An absolute human stemness index associated with oncogenic dedifferentiation

Multi-omics analysis unraveling stemness features associated with oncogenic dedifferentiation in 12 cancers

Tumorigenesis is typically accompanied by cellular dedifferentiation and the acquisition of stem cell-like attributes. However, few studies have comprehensively evaluated the putative relationships between these characteristics and various cancers. Here, we integrated gene expression and DNA methylation quantitative trait loci (cis-eQTL and cis-mQTL) data from the blood to perform multi-omics Mendelian randomization analysis. Our analyses revealed 967 stem cell-associated genes (P<0.05) and 11,262 methylation sites (P<0.01) significantly related to 12 cancers. SMAD7 (cg14321542) in colon cancer, IGF2 (cg13508136) in prostate cancer, and FADS1 (cg07005513) in rectal cancer were prioritized as candidate causal genes and regulatory elements. Notably, using cis-eQTL data from the corresponding tissue sites, we detected 16 stem cell-associated genes dramatically causally associated with six cancers (FDR<0.2). The gene THBS3 was particularly common in both blood and stomach tissues and exhibited prognostic significance. Furthermore, it was markedly associated with one microbial metabolic pathway and four immunophenotypes. Functional validation using the ECC12 gastric cancer cell line revealed that the inhibition of its expression could accelerate oxidative phosphorylation and reactive oxygen species production, reduce clonal proliferation ability, and promote the apoptosis of stomach tumor cells. Additionally, based on spatial transcriptomic data from gastrointestinal cancers, the results demonstrated the clusters enriched with the most stem cell-associated genes exhibited significantly enhanced tumor-promoting potency, and the THBS3-expressing cells displayed suppressed oxidative phosphorylation. Overall, this study enhances our understanding of tumorigenic mechanisms and aids in the identification of therapeutic targets.

Identification of exosome-related genes associated with prognosis and immune infiltration features in pancreatic cancer

BACKGROUND

This study is designed to explore the prognostic significance of exosome-related genes (ERGs) and their impact on the the tumor microenvironment (TME) of pancreatic cancer.

METHODS

Transcriptomic data alongside clinical details of patients with PC were retrieved from both The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) repository. A collection of 121 exosome-associated genes were obtained from the ExoBCD database. For constructing a risk scoring model, the absolute shrinkage and selection operator (LASSO) regression method was employed. Gene set enrichment and variance analyses were facilitated by the clusterProfiler and GSVA R software tools. Additionally, CIBERSORT was used to estimate immune cell infiltration levels. Lastly, the TIDE algorithm was leveraged to evaluate the connection between gene expression and drug sensitivity. A series of experiments were used to verify the role of DLGAP5 in PC.

RESULTS

Two unique molecular clusters were uncovered, and our analysis revealed a connection between ERG dysregulation across multiple layers and patient demographic, histopathological attributes, prognosis, as well as immune cell infiltration patterns within the TME. An ERG_score was developed for forecasting overall survival and its predictive capacity was confirmed in PC cases. A precise nomogram was established to enhance the clinical utility of the ERG_score. Patients in the low-risk group exhibited higher immune and ESTIMATE scores than that in the high-risk group, displaying an improved overall survival (OS). The ERG_score was associated with cancer stem cell (CSC) index and drug sensitivity. Crucial evaluations of ERGs illuminated the significance of DLGAP5, emphasizing its expression in PC and its contributory role in tumor growth stimulation.

CONCLUSIONS

Our investigation reveals a correlation between the exosome-related risk assessment signature and the survival outcome as well as immune cell infiltration in patients with PC. This finding potentially paves the way for enhanced therapeutic strategies for PC.

Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-the-art predictive computational methods have facilitated the prediction of cancer stemness, there remains a lack of efficient resources to accommodate various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at both bulk and single-cell levels. This resource integrates eight robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five distinct aspects: identifying the signature genes of cancer stemness; exploring the associations with cancer hallmarks and cellular states; exploring the associations with immune response and the communications with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding downstream functional interpretation, including immune response and cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.

Sesamolin serves as an MYH14 inhibitor to sensitize endometrial cancer to chemotherapy and endocrine therapy via suppressing MYH9/GSK3β/β-catenin signaling

BACKGROUND

Endometrial cancer (EC) is one of the most common gynecological cancers. Herein, we aimed to define the role of specific myosin family members in EC because this protein family is involved in the progression of various cancers.

METHODS

Bioinformatics analyses were performed to reveal EC patients' prognosis-associated genes in patients with EC. Furthermore, colony formation, immunofluorescence, cell counting kit 8, wound healing, and transwell assays as well as coimmunoprecipitation, cycloheximide chase, luciferase reporter, and cellular thermal shift assays were performed to functionally and mechanistically analyze human EC samples, cell lines, and a mouse model, respectively.

RESULTS

Machine learning techniques identified MYH14, a member of the myosin family, as the prognosis-associated gene in patients with EC. Furthermore, bioinformatics analyses based on public databases showed that MYH14 was associated with EC chemoresistance. Moreover, immunohistochemistry validated MYH14 upregulation in EC cases compared with that in normal controls and confirmed that MYH14 was an independent and unfavorable prognostic indicator of EC. MYH14 impaired cell sensitivity to carboplatin, paclitaxel, and progesterone, and increased cell proliferation and metastasis in EC. The mechanistic study showed that MYH14 interacted with MYH9 and impaired GSK3β-mediated β-catenin ubiquitination and degradation, thus facilitating the Wnt/β-catenin signaling pathway and epithelial-mesenchymal transition. Sesamolin, a natural compound extracted from Sesamum indicum (L.), directly targeted MYH14 and attenuated EC progression. Additionally, the compound disrupted the interplay between MYH14 and MYH9 and repressed MYH9-regulated Wnt/β-catenin signaling. The in vivo study further verified sesamolin as a therapeutic drug without side effects.

CONCLUSIONS

Herein, we identified that EC prognosis-associated MYH14 was independently responsible for poor overall survival time of patients, and it augmented EC progression by activating Wnt/β-catenin signaling. Targeting MYH14 by sesamolin, a cytotoxicity-based approach, can be applied synergistically with chemotherapy and endocrine therapy to eventually mitigate EC development. This study emphasizes MYH14 as a potential target and sesamolin as a valuable natural drug for EC therapy.

Quantitative Modeling of Stemness in Single-Cell RNA Sequencing Data: A Nonlinear One-Class Support Vector Machine Method

Intratumoral heterogeneity and the presence of cancer stem cells are challenging issues in cancer therapy. An appropriate quantification of the stemness of individual cells for assessing the potential for self-renewal and differentiation from the cell of origin can define a measurement for quantifying different cell states, which is important in understanding the dynamics of cancer evolution, and might further provide possible targeted therapies aimed at tumor stem cells. Nevertheless, it is usually difficult to quantify the stemness of a cell based on molecular information associated with the cell. In this study, we proposed a stemness definition method with one-class Hadamard kernel support vector machine (OCHSVM) based on single-cell RNA sequencing (scRNA-seq) data. Applications of the proposed OCHSVM stemness are assessed by various data sets, including preimplantation embryo cells, induced pluripotent stem cells, or tumor cells. We further compared the OCHSVM model with state-of-the-art methods CytoTRACE, one-class logistic regression, or one-class SVM methods with different kernels. The computational results demonstrate that the OCHSVM method is more suitable for stemness identification using scRNA-seq data.

An individualized stemness-related signature to predict prognosis and immunotherapy responses for gastric cancer using single-cell and bulk tissue transcriptomes

BACKGROUND

Currently, many stemness-related signatures have been developed for gastric cancer (GC) to predict prognosis and immunotherapy outcomes. However, due to batch effects, these signatures cannot accurately analyze patients one by one, rendering them impractical in real clinical scenarios. Therefore, we aimed to develop an individualized and clinically applicable signature based on GC stemness.

METHODS

Malignant epithelial cells from single-cell RNA-Seq data of GC were used to identify stemness-related signature genes based on the CytoTRACE score. Using two bulk tissue datasets as training data, the enrichment scores of the signature genes were applied to classify samples into two subtypes. Then, using the identified subtypes as criteria, we developed an individualized stemness-related signature based on the within-sample relative expression orderings of genes.

RESULTS

We identified 175 stemness-related signature genes, which exhibited significantly higher AUCell scores in poorly differentiated GCs compared to differentiated GCs. In training datasets, GC samples were classified into two subtypes with significantly different survival times and genomic characteristics. Utilizing the two subtypes, an individualized signature was constructed containing 47 gene pairs. In four independent testing datasets, GC samples classified as high risk exhibited significantly shorter survival times, higher infiltration of M2 macrophages, and lower immune responses compared to low-risk samples. Moreover, the potential therapeutic targets and corresponding drugs were identified for the high-risk group, such as CD248 targeted by ontuxizumab.

CONCLUSIONS

We developed an individualized stemness-related signature, which can accurately predict the prognosis and efficacy of immunotherapy for each GC sample.

Phellopterin attenuates ovarian cancer proliferation and chemoresistance by inhibiting the PU.1/CLEC5A/PI3K-AKT feedback loop

Ovarian cancer is known as the second leading cause of gynecologic cancer-associated deaths in women worldwide. Developing new and effective compounds to alleviate chemoresistance is an urgent priority in ovarian cancer. Here, we aimed to reveal the biological function and underlying mechanisms of phellopterin, a naturally sourced ingredient of Angelica dahurica, in ovarian cancer progression as well as evaluate the therapeutic potential of phellopterin in ovarian cancer patients. In this investigation, we found that phellopterin mitigated DNA replication and induced cell cycle arrest, apoptosis, and DNA damage, attenuating cell proliferation and chemoresistance of ovarian cancer. Interestingly, bioinformatics analyses of data from our RNA sequencing and The Cancer Genome Atlas ovarian cancer dataset suggested that phellopterin presented anti-cancer activities in ovarian cancer cells by modulating signals affecting ovarian cancer progression and identified phellopterin as a potential compound in improving ovarian cancer patients' prognosis. In addition, the C-Type Lectin Domain Containing 5A (CLEC5A) was demonstrated as a downstream effector of phellopterin and involved in a positive PU.1/CLEC5A/PI3K-AKT feedback loop. Interestingly, phellopterin might inactivate the positive feedback circuit to suppress ovarian cancer progression. Collectively, our investigation revealed that phellopterin mitigated ovarian cancer proliferation and chemoresistance through suppressing the PU.1/CLEC5A/PI3K-AKT feedback loop, and predicted phellopterin as a new and effective cytotoxic drug and CLEC5A as a potential target for the treatment of ovarian cancer.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

MTHFR act as a potential cancer biomarker in immune checkpoints blockades, heterogeneity, tumor microenvironment and immune infiltration

PURPOSE

To evaluate the role and landscape of 5-10-Methylenetetrahydrofolate reductase (MTHFR) to immune infiltration, tumor microenvironment, heterogeneity, immune checkpoints blockades, prognostic significance across cancer types.

METHODS

Data sets of genomic, transcriptomic and clinic features of MTHFR across > 60,000 patients and up to 44 cancer types were comprehensively analyzed using R software.

RESULTS

Expression of MTHFR gene is significantly lower in 17 tumors and correlated with overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI) in specific tumors. Gene alterations of MTHFR are observed significant differences across tumor types. Expression of MTHFR is negatively correlated with the stemness index (mDNAsi, mRNAsi, DMPsi, ENHsi, EREG-mDNAsi and EREG-mRNAsi) in the most cancers. MTHFR showed significantly correlated with 67 types of immune cell infiltration scores in 44 cancer types by XCELL algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis are conducted to show the core tumor mechanism and biological process. Correlations between MTHFR and biomarkers of heterogeneity (MSI, TMB, MATH, HRD, LOH, Neoantigen, ploidy and purity) are also significant in specific tumors. MTHFR is significantly positively correlated with biomarkers of immune related genes (CD19, CD274, CD80, CD86) and mismatched repair genes (MLH1, PMS2, MSH2, MSH6, EPCAM, MLH3, PMS1, EXO1) in most cancer types. Receiver Operating Characteristics (ROC) analyses show MTHFR could act as a potential biomarker in anti-PD-1 (nivolumab to melanoma) and anti-CTLA4 (ipilimumab to melanoma) group of ontreatment, in anti-PD-1 (pembrolizumab to melanoma) group of pretreatment. Two immunohistochemistry antibodies HPA076180 and HPA077255 are verified in 20 types of tumor and could be used to detect the expression of MTHFR efficiently in clinic.

CONCLUSIONS

MTHFR could predict the response of immune checkpoints blockades, heterogeneity, tumor microenvironment and immune infiltration.

Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction

There is no robust genomic signature to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). It was known that clonal heterogeneity was closely associated to tumour progression and prognosis prediction. Herein, using stage I patients from The Cancer Genome Atlas, we identified the clonal/subclonal events of each gene and preselected a set of genes with prognosis-specific mutation patterns based on a robust published transcriptomic prognostic signature. Subsequently, we constructed a mutational prognostic signature (MPS), whose prognostic performance was independently validated in two datasets of stage I samples. The predicted high-risk patients had significantly higher immune cell infiltration, along with higher expression of cytotoxic and immune checkpoint genes, and an integrated dataset with 88 samples confirmed that high-risk patients could benefit from immunotherapy. The developed MPS can identify the high-risk patients with stage I LUAD and improve individualised treatment planning of high-risk patients who might benefit from immunotherapy. KEY MESSAGES: We creatively developed a prognostic signature (57-MPS) based on clonal diversity. The high-risk samples displayed an underlying immunosuppressive mechanism. 57-MPS improved the predictive performance of PD-L1 for immunotherapy.

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

Background

Breast cancer is a big threat to the women across the world with substantial morbidity and mortality. The pressing matter of our study is to establish a prognostic gene model for breast cancer based on mRNAsi for predicting patient's prognostic survival.

Methods

From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded the expression profiles of genes in breast cancer. On the basis of one-class logistic regression (OCLR) machine learning algorithm, mRNAsi of samples was calculated. Kaplan-Meier (K-M) and Kruskal-Wallis (K-W) tests were utilized for the assessment of the connection between mRNAsi and clinicopathological variables of the samples. As for the analysis on the correlation between mRNAsi and immune infiltration, ESTIMATE combined with Spearman test was employed. The weighted gene coexpression network analysis (WGCNA) network was established by utilizing the differentially expressed genes in breast cancer, and the target module with the most significant correlation with mRNAsi was screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to figure out the biological functions of the target module. As for the construction of the prognostic model, univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on genes in the module. The single sample gene set enrichment analysis (ssGSEA) and tumor mutational burden were employed for the analysis on immune infiltration and gene mutations in the high- and low-risk groups. As for the analysis on whether this model had the prognostic value, the nomogram and calibration curves of risk scores and clinical characteristics were drawn.

Results

Nine mRNAsi-related genes (CFB, MAL2, PSME2, MRPL13, HMGB3, DCTPP1, SHCBP1, SLC35A2, and EVA1B) comprised the prognostic model. According to the results of ssGSEA and gene mutation analysis, differences were shown in immune cell infiltration and gene mutation frequency between the high- and low-risk groups.

Conclusion

Nine mRNAsi-related genes screened in our research can be considered as the biomarkers to predict breast cancer patients' prognoses, and this model has a potential relationship with individual somatic gene mutations and immune regulation. This study can offer new insight into the development of diagnostic and clinical treatment strategies for breast cancer.

Comprehensive analysis for cellular senescence-related immunogenic characteristics and immunotherapy prediction of acute myeloid leukemia

In malignancies, cellular senescence is critical for carcinogenesis, development, and immunological regulation. Patients with acute myeloid leukemia (AML) have not investigated a reliable cellular senescence-associated profile and its significance in outcomes and therapeutic response. Cellular senescence-related genes were acquired from the CellAge database, while AML data were obtained from the GEO and TCGA databases. The TCGA-AML group served as a training set to construct a prognostic risk score signature, while the GSE71014 set was used as a testing set to validate the accuracy of the signature. Through exploring the expression profiles of cellular senescence-related genes (SRGs) in AML patients, we used Lasso and Cox regression analysis to establish the SRG-based signature (SRGS), which was validated as an independent prognostic predictor for AML patients via clinical correlation. Survival analysis showed that AML patients in the low-risk score group had a longer survival time. Tumor immune infiltration and functional enrichment analysis demonstrated that AML patients with low-risk scores had higher immune infiltration and active immune-related pathways. Meanwhile, drug sensitivity analysis and the TIDE algorithm showed that the low-risk score group was more susceptible to chemotherapy and immunotherapy. Cell line analysis in vitro further confirmed that the SRGs in the proposed signature played roles in the susceptibility to cytarabine and YM155. Our results indicated that SRGS, which regulates the immunological microenvironment, is a reliable predictor of the clinical outcome and immunotherapeutic response in AML.

Prostate cancer as a dedifferentiated organ: androgen receptor, cancer stem cells, and cancer stemness

Cancer progression is characterized and driven by gradual loss of a differentiated phenotype and gain of stem cell-like features. In prostate cancer (PCa), androgen receptor (AR) signaling is important for cancer growth, progression, and emergence of therapy resistance. Targeting the AR signaling axis has been, over the decades, the mainstay of PCa therapy. However, AR signaling at the transcription level is reduced in high-grade cancer relative to low-grade PCa and loss of AR expression promotes a stem cell-like phenotype, suggesting that emergence of resistance to AR-targeted therapy may be associated with loss of AR signaling and gain of stemness. In the present mini-review, we first discuss PCa from the perspective of an abnormal organ with increasingly deregulated differentiation, and discuss the role of AR signaling during PCa progression. We then focus on the relationship between prostate cancer stem cells (PCSCs) and AR signaling. We further elaborate on the current methods of using transcriptome-based stemness-enriched signature to evaluate the degree of oncogenic dedifferentiation (cancer stemness) in pan-cancer datasets, and present the clinical significance of scoring transcriptome-based stemness across the spectrum of PCa development. Our discussions highlight the importance to evaluate the dynamic changes in both stem cell-like features (stemness score) and AR signaling activity across the PCa spectrum.

m7G regulator-mediated molecular subtypes and tumor microenvironment in kidney renal clear cell carcinoma

Background: RNA methylation modification plays an important role in immune regulation. m7G RNA methylation is an emerging research hotspot in the RNA methylation field. However, its role in the tumor immune microenvironment of kidney renal clear cell carcinoma (KIRC) is still unclear.

Methods: We analyzed the expression profiles of 29 m7G regulators in KIRC, integrated multiple datasets to identify a novel m7G regulator-mediated molecular subtype, and developed the m7G score. We evaluated the immune tumor microenvironments in m7G clusters and analyzed the correlation of the m7G score with immune cells and drug sensitivity. We tested the predictive power of the m7G score for prognosis of patients with KIRC and verified the predictive accuracy of the m7G score by using the GSE40912 and E-MTAB-1980 datasets. The genes used to develop the m7G score were verified by qRT-PCR. Finally, we experimentally analyzed the effects of WDR4 knockdown on KIRC proliferation, migration, invasion, and drug sensitivity.

Results: We identified three m7G clusters. The expression of m7G regulators was higher in cluster C than in other clusters. m7G cluster C was related to immune activation, low tumor purity, good prognosis, and low m7G score. Cluster B was related to drug metabolism, high tumor purity, poor survival, and high m7G score. Cluster A was related to purine metabolism. The m7G score can well-predict the prognosis of patients with KIRC, and its prediction accuracy based on the m7G score nomogram was very high. Patients with high m7G scores were more sensitive to rapamycin, gefitinib, sunitinib, and vinblastine than other patients. Knocking down WDR4 can inhibit the proliferation, migration, and invasion of 786-0 and Caki-1 cells and increase sensitivity to sorafenib and sunitinib.

Conclusion: We proposed a novel molecular subtype related to m7G modification and revealed the immune cell infiltration characteristics of different subtypes. The developed m7G score can well-predict the prognosis of patients with KIRC, and our research provides a basis for personalized treatment of patients with KIRC.

Comprehensive Analyses of Ferroptosis-Related Alterations and Their Prognostic Significance in Glioblastoma

Background: This study was designed to explore the implications of ferroptosis-related alterations in glioblastoma patients.

Method: After obtaining the data sets CGGA325, CGGA623, TCGA-GBM, and GSE83300 online, extensive analysis and mutual verification were performed using R language-based analytic technology, followed by further immunohistochemistry staining verification utilizing clinical pathological tissues.

Results: The analysis revealed a substantial difference in the expression of ferroptosis-related genes between malignant and paracancerous samples, which was compatible with immunohistochemistry staining results from clinicopathological samples. Three distinct clustering studies were run sequentially on these data. All of the findings were consistent and had a high prediction value for glioblastoma. Then, the risk score predicting model containing 23 genes (CP, EMP1, AKR1C1, FMOD, MYBPH, IFI30, SRPX2, PDLIM1, MMP19, SPOCD1, FCGBP, NAMPT, SLC11A1, S100A10, TNC, CSMD3, ATP1A2, CUX2, GALNT9, TNFAIP6, C15orf48, WSCD2, and CBLN1) on the basis of “Ferroptosis.gene.cluster” was constructed. In the subsequent correlation analysis of clinical characteristics, tumor mutation burden, HRD, neoantigen burden and chromosomal instability, mRNAsi, TIDE, and GDSC, all the results indicated that the risk score model might have a better predictive efficiency.

Conclusion: In glioblastoma, there were a large number of abnormal ferroptosis-related alterations, which were significant for the prognosis of patients. The risk score-predicting model integrating 23 genes would have a higher predictive value.

The Genetic Evolution of Metastasis

Cancer is an evolutionary process that is characterized by the emergence of multiple genetically distinct populations or clones within the primary tumor. Intratumor heterogeneity provides a substrate for the selection of adaptive clones, such as those that lead to metastasis. Comparative molecular studies of primary tumors and metastases have identified distinct genomic features associated with the development of metastases. In this review, we discuss how these insights could inform clinical decision-making and uncover rational antimetastasis treatment strategies.

StemSC: a cross-dataset human stemness index for single-cell samples

BACKGROUND

Stemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these methods showed low negative correlations with differentiation time and can't leverage the existing experimentally validated stem cells to recognize the stem-like cells.

METHODS

Here, we constructed a stemness index for single-cell samples (StemSC) based on relative expression orderings (REO) of gene pairs. Firstly, we identified the stemness-related genes by selecting the genes significantly related to differentiation time. Then, we used 13 RNA-seq datasets from both the bulk and single-cell embryonic stem cell (ESC) samples to construct the reference REOs. Finally, the StemSC value of a given sample was calculated as the percentage of gene pairs with the same REOs as the ESC samples.

RESULTS

We validated the StemSC by its higher negative correlations with differentiation time in eight normal datasets and its higher positive correlations with tumor dedifferentiation in three colorectal cancer datasets and four glioma datasets. Besides, the robust of StemSC to batch effect enabled us to leverage the existing experimentally validated cancer stem cells to recognize the stem-like cells in other independent tumor datasets. And the recognized stem-like tumor cells had fewer interactions with anti-tumor immune cells. Further survival analysis showed the immunotherapy-treated patients with high stemness had worse survival than those with low stemness.

CONCLUSIONS

StemSC is a better stemness index to calculate the stemness across datasets, which can help researchers explore the effect of stemness on other biological processes.

A novel qualitative signature based on lncRNA pairs for prognosis prediction in hepatocellular carcinoma

Background

Prognostic assessment is imperative for clinical management of patients with hepatocellular carcinoma (HCC). Most reported prognostic signatures are based on risk scores summarized from quantitative expression level of candidate genes, which are vulnerable against experimental batch effects and impractical for clinical application. We aimed to develop a robust qualitative signature to assess individual survival risk for HCC patients.

Methods

Long non-coding RNA (lncRNA) pairs correlated with overall survival (OS) were identified and an optimal combination of lncRNA pairs based on the majority voting rule was selected as a classification signature to predict the overall survival risk in the cancer genome atlas (TCGA). Then, the signature was further validated in two external datasets. Besides, biomolecular characteristics, immune infiltration status, and chemotherapeutics efficacy of different risk groups were further compared. Finally, we performed key lncRNA screening and validated it in vitro.

Results

A signature consisting of 50 lncRNA pairs (50-LPS) was identified in TCGA and successfully validated in external datasets. Patients in the high-risk group, when at least 25 of the 50-LPS voted for high risk, had significantly worse OS than the low-risk group. Multivariate Cox, receiver operating characteristic (ROC) curve and decision curve analyses (DCA) demonstrated that the 50-LPS was an independent prognostic factor and more powerful than other available clinical factors in OS prediction. Comparison analyses indicated that different risk groups had distinct biomolecular characteristics, immune infiltration status, and chemotherapeutics efficacy. TDRKH-AS1 was confirmed as a key lncRNA and associated with cell growth of HCC.

Conclusions

The 50-LPS could not only predict the prognosis of HCC patients robustly and individually, but also provide theoretical basis for therapy. Besides, TDRKH-AS1 was identified as a key lncRNA in the proliferation of HCC. The 50-LPS might guide personalized therapy for HCC patients in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02507-z.

Implications of Stemness Features in 1059 Hepatocellular Carcinoma Patients from Five Cohorts: Prognosis, Treatment Response, and Identification of Potential Compounds

Cancer stemness has been reported to drive hepatocellular carcinoma (HCC) tumorigenesis and treatment resistance. In this study, five HCC cohorts with 1059 patients were collected to calculate transcriptional stemness indexes (mRNAsi) by the one-class logistic regression machine learning algorithm. In the TCGA-LIHC cohort, we found mRNAsi was an independent prognostic factor, and 626 mRNAsi-related genes were identified by Spearman correlation analysis. The HCC stemness risk model (HSRM) was trained in the TCGA-LIHC cohort and significantly discriminated overall survival in four independent cohorts. HSRM was also significantly associated with transarterial chemoembolization treatment response and rapid tumor growth in HCC patients. Consensus clustering was conducted based on mRNAsi-related genes to divide 1059 patients into two stemness subtypes. On gene set variation analysis, samples of subtype I were found enriched with pathways such as DNA replication and cell cycle, while several liver-specific metabolic pathways were inhibited in these samples. Somatic mutation analysis revealed more frequent mutations of TP53 and RB1 in the subtype I samples. In silico analysis suggested topoisomerase, cyclin-dependent kinase, and histone deacetylase as potential targets to inhibit HCC stemness. In vitro assay showed two predicted compounds, Aminopurvalanol-a and NCH-51, effectively suppressed oncosphere formation and impaired viability of HCC cell lines, which may shed new light on HCC treatment.

Immune Infiltration, Cancer Stemness, and Targeted Therapy in Gastrointestinal Stromal Tumor

Objective

To investigate the characteristics of the tumor immune microenvironment in patients with gastrointestinal stromal tumor (GIST) and identify cancer stem-like properties of GIST to screen potential druggable molecular targets.

Methods

The gene expression data of 60 patients with GIST was retrieved from the Array Express database. CIBERSORT was applied to calculate the level of immune infiltration. ssGSEA and ESTIMATE were used to calculate the cancer stemness index and tissue purity. The Connectivity Map (CMAP) database was implemented to screen targeted drugs based on cancer stem-like properties of GIST.

Result

There was a difference in the level of immune infiltration between the metastasis and non-metastasis GIST groups. The low level of T-cell infiltration was correlated with high tumor purity and tumor stemness index, and the correlation coefficients were -0.87 and -0.61 (p < 0.001), respectively. Furthermore, there was a positive correlation between cancer stemness index and cell purity (p < 0.001). The cancer stemness index in the metastasis group was higher than that in the non-metastasis group (p = 0.0017). After adjusting for tumor purity, there was no significant correlation between T-cell infiltration and cancer stemness index (p = 0.086). Through the pharmacological mechanism of topoisomerase inhibitors, six molecular complexes may be the targets of GIST treatment.

Conclusion

Immune infiltration in GIST patients is related to cancer stem-like properties, and the correlation relies on tumor purity. Cancer stemness index can be used as a new predictive biomarker of tumor metastasis and targets of drug therapy for GIST patients.

Analyzing mRNAsi-Related Genes Identifies Novel Prognostic Markers and Potential Drug Combination for Patients with Basal Breast Cancer

Basal breast cancer subtype is the worst prognosis subtypes among all breast cancer subtypes. Recently, a new tumor stemness index-mRNAsi is found to be able to measure the degree of oncogenic differentiation of tissues. The mRNAsi involved in a variety of cancer processes is derived from the innovative application of one-class logistic regression (OCLR) machine learning algorithm to the whole genome expression of various stem cells and tumor cells. However, it is largely unknown about mRNAsi in basal breast cancer. Here, we find that basal breast cancer carries the highest mRNAsi among all four subtypes of breast cancer, especially 385 mRNAsi-related genes are positively related to the high mRNAsi value in basal breast cancer. This high mRNAsi is also closely related to active cell cycle, DNA replication, and metabolic reprogramming in basal breast cancer. Intriguingly, in the 385 genes, TRIM59, SEPT3, RAD51AP1, and EXO1 can act as independent protective prognostic factors, but CTSF and ABHD4B can serve as independent bad prognostic factors in patients with basal breast cancer. Remarkably, we establish a robust prognostic model containing the 6 mRNAsi-related genes that can effectively predict the survival rate of patients with the basal breast cancer subtype. Finally, the drug sensitivity analysis reveals that some drug combinations may be effectively against basal breast cancer via targeting the mRNAsi-related genes. Taken together, our study not only identifies novel prognostic biomarkers for basal breast cancers but also provides the drug sensitivity data by establishing an mRNAsi-related prognostic model.

Identification of Stemness Characteristics Associated With the Immune Microenvironment and Prognosis in Gastric Cancer

Background

Gastric cancer (GC) is a highly heterogeneous disease. In recent years, the prognostic value of the mRNA expression-based stemness index (mRNAsi) across cancers has been reported. We intended to identify stemness index-associated genes (SI-genes) for clinical characteristic, gene mutation status, immune response, and tumor microenvironment evaluation as well as risk stratification and survival prediction.

Methods

The correlations between the mRNAsi and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration and tumor microenvironment were evaluated. Weighted gene correlation network analysis (WGCNA) was performed to identify SI-genes from differentially expressed genes (DEGs) in The Cancer Genome Atlas (TCGA). Single-sample gene set enrichment analysis (ssGSEA) was employed to calculate the sample SI-gene-based ssGSEA score according to the SI-genes. Then, the correlations between the ssGSEA score and GC prognosis, clinical characteristics, gene mutation status, immune cell infiltration and tumor microenvironment were analyzed. Finally, the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to construct a prognostic signature with prognostic SI-genes. The ssGSEA score and prognostic signature were validated using the Gene Expression Omnibus (GEO) database.

Results

The mRNAsi could predict overall survival (OS), clinical characteristics, the gene mutation status, immune cell infiltration, and the tumor microenvironment composition. Fourteen positive SI-genes and 178 negative SI-genes were screened out using WGCNA. The ssGSEA score, similar to the mRNAsi, was found to be closely related to OS, clinical characteristics, the gene mutation status, immune cell infiltration, and the tumor microenvironment composition. Finally, a prognostic signature based on 18 prognostic SI-genes was verified to more accurately predict GC 1-year, 3-year, and 5-year OS than traditional clinical prediction models.

Conclusion

The ssGSEA score and prognostic signature based on 18 prognostic SI-genes are of great value for immune response evaluation, risk stratification and survival prediction in GC and suggest that stemness features are crucial drivers of GC progression.

CD73, Tumor Plasticity and Immune Evasion in Solid Cancers

Simple Summary

Tumors are ecosystems composed of cancer cells and non-tumor stroma together in a hypoxic environment often described as wounds that do not heal. Accumulating data suggest that solid tumors hijack cellular plasticity possibly to evade detection by the immune system. CD73-mediated generation of the purine nucleoside adenosine, is an important biochemical constituent of the immunosuppressive tumor microenvironment. In this review, the association between CD73 expression and features associated with cellular plasticity involving stemness, epithelial-to-mesenchymal transition and metastasis together with immune infiltration is summarized for a wide range of solid tumor types. Our analyses demonstrate that CD73 correlates with signatures associated with cellular plasticity in solid tumors. In addition, there are strong associations between CD73 expression and type of infiltrating lymphocytes. Collectively, the observations suggest a biomarker-based stratification to identify CD73-adenosinergic rich tumors may help identify patients with solid cancers who will respond to a combinatorial strategy that includes targeting CD73.

Abstract

Regulatory networks controlling cellular plasticity, important during early development, can re-emerge after tissue injury and premalignant transformation. One such regulatory molecule is the cell surface ectoenzyme ecto-5′-nucleotidase that hydrolyzes the conversion of extracellular adenosine monophosphate to adenosine (eADO). Ecto-5′-nucleotidase (NT5E) or cluster of differentiation 73 (CD73), is an enzyme that is encoded by NT5E in humans. In normal tissue, CD73-mediated generation of eADO has important pleiotropic functions ranging from the promotion of cell growth and survival, to potent immunosuppression mediated through purinergic G protein-coupled adenosine receptors. Importantly, tumors also utilize several mechanisms mediated by CD73 to resist therapeutics and in particular, evade the host immune system, leading to undesired resistance to targeted therapy and immunotherapy. Tumor cell CD73 upregulation is associated with worse clinical outcomes in a variety of cancers. Emerging evidence indicates a link between tumor cell stemness with a limited host anti-tumor immune response. In this review, we provide an overview of a growing body of evidence supporting the pro-tumorigenic role of CD73 and adenosine signaling. We also discuss data that support a link between CD73 expression and tumor plasticity, contributing to dissemination as well as treatment resistance. Collectively, targeting CD73 may represent a novel treatment approach for solid cancers.