLAT was downregulated and correlated with better survival of HER2-positive breast cancer patients (HR = 3.30, p = 0.022). High expressed DLAT was associated with resistant to HER2-targeted therapy. Knocking down DLAT with siRNA increased sensitivity of breast cancer to trastuzumab. KEGG pathway and GO enrichment of DEGs indicated that DLAT participates in various pathways correlated with organelle fission, chromosome segregation, nuclear division, hormone-mediated signaling pathway, regulation of intracellular estrogen receptor signaling pathway, condensed chromosome and PPAR signaling pathway. There was a negative correlation between TIDE and DLAT expression (r = - 0.292, p < 0.001), which means high DLAT expression is an indicator of sensitivity to immunotherapy. In conclusion, our study constructed a four CRGs signature prognostic prediction model and identified DLAT as an independent prognostic factor and associated with resistant to HER2-targeted therapy for HER2-positive breast cancer patients.

Construction and validation of a novel lysosomal signature for hepatocellular carcinoma prognosis, diagnosis, and therapeutic decision-making

Lysosomes is a well-recognized oncogenic driver and chemoresistance across variable cancer types, and has been associated with tumor invasiveness, metastasis, and poor prognosis. However, the significance of lysosomes in hepatocellular carcinoma (HCC) is not well understood. Lysosomes-related genes (LRGs) were downloaded from Genome Enrichment Analysis (GSEA) databases. Lysosome-related risk score (LRRS), including eight LRGs, was constructed via expression difference analysis (DEGs), univariate and LASSO-penalized Cox regression algorithm based on the TCGA cohort, while the ICGC cohort was obtained for signature validation. Based on GSE149614 Single-cell RNA sequencing data, model gene expression and liver tumor niche were further analyzed. Moreover, the functional enrichments, tumor microenvironment (TME), and genomic variation landscape between LRRSlow/LRRShigh subgroup were systematically investigated. A total of 15 Lysosomes-related differentially expressed genes (DELRGs) in HCC were detected, and then 10 prognosis DELRGs were screened out. Finally, the 8 optimal DELRGs (CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B) were selected to construct the LRRS prognosis signature of HCC. LRRS was considered as an independent prognostic factor and was associated with advanced clinicopathological features. LRRS also proved to be a potential marker for HCC diagnosis, especially for early-stage HCC. Then, a nomogram integrating the LRRS and clinical parameters was set up displaying great prognostic predictive performance. Moreover, patients with high LRRS showed higher tumor stemness, higher heterogeneity, and higher genomic alteration status than those in the low LRRS group and enriched in metabolism-related pathways, suggesting its underlying role in the progression and development of liver cancer. Meanwhile, the LRRS can affect the proportion of immunosuppressive cell infiltration, making it a vital immunosuppressive factor in the tumor microenvironment. Additionally, HCC patients with low LRRS were more sensitive to immunotherapy, while patients in the high LRRS group responded better to chemotherapy. Upon single-cell RNA sequencing, CLN3, GBA, and LAPTM4B were found to be specially expressed in hepatocytes, where they promoted cell progression. Finally, RT-qPCR and external datasets confirmed the mRNA expression levels of model genes. This study provided a direct links between LRRS signature and clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of lysosome in the development and treatment resistance of liver cancer, providing valuable insights into the prognosis prediction and treatment response of HCC, thereby providing valuable insights into prognostic prediction, early diagnosis, and therapeutic response of HCC.

Prognostic related gene index for predicting survival and immunotherapeutic effect of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common malignant liver tumor. It is an aggressive disease with high mortality rate. In this study, we investigated a new prognosis-related gene index (PRGI) that can predict the survival and efficacy of immunotherapy in patients with HCC. RNA-seq data and clinical data of HCC samples were obtained from the cancer genome atlas and ICGC databases. Prognosis-related genes were obtained using log-rank tests and univariate Cox proportional hazards regression. Univariate and multivariate analyses were performed on the overall survival rate of patients with prognosis-related genes and multiple clinicopathological factors, and a nomogram was constructed. A PRGI was then constructed based on least absolute shrinkage and selection operator or multivariate Cox Iterative Regression. The possible correlation between PRGI and immune cell infiltration or immunotherapy efficacy was discussed. Eight genes were identified to construct the PRGI. PRGI can predict the infiltration of immune cells into the tumor microenvironment of HCC and the response to immunotherapy. PRGI can accurately predict the survival rate of patients with HCC, reflect the immune microenvironment, and predict the efficacy of immunotherapy.

Loss of ATOH1 in Pit Cell Drives Stemness and Progression of Gastric Adenocarcinoma by Activating AKT/mTOR Signaling through GAS1

Gastric cancer stem cells (GCSCs) are self-renewing tumor cells that govern chemoresistance in gastric adenocarcinoma (GAC), whereas their regulatory mechanisms remain elusive. Here, the study aims to elucidate the role of ATOH1 in the maintenance of GCSCs. The preclinical model and GAC sample analysis indicate that ATOH1 deficiency is correlated with poor GAC prognosis and chemoresistance. ScRNA-seq reveals that ATOH1 is downregulated in the pit cells of GAC compared with those in paracarcinoma samples. Lineage tracing reveals that Atoh1 deletion strongly confers pit cell stemness. ATOH1 depletion significantly accelerates cancer stemness and chemoresistance in Tff1-CreERT2; Rosa26Tdtomato and Tff1-CreERT2; Apcfl/fl ; p53fl/fl (TcPP) mouse models and organoids. ATOH1 deficiency downregulates growth arrest-specific protein 1 (GAS1) by suppressing GAS1 promoter transcription. GAS1 forms a complex with RET, which inhibits Tyr1062 phosphorylation, and consequently activates the RET/AKT/mTOR signaling pathway by ATOH1 deficiency. Combining chemotherapy with drugs targeting AKT/mTOR signaling can overcome ATOH1 deficiency-induced chemoresistance. Moreover, it is confirmed that abnormal DNA hypermethylation induces ATOH1 deficiency. Taken together, the results demonstrate that ATOH1 loss promotes cancer stemness through the ATOH1/GAS1/RET/AKT/mTOR signaling pathway in GAC, thus providing a potential therapeutic strategy for AKT/mTOR inhibitors in GAC patients with ATOH1 deficiency.

Cross talk between tumor stemness and microenvironment for prognosis and immunotherapy of uveal melanoma

PURPOSE

Tumor stem cells have emerged as a crucial focus of investigation and a therapeutic target in the context of cancer metastasis and drug resistance. They represent a promising novel approach to address the treatment of uveal melanoma (UVM).

METHODS

According to the one-class logistic regression (OCLR) approach, we first estimated two stemness indices (mDNAsi and mRNAsi) in a cohort of UVM (n = 80). The prognostic value of stemness indices among four subtypes of UVM (subtype A-D) was investigated. Moreover, univariate Cox regression and Lasso-penalized algorithms were conducted to identify a stemness-associated signature and verify in several independent cohorts. Besides, UVM patients classified into subgroups based on the stemness-associated signature. The differences in clinical outcomes, tumor microenvironment, and probability of immunotherapeutic response were investigated further.

RESULTS

We observed that mDNAsi was significantly linked with overall survival (OS) time of UVM, but no association was discovered between mRNAsi and OS. Stratification analysis indicated that the prognostic value of mDNAsi was only limited in subtype D of UVM. Besides, we established and verified a prognostic stemness-associated gene signature which can classify UVM patients into subgroups with distinct clinical outcomes, tumor mutation, immune microenvironment, and molecular pathways. The high risk of UVM is more sensitive to immunotherapy. Finally, a well-performed nomogram was constructed to predict the mortality of UVM patients.

CONCLUSIONS

This study offers a comprehensive examination of UVM stemness characteristics. We discovered mDNAsi-associated signatures improved the prediction capacity of individualized UVM prognosis and indicated prospective targets for stemness-regulated immunotherapy. Analysis of the interaction between stemness and tumor microenvironment may shed light on combinational treatment that targets both stem cell and the tumor microenvironment.

Deep dissection of stemness-related hierarchies in hepatocellular carcinoma

BACKGROUND

Increasing evidence suggests that hepatocellular carcinoma (HCC) stem cells (LCSCs) play an essential part in HCC recurrence, metastasis, and chemotherapy and radiotherapy resistance. Multiple studies have demonstrated that stemness-related genes facilitate the progression of tumors. However, the mechanism by which stemness-related genes contribute to HCC is not well understood. Here, we aim to construct a stemness-related score (SRscores) model for deeper analysis of stemness-related genes, assisting with the prognosis and individualized treatment of HCC patients.Further, we found that the gene LPCAT1 was highly expressed in tumor tissues by immunohistochemistry, and sphere-forming assay revealed that knockdown of LPCAT1 inhibited the sphere-forming ability of hepatocellular carcinoma cells.

METHODS

We used the TCGA-LIHC dataset to screen stemness-related genes of HCC from the MSigDB database. Prognosis, tumor microenvironment, immunological checkpoints, tumor immune dysfunction, rejection, treatment sensitivity, and putative biological pathways were examined. Random forest created the SRscores model. The anti-PD-1/anti-CTLA4 immunotherapy, tumor mutational burden, medication sensitivity, and cancer stem cell index were compared between the high- and low-risk score groups. We also examined risk scores for different cell types using single-cell RNA sequencing data and correlated transcription factor activity in cancer stem cells with SRscores genes. Finally, we tested core marker expression and biological functions.

RESULTS

Patients can be divided into two subtypes (Cluster1 and Cluster2) based on the TCGA-LIHC dataset's identification of 11 stemness-related genes. Additionally, a SRscores was developed based on subtypes. Cluster2 and the group with the lowest SRscores had superior survival and immunotherapy response than Cluster1 and the group with the highest SRscores. The group with a high SRscores was significantly more enriched in classical tumor pathways than the group with a low SRscores. Multiple transcription factors and SRscores genes are correlated. The core gene LPCAT1 is highly expressed in rat liver cancer tissues and promotes tumor cell sphere formation.

CONCLUSION

A SRscores model can be utilized to predict the prognosis of HCC patients as well as their response to immunotherapy.

MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches

Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including EP300, CXCR4, WNT4, ZIC4, MEIS1, SLC8A1, NFASC, ASCL2, KIF5C, SYNGAP1, SEMA4F, ROR1, DPYSL4, ARTN, RTN4RL1, and TLX2. Our findings contribute to enhanced survival outcomes for patients with medulloblastoma. Continued research and validation efforts are needed to further refine and expand the utility of our approach in other cancer types, advancing personalized medicine in pediatric oncology.

Senescence-associated secretory phenotype constructed detrimental and beneficial subtypes and prognostic index for prostate cancer patients undergoing radical prostatectomy

BACKGROUND

Cellular senescence is growing in popularity in cancer. A dual function is played by the senescence-associated secretory phenotype (SASP) that senescent cells produce in the development of pro-inflammatory niches, tissue regeneration or destruction, senescence propagation, and malignant transformation. In this study, we conducted thorough bioinformatic analysis and meta-analysis to discover detrimental and beneficial subtypes and prognostic index for prostate cancer (PCa) patients using the experimentally confirmed SASP genes.

METHODS

We identified differentially expressed and prognosis-related SASP genes and used them to construct two molecular subtypes and risk score. Another two external cohorts were used to confirm the prognostic effect of the above subtypes and risk score and meta-analysis was further conducted. Additionally, functional analysis, tumor stemness and heterogeneity and tumor microenvironment were also evaluated. We completed analyses using software R 3.6.3 and its suitable packages. Meta-analysis was performed by software Stata 14.0.

RESULTS

Through multivariate Cox regression analysis and consensus clustering analysis, we used VGF, IGFBP3 and ANG to establish detrimental and beneficial subtypes in the TCGA cohort, which was validated through other two independent cohorts. Meta-analysis showed that detrimental SASP group had significantly higher risk of biochemical recurrence (BCR) than beneficial SASP group (HR: 2.48). Moreover, we also constructed and validated risk score based on these genes to better guide clinical practice. DNA repair, MYC target, oxidative phosphorylation, proteasome and ribosome were highly enriched in detrimental SASP group. Detrimental SASP group had significantly higher levels of B cells, CD8+ T cells, homologous recombination deficiency, loss of heterozygosity, microsatellite instability, purity, tumor mutation burden, mRNAsi, differentially methylated probes and epigenetically regulated RNA expression than beneficial SASP group. The top mutation genes between detrimental and beneficial SASP groups were SPOP, FOXA1, KMT2C, APC, BSN, DNAH17, MYH6, EPPK1, ZNF536 and ZC3H13 with statistical significance.

CONCLUSIONS

From perspective of SASP, we found detrimental and beneficial tumor subtypes which were closely associated with BCR-free survival for PCa patients, which might be important for the furture research in the field of PCa.

M1A and m7G modification-related genes are potential biomarkers for survival prognosis and for deciphering the tumor immune microenvironment in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is the most common esophageal malignancy, and RNA methylation has been reported to be involved in the tumorigenesis of ESCC. However, no study has explored methylation modifications in m¹A and m⁷G as prognostic markers for survival prediction in ESCC.

METHODS

Public gene-expression data and clinical annotation of 254 patients obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases were analyzed to identify potential consensus clusters of m¹A and m⁷G modification-related genes. The RNA-seq of 20 patients in Sun Yat-Sen University Cancer Center was used as the validation set. Following screening for relevant differentially expressed genes (DEGs) and enrichment pathways were elucidated. DEGs were used to construct risk models using the randomForest algorithm, and the prognostic role of the models was assessed by applying Kaplan-Meier analysis. Extent of immune cell infiltration, drug resistance, and response to cancer treatment among different clusters and risk groups were also evaluated.

RESULTS

Consensus clustering analysis based on m¹A and m⁷G modification patterns revealed three potential clusters. In total, 212 RNA methylation-related DEGs were identified. The methylation-associated signature consisting of 6 genes was then constructed to calculate methylation-related score (MRScore) and patients were dived into MRScore-high and MRScore-low groups. This signature has satisfied prognostic value for survival of ESCC (AUC = 0.66, 0.67, 0.64 for 2-, 3-, 4- year OS), and has satisfied performance in the validation SYSUCC cohort (AUC = 0.66 for 2- and 3-year OS). Significant correlation between m¹A and m⁷G modification-related genes and immune cell infiltration, and drug resistance was also observed.

CONCLUSIONS

Transcriptomic prognostic signatures based on m¹A and m⁷G modification-related genes are closely associated with immune cell infiltration in ESCC patients and have important correlations with the therapeutic sensitivity of multiple chemotherapeutic agents.

TM4SF1 upregulates MYH9 to activate the NOTCH pathway to promote cancer stemness and lenvatinib resistance in HCC

TM4SF1, a member of the transmembrane 4 superfamily, is crucial for both healthy and malignant human tissues. The significant function of TM4SF1 in the incidence and progression of cancer has been widely recognized in recent years. Although some achievements have been made in the study of TM4SF1, the effect of TM4SF1 on cancer stemness in hepatocellular carcinoma (HCC) and its molecular basis are yet to be reported. We found through abundant in vitro and in vivo experiments which the expression of TM4SF1 was positively correlated with the progression and cancer stemness of HCC. We identified the downstream protein MYH9 of TM4SF1 and its final regulatory target NOTCH pathway using bioinformatics analysis and protein mass spectrometry. We cultivated a Lenvatinib-resistant strain from HCC cells to examine the relationship between cancer stemness and tumor drug resistance. The study confirmed that TM4SF1 could regulate the NOTCH pathway by upregulating MYH9, thus promoting cancer stemness and Lenvatinib resistance in HCC. This study not only provided a new idea for the pathogenesis of HCC but also confirmed that TM4SF1 might become a new intervention point to improve the clinical efficacy of Lenvatinib in treating HCC.

Cuproptosis signature and PLCD3 predicts immune infiltration and drug responses in osteosarcoma

Osteosarcoma (OS) is a cancer that is frequently found in children and adolescents and has made little improvement in terms of prognosis in recent years. A recently discovered type of programmed cell death called cuproptosis is mediated by copper ions and the tricarboxylic acid (TCA) cycle. The expression patterns, roles, and prognostic and predictive capabilities of the cuproptosis regulating genes were investigated in this work. TARGET and GEO provided transcriptional profiling of OS. To find different cuproptosis gene expression patterns, consensus clustering was used. To identify hub genes linked to cuproptosis, differential expression (DE) and weighted gene co-expression network analysis (WGCNA) were used. Cox regression and Random Survival Forest were used to build an evaluation model for prognosis. For various clusters/subgroups, GSVA, mRNAsi, and other immune infiltration experiments were carried out. The drug-responsive study was carried out by the Oncopredict algorithm. Cuproptosis genes displayed two unique patterns of expression, and high expression of FDX1 was associated with a poor outcome in OS patients. The TCA cycle and other tumor-promoting pathways were validated by the functional study, and activation of the cuproptosis genes may also be connected with immunosuppressive state. The robust survival prediction ability of a five-gene prognostic model was verified. This rating method also took stemness and immunosuppressive characteristics into account. Additionally, it can be associated with a higher sensitivity to medications that block PI3K/AKT/mTOR signaling as well as numerous chemoresistances. U2OS cell migration and proliferation may be encouraged by PLCD3. The relevance of PLCD3 in immunotherapy prediction was verified. The prognostic significance, expressing patterns, and functions of cuproptosis in OS were revealed in this work on a preliminary basis. The cuproptosis-related scoring model worked well for predicting prognosis and chemoresistance.

Characterization of lipid droplet metabolism patterns identified prognosis and tumor microenvironment infiltration in gastric cancer

Background

Gastric cancer is one of the common malignant tumors of the digestive system worldwide, posing a serious threat to human health. A growing number of studies have demonstrated the important role that lipid droplets play in promoting cancer progression. However, few studies have systematically evaluated the role of lipid droplet metabolism-related genes (LDMRGs) in patients with gastric cancer.

Methods

We identified two distinct molecular subtypes in the TCGA-STAD cohort based on LDMRGs expression. We then constructed risk prediction scoring models in the TCGA-STAD cohort by lasso regression analysis and validated the model with the GSE15459 and GSE66229 cohorts. Moreover, we constructed a nomogram prediction model by cox regression analysis and evaluated the predictive efficacy of the model by various methods in STAD. Finally, we identified the key gene in LDMRGs, ABCA1, and performed a systematic multi-omics analysis in gastric cancer.

Results

Two molecular subtypes were identified based on LDMRGs expression with different survival prognosis and immune infiltration levels. lasso regression models were effective in predicting overall survival (OS) of gastric cancer patients at 1, 3 and 5 years and were validated in the GEO database with consistent results. The nomogram prediction model incorporated additional clinical factors and prognostic molecules to improve the prognostic predictive value of the current TNM staging system. ABCA1 was identified as a key gene in LDMRGs and multi-omics analysis showed a strong correlation between ABCA1 and the prognosis and immune status of patients with gastric cancer.

Conclusion

This study reveals the characteristics and possible underlying mechanisms of LDMRGs in gastric cancer, contributing to the identification of new prognostic biomarkers and providing a basis for future research.

Identification of Homoharringtonine as a potent inhibitor of glioblastoma cell proliferation and migration

We previously demonstrated that Annexin A2 (ANXA2) is a pivotal mediator of the pro-oncogenic features displayed by glioblastoma (GBM) tumors, the deadliest adult brain malignancies, being involved in cell stemness, proliferation and invasion, thus negatively impacting patient prognosis. Based on these results, we hypothesized that compounds able to revert ANXA2-dependent transcriptional features could be exploited as reliable treatments to inhibit GBM cell aggressiveness by hampering their proliferative and migratory potential. Transcriptional signatures obtained by the modulation of ANXA2 activity/levels were functionally mapped through the QUADrATiC bioinformatic tool for compound identification. Selected compounds were screened by cell proliferation and migration assays in primary GBM cells, and we identified Homoharringtonine (HHT) as a potent inhibitor of GBM cell motility and proliferation, without affecting their viability. A further molecular characterization of the effects displayed by HHT, confirmed its ability to inhibit a transcriptional program involved in cell migration and invasion. Moreover, we demonstrated that the multiple antitumoral effects displayed by HHT are correlated to the inhibition of a platelet derived growth factor receptor α (PDGFRα)-dependent intracellular signaling through the impairment of Signal transducer and activator of transcription 3 (STAT3) and Ras homolog family member A (RhoA) axes. Our results demonstrate that HHT may act as a potent inhibitor of cancer cell proliferation and invasion in GBM, by hampering multiple PDGFRα-dependent oncogenic signals transduced through the STAT3 and RhoA intracellular components, finally suggesting its potential transferability for achieving an effective impairment of peculiar GBM hallmarks.

ARNTL2 is an indicator of poor prognosis, promotes epithelial-to-mesenchymal transition and inhibits ferroptosis in lung adenocarcinoma

OBJECTIVES

ARNTL2, as a circadian transcription factor, has been recently proposed to play an important role in a variety of tumors. however, the role of ARNTL2 in lung carcinogenesis and progression remains unclear. The purpose of this study was to investigate the effect of ARNTL2 on the clinical characteristics and prognosis of lung adenocarcinoma and to explore the relationship between ARNTL2 and EMT, ferroptosis in lung adenocarcinoma.

METHODS

The Cancer Genome Atlas (TCGA) database's multi-omics data were downloaded using the Xena browser. Based on the expression levels of ARNTL2, patients with lung adenocarcinoma from TCGA were divided into two groups: those with high ARNTL2 expression and those with low ARNTL2 expression. ARNTL2 was studied for its effects on lung adenocarcinoma's clinicopathological, genomic, and immunological characteristics. Furthermore, in vivo and in vitro assays were used to confirm the impact of ARNLT2 knockdown on lung adenocarcinoma cells.

RESULTS

We found ARNTL2 is highly expressed in lung adenocarcinoma and was an independent predictor of a poor prognosis in patients with lung adenocarcinoma. In addition, we demonstrated that knockdown of ARNTL2 promoted ferroptosis, inhibited EMT, cell proliferation, migration and invasion in lung adenocarcinoma. In contrast, overexpressing ARNTL2 yielded the opposite results.

CONCLUSIONS

ARNTL2 is an independent unfavorable prognostic factor for lung adenocarcinoma. It plays a facilitating role in the development of lung adenocarcinoma, especially in promoting EMT and inhibiting ferroptosis, revealing that ARNTL2 may be a potential biomarker for lung adenocarcinoma.

Methylation-based reclassification and risk stratification of skull-base chordomas

BACKGROUND

Skull-base chordomas are rare malignant bone cancers originating from the remnant of the notochord. Survival is variable, and clinical or molecular factors cannot reliably predict their outcomes. This study therefore identified epigenetic subtypes that defined new chordoma epigenetic profiles and their corresponding characteristics.

METHODS

Methylation profiles of 46 chordoma-resected neoplasms between 2008 and 2014, along with clinical information, were collected. K-means consensus clustering and principal component analysis were used to identify and validate the clusters. Single-sample gene set enrichment analysis, methylCIBERSORT algorithm, and copy number analysis were used to identify the characteristics of the clusters.

RESULTS

Unsupervised clustering analysis confirmed two clusters with a progression-free survival difference. Gene set enrichment analysis indicated that the early and late estrogen response pathways and the hypoxia pathway were activated whereas the inflammatory and interferon gamma responses were suppressed. Forty-six potential therapeutic targets corresponding to differentially methylated sites were identified from chordoma patients. Subgroups with a worse outcome were characterized by low immune cell infiltration, higher tumor purity, and higher stemness indices. Moreover, copy number amplifications mostly occurred in cluster 1 tumors and the high-risk group. Additionally, the presence of a CCNE1 deletion was exclusively found in the group of chordoma patients with better outcome, whereas RB1 and CDKN2A/2B deletions were mainly found in the group of chordoma patients with worse outcome.

CONCLUSIONS

Chordoma prognostic epigenetic subtypes were identified, and their corresponding characteristics were found to be variable.

Degree of stemness predicts micro-environmental response and clinical outcomes of diffuse large B-cell lymphoma and identifies a potential targeted therapy

Some cells within a diffuse large B-cell lymphoma (DLBCL) have the genotype of a stem cell, the proportion of which is termed degree of stemness. We interrogated correlations between the degree of stemness with immune and stromal cell scores and clinical outcomes in persons with DLBCL. We evaluated gene expression data on 1,398 subjects from Gene Expression Omnibus to calculate the degree of stemness. Subjects were classified into low- and high-stemness cohorts based on restricted cubic spline plots. Weighted gene co-expression network analysis (WGCNA) was used to screen for stemness-related genes. Immune and stromal scores correlated with the degree of stemness (both P < 0.001). A high degree of stemness correlated with a shorter progression-free survival (PFS; Hazard Ratio [HR; 95% Confidence Interval [CI] =1.90 (1.37, 2.64; P < 0.001) and a shorter survival (HR = 2.29 (1.53, 3.44; P < 0.001). CDC7 expression correlated with the degree of stemness, and CDC7-inhibitors significantly increased apoptosis (P < 0.01), the proportion of cells in G₁ phase (P < 0.01), and inhibited lymphoma growth in a mice xenograft model (P = 0.04). Our data indicate correlations between the degree of stemness, immune and stromal scores, PFS, and survival. These data will improve the prediction of therapy outcomes in DLBCL and suggest potential new therapies.

The Clinical Value of Blood miR-654-5p, miR-126, miR-10b, and miR-144 in the Diagnosis of Colorectal Cancer

Colorectal cancer (CRC) is the third cause of cancer-related death and the fourth most frequently diagnosed cancer across the globe. The objective of this study is to obtain novel and effective diagnostic markers to enrich CRC diagnosis methods. Herein, exosomal miRNA expression data of CRC and normal blood were subjected to XGBoost algorithm, and 5 miRNAs related to CRC diagnosis were primarily confirmed. Then multilayer perceptron (MLP) classifiers were constructed based on different subsets. Via integrated feature selection (IFS), we noticed that the MLP classifier constructed by the first four miRNAs (miR-654-5p, miR-126, miR-10b, and miR-144) had the highest Matthews correlation coefficient (MCC). Subsequently, principal component analysis (PCA) for dimensionality reduction was performed on samples based on the miR-654-5p, miR-126, miR-10b, and miR-144 expression data. The signature based on these four feature miRNAs, as the analysis indicated, could effectively distinguish CRC samples from normal samples. Further, we extracted the exosomes from clinical blood samples and applied qRT-PCR analysis, which revealed that the expression of these four feature miRNAs was in the trend of that in the test set. Collectively, these four feature miRNAs might be tumor biomarkers in the serum, and our study offers innovative thinking on early-stage CRC diagnosis.

Crosstalk of Oxidative Phosphorylation-Related Subtypes, Establishment of a Prognostic Signature and Immune Infiltration Characteristics in Colorectal Adenocarcinoma

Oxidative phosphorylation (OXPHOS) is an emerging target in cancer therapy. However, the prognostic signature of OXPHOS in colorectal adenocarcinoma (COAD) remains non-existent. We comprehensively investigated the expression pattern of OXPHOS-related genes (ORGs) in COAD from public databases. Based on four ORGs, an OXPHOS-related prognostic signature was established in which COAD patients were assigned different risk scores and classified into two different risk groups. It was observed that the low-risk group had a better prognosis but lower immune activities including immune cells and immune-related function in the tumor microenvironment. Combining with relevant clinical features, a nomogram for clinical application was also established. Receiver operating characteristic (ROC) and calibration curves were constructed to demonstrate the predictive ability of this risk signature. Moreover, a higher risk score was significantly positively correlated with higher tumor mutation burden (TMB) and generally higher gene expression of immune checkpoint, N6-methyladenosine (m6A) RNA methylation regulators and mismatch repair (MMR) related proteins. The results also indicated that the high-risk group was more sensitive to immunotherapy and certain chemotherapy drugs. In conclusion, OXPHOS-related prognostic signature can be utilized to better understand the roles of ORGs and offer new perspectives for clinical prognosis and personalized treatment.

An integrative analysis of DNA methylation and gene expression to predict lung adenocarcinoma prognosis

Background: Abnormal DNA methylation of gene promoters is an important feature in lung adenocarcinoma (LUAD). However, the prognostic value of DNA methylation remains to be further explored. Objectives. We sought to explore DNA methylation characteristics and develop a quantifiable criterion related to DNA methylation to improve survival prediction for LUAD patients.

Methods: Illumina Human Methylation450K array data, level 3 RNA-seq data and corresponding clinical information were obtained from TCGA. Cox regression analysis and the Akaike information criterion were used to construct the best-prognosis methylation signature. Receiver operating characteristic curve analysis was used to validate the prognostic ability of the DNA methylation-related feature score. qPCR was used to measure the transcription levels of the identified genes upon methylation.

Results: We identified a set of DNA methylation features composed of 11 genes (MYEOV, KCNU1, SLC27A6, NEUROD4, HMGB4, TACR3, GABRA5, TRPM8, NLRP13, EDN3 and SLC34A1). The feature score, calculated based on DNA methylation features, was independent of tumor recurrence and TNM stage in predicting overall survival. Of note, the combination of this feature score and TNM stage provided a better overall survival prediction than either of them individually. The transcription levels of all the hypermethylated genes were significantly increased after demethylation, and the expression levels of 3 hypomethylated proteins were significantly higher in tumor tissues than in normal tissues, as indicated by immunohistochemistry data from the Human Protein Atlas. Our results suggested that these identified genes with prognostic features were regulated by DNA methylation of their promoters.

Conclusion: Our studies demonstrated the potential application of DNA methylation markers in the prognosis of LUAD.

Identified Gefitinib Metabolism-Related lncRNAs can be Applied to Predict Prognosis, Tumor Microenvironment, and Drug Sensitivity in Non-Small Cell Lung Cancer

Gefitinib has shown promising efficacy in the treatment of patients with locally advanced or metastatic EGFR-mutated non-small cell lung cancer (NSCLC). Molecular biomarkers for gefitinib metabolism-related lncRNAs have not yet been elucidated. Here, we downloaded relevant genes and matched them to relevant lncRNAs. We then used univariate, LASSO, and multivariate regression to screen for significant genes to construct prognostic models. We investigated TME and drug sensitivity by risk score data. All lncRNAs with differential expression were selected for GO/KEGG analysis. Imvigor210 cohort was used to validate the value of the prognostic model. Finally, we performed a stemness indices difference analysis. lncRNA-constructed prognostic models were significant in the high-risk and low-risk subgroups. Immune pathways were identified in both groups at low risk. The higher the risk score the greater the value of exclusion, MDSC, and CAF. PRRophetic algorithm screened a total of 58 compounds. In conclusion, the prognostic model we constructed can accurately predict OS in NSCLC patients. Two groups of low-risk immune pathways are beneficial to patients. Gefitinib metabolism was again validated to be related to cytochrome P450 and lipid metabolism. Finally, drugs that might be used to treat NSCLC patients were screened.

Integration of immunohistochemistry, RNA sequencing, and multiplex ligation-dependent probe amplification for molecular classification of pediatric medulloblastoma

BACKGROUND

Medulloblastoma (MB) is commonly classified into four molecular groups, that is, WNT, SHH, group 3, and group 4, for prognostic and therapeutic purposes.

METHODS

Here we applied immunohistochemistry (IHC) and RNA sequencing (RNA-seq) for the molecular classification of MB, and utilized multiplex ligation-dependent probe amplification (MLPA) to determine chromosomal alterations and specific gene amplifications.

RESULTS

We retrospectively enrolled 37 pediatric MB patients. Twenty-three had genomic material available for gene/RNA analysis. For IHC, β-catenin, GAB1, and YAP were the biomarkers to segregate MB into three subgroups, WNT (1/23), SHH (5/23), and non-WNT/non-SHH (17/23). However, four cases (17.3%) were found to be misclassified after analysis by RNA-seq. The result of MLPA revealed two group 3 tumors carrying MYC amplification, and three SHH tumors harboring MYCN amplification. While IHC provided rapid subgroup stratification, it might result in incorrect subgrouping. Thus, validation of the IHC result with genomic data analysis by RNA-seq or other tools would be preferred. In addition, MLPA can detect important genetic alterations and is helpful for the identifications of high-risk patients.

CONCLUSIONS

Our study revealed that integration of these diagnostic tools can provide a precise and timely classification of MB, optimizing an individualized, risk-directed postoperative adjuvant therapy for these patients. This workflow can be applied in a countrywide fashion to guide future clinical trials for patients with MB.

xCT contributes to colorectal cancer tumorigenesis through upregulation of the MELK oncogene and activation of the AKT/mTOR cascade

Colorectal cancer (CRC) is one of the most commonly diagnosed and deadly malignant tumors globally, and its occurrence and progression are closely related to the poor histological features and complex molecular characteristics among patients. It is urgent to identify specific biomarkers for effective treatment of CRC. In this study, we performed comprehensive experiments to validate the role of xCT expression in CRC tumorigenesis and stemness and confirmed xCT knockdown significantly suppressed the proliferation, migration, and stemness of CRC cells in vitro and effectively inhibited CRC tumorigenesis and metastasis in vivo. In addition, bioinformatic analysis and luciferase assays were used to identify E2F1 as a critical upstream transcription factor of SLC7A11 (the gene encoding for xCT) that facilitated CRC progression and cell stemness. Subsequent RNA sequencing, western blotting, rescue assay, and immunofluorescence assays revealed MELK directly co-expressed with xCT in CRC cells, and its upregulation significantly attenuated E2F1/xCT-mediated tumorigenesis and stemness in CRC. Further molecular mechanism exploration confirmed that xCT knockdown may exert an antitumor effect by controlling the activation of MELK-mediated Akt/mTOR signaling. Erastin, a specific inhibitor of xCT, was also proven to effectively inhibit CRC tumorigenesis and cell stemness. Altogether, our study showed that E2F1/xCT is a promising therapeutic target of CRC that promotes tumorigenesis and cell stemness. Erastin is also an effective antitumoral agent for CRC.

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

Pyroptosis-related lncRNAs are potential biomarkers for predicting prognoses and immune responses in patients with UCEC

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease globally, and there is no unified prognostic signature at present. In our study, two clusters were identified. Cluster 1 showed better prognosis and higher infiltration level, such as tumor microenvironment (TME), tumor mutation burden (TMB), and immune checkpoint genes expression. Gene set enrichment analysis (GSEA) indicated that some tumor-related pathways and immune-associated pathways were exposed. What is more, six pyroptosis-related long noncoding RNAs (lncRNAs) (PRLs) were applied to establish a prognostic signature through multiple Cox regression analysis. In both training and testing sets, patients with higher risk score had poorer survival than patients with low risk. The area under the curve (AUC) of receiver operating characteristic (ROC) curves performed that the survival probability was better in people with lower risk score. Mechanism analysis revealed that high risk score was correlated with reduced immune infiltration and T cells exhaustion, matching the definition of an "immune-desert" phenotype. Patients with lower risk score were characterized by higher immune checkpoint gene expression and TMB and have a sensitive response to immunotherapy and chemotherapy compared with patients with high risk score. The signature has accurate prediction ability of UCEC and is a promising therapeutic target to improve the effect of immunotherapy.

Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a frequently diagnosed cancer type, and many patients have already reached an advanced stage when diagnosed. Thus, it is crucial to develop a novel and efficient approach to diagnose and classify lung adenocarcinoma at an early stage. In our study, we combined in silico analysis and machine learning to develop a new five-gene–based diagnosis strategy, which was further verified in independent cohorts and in vitro experiments. Considering the heterogeneity in cancer, we used the MATH (mutant-allele tumor heterogeneity) algorithm to divide patients with early-stage LUAD into two groups (C1 and C2). Specifically, patients in C2 had lower intratumor heterogeneity and higher abundance of immune cells (including B cell, CD4 T cell, CD8 T cell, macrophage, dendritic cell, and neutrophil). In addition, patients in C2 had a higher likelihood of immunotherapy response and overall survival advantage than patients in C1. Combined drug sensitivity analysis (CTRP/PRISM/CMap/GDSC) revealed that BI-2536 might serve as a new therapeutic compound for patients in C1. In order to realize the application value of our study, we constructed the classifier (to classify early-stage LUAD patients into C1 or C2 groups) with multiple machine learning and bioinformatic analyses. The 21-gene–based classification model showed high accuracy and strong generalization ability, and it was verified in four independent validation cohorts. In summary, our research provided a new strategy for clinicians to make a quick preliminary assisting diagnosis of early-stage LUAD and make patient classification at the intratumor heterogeneity level. All data, codes, and study processes have been deposited to Github and are available online.

mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes

Background

Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown.

Methods

Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC.

Results

We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients.

Conclusions

These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.