• biomarker
• drug target
• liquid biopsy
• neuroendocrine
• prostate cancer
• stem

• Humans
• Male
• Prostatic Neoplasms/genetics
• Prostatic Neoplasms/blood
• Prostatic Neoplasms/pathology
• Prostatic Neoplasms/diagnosis
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/blood
• Liquid Biopsy
• Aged
• Gene Expression Regulation, Neoplastic
• Middle Aged
• Clinical Relevance

• Humans
• STAT1 Transcription Factor/metabolism
• STAT1 Transcription Factor/genetics
• Neoplastic Stem Cells/metabolism
• Neoplastic Stem Cells/pathology
• Female
• Animals
• Single-Cell Analysis/methods
• Mice
• Cell Lineage
• Triple Negative Breast Neoplasms/genetics
• Triple Negative Breast Neoplasms/pathology
• Triple Negative Breast Neoplasms/metabolism
• Gene Expression Regulation, Neoplastic
• Signal Transduction
• Sequence Analysis, RNA/methods
• Interferons/metabolism
• Interferons/genetics
• Cell Line, Tumor
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• Breast Neoplasms/metabolism
• Xenograft Model Antitumor Assays
• RNA-Seq

• F31CA278274 National Cancer Institute (NCI)
• U54CA224076 National Cancer Institute (NCI)
• R01CA224867 National Cancer Institute (NCI)
• RP170691 Cancer Research and Prevention Initiative of Texas
• R35GM133658 National Institutes of Health (NIH)
• CCR19609287 Susan G. Komen Foundation

• biomarkers
• co-inhibitory molecules
• immune checkpoint inhibitors
• immunotherapy
• pan-cancer

• Humans
• Neoplasms/therapy
• Neoplasms/immunology
• Neoplasms/genetics
• Neoplasms/metabolism
• Prognosis
• Immunotherapy/methods
• Biomarkers, Tumor/genetics
• Gene Expression Regulation, Neoplastic
• Immune Checkpoint Inhibitors/therapeutic use
• Receptors, Immunologic

• Cancer therapy
• Hippo signaling
• Regulation
• TAZ
• Tumorigenesis
• YAP

• Humans
• Hippo Signaling Pathway
• Protein Serine-Threonine Kinases/metabolism
• Protein Serine-Threonine Kinases/genetics
• Signal Transduction
• Carcinogenesis/genetics
• Carcinogenesis/metabolism
• Neoplasms/metabolism
• Neoplasms/genetics
• Neoplasms/pathology
• Animals
• Cell Proliferation
• Transcription Factors/metabolism
• Adaptor Proteins, Signal Transducing/metabolism

• ANUI2024M111 Alnoor University

• Breast cancer
• Mendelian randomization
• Pathway genes
• Pyruvate metabolism
• Targeted drugs

• Flavones/pharmacology
• Humans
• Neoplastic Stem Cells/drug effects
• Neoplastic Stem Cells/metabolism
• Breast Neoplasms/drug therapy
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• MCF-7 Cells
• Computational Biology/methods
• Female
• Cell Cycle/drug effects
• Gene Expression Regulation, Neoplastic/drug effects
• Signal Transduction/drug effects

• Cancer stem cells
• Immune microenvironment
• LncRNAs
• Personalized treatment
• Prognostic model
• Triple-negative breast cancer

• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Humans
• Triple Negative Breast Neoplasms/genetics
• Triple Negative Breast Neoplasms/pathology
• Triple Negative Breast Neoplasms/immunology
• Prognosis
• Neoplastic Stem Cells/metabolism
• Neoplastic Stem Cells/pathology
• Female
• Gene Expression Regulation, Neoplastic
• Computer Simulation
• Treatment Outcome
• Animals
• Kaplan-Meier Estimate
• Gene Regulatory Networks
• Middle Aged
• Cell Line, Tumor
• ROC Curve
• Gene Expression Profiling
• Proportional Hazards Models
• Immunity/genetics
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism

• (QYT2021-02) Hospital-level Project of QingPu Branch of Zhongshan Hospital Affiliated to Fudan University
• Hospital-level Project of QingPu Branch of Zhongshan Hospital Affiliated to Fudan University

• BCa tumors
• Breast cancer
• hematopoietic stem cells.
• inhibitors
• molecular pathway
• stem cell therapy
• stem cells

• Female
• Humans
• Breast Neoplasms/drug therapy
• Neoplastic Stem Cells/pathology

• breast cancer
• early diagnosis
• hub-genes
• integrated robust statistics and bioinformatics approaches
• prognosis and therapies
• transcriptomics profiles

• Humans
• Female
• Breast Neoplasms/diagnosis
• Breast Neoplasms/genetics
• Breast Neoplasms/therapy
• Transcriptome/genetics
• Molecular Docking Simulation
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• Early Detection of Cancer
• Gene Expression Profiling/methods
• Prognosis
• Gene Expression Regulation, Neoplastic
• Gene Regulatory Networks

• A-289/5/52/RU/Science-24/2022-2023 Rajshahi University Research Project

• Biomarkers
• Cancer stemness features
• Prognosis
• Renal cell carcinoma
• mRNAsi

• T regulatory cells (Treg cells)
• angiogenesis
• hypoxia inducible factors (HIFs)
• hypoxic tumor microenvironment
• immunological switch point
• myeloid derived suppressor cells (MDSCs)
• tumor endothelial cells (TECs)

• MAGE-A3
• WGCNA
• gastric cancer
• mRNAsi

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.

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.

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.

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.

• cancer stem cells
• kidney renal clear cell carcinoma
• phytochemicals
• prognosis
• tumor microenvironment

• CSC No. 201908080121 China Scholarship Council

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle paralysis, which is followed by degeneration of motor neurons in the motor cortex of the brainstem and spinal cord. The etiology of sporadic ALS (sALS) is still unknown, limiting the exploration of potential treatments. Ferroptosis is a new form of cell death and is reported to be closely associated with Alzheimer’s disease (AD), Parkinson’s disease (PD), and ALS. In this study, we used datasets (autopsy data and blood data) from Gene Expression Omnibus (GEO) to explore the role of ferroptosis and ferroptosis-related gene (FRG) alterations in ALS. Gene set enrichment analysis (GSEA) found that the activated ferroptosis pathway displayed a higher enrichment score, and the expression of 26 ferroptosis genes showed obvious group differences between ALS and controls. Using weighted gene correlation network analysis (WGCNA), we identified FRGs associated with ALS, of which the Gene Ontology (GO) analysis displayed that the biological process of oxidative stress was the most to be involved in. KEGG pathway analysis revealed that the FRGs were enriched not only in ferroptosis pathways but also in autophagy, FoxO, and mTOR signaling pathways. Twenty-one FRGs (NR4A1, CYBB, DRD4, SETD1B, LAMP2, ACSL4, MYB, PROM2, CHMP5, ULK1, AKR1C2, TGFBR1, TMBIM4, MLLT1, PSAT1, HIF1A, LINC00336, AMN, SLC38A1, CISD1, and GABARAPL2) in the autopsy data and 16 FRGs (NR4A1, DRD4, SETD1B, MYB, PROM2, CHMP5, ULK1, AKR1C2, TGFBR1, TMBIM4, MLLT1, HIF1A, LINC00336, IL33, SLC38A1, and CISD1) in the blood data were identified as target genes by least absolute shrinkage and selection operator analysis (LASSO), in which gene signature could differentiate ALS patients from controls. Finally, the higher the expression of CHMP5 and SLC38A1 in whole blood, the shorter the lifespan of ALS patients will be. In summary, our study presents potential biomarkers for the diagnosis and prognosis of ALS.

• breast cancer stem cells
• cell interactions
• clinical trials
• signaling pathways

• BC180227, BC200100 Department of Defence
• R01 CA200673 NCI NIH HHS
• R01 CA203834 NCI NIH HHS
• R01 CA260239 NCI NIH HHS
• CA200673, CA203834, CA260239 NIH HHS

Inflammatory breast cancer (IBC) is a highly aggressive phenotype of breast cancer that is characterized by a high incidence early metastasis. We previously reported a significant association of human cytomegalovirus (HCMV) DNA in the carcinoma tissues of IBC patients but not in the adjacent normal tissues. HCMV-infected macrophages serve as “mobile vectors” for spreading and disseminating virus to different organs, and IBC cancer tissues are highly infiltrated by tumor-associated macrophages (TAMs) that enhance IBC progression and promote breast cancer stem cell (BCSC)-like properties. Therefore, there is a need to understand the role of HCMV-infected TAMs in IBC progression. The present study aimed to test the effect of the secretome (cytokines and secreted factors) of TAMs derived from HCMV⁺ monocytes isolated from IBC specimens on the proliferation, invasion, and BCSC abundance when tested on the IBC cell line SUM149. HCMV⁺ monocytes were isolated from IBC patients during modified radical mastectomy surgery and tested in vitro for polarization into TAMs using the secretome of SUM149 cells. MTT, clonogenic, invasion, real-time PCR arrays, PathScan Intracellular Signaling array, and cytokine arrays were used to characterize the secretome of HCMV⁺ TAMs for their effect on the progression of SUM149 cells. The results showed that the secretome of HCMV⁺ TAMs expressed high levels of IL-6, IL-8, and MCP-1 cytokines compared to HCMV^- TAMs. In addition, the secretome of HCMV⁺ TAMs induced the proliferation, invasion, colony formation, and expression of BCSC-related genes in SUM149 cells compared to mock untreated cells. In addition, the secretome of HCMV⁺ TAMs activated the phosphorylation of intracellular signaling molecules p-STAT3, p-AMPKα, p-PRAS40, and p-SAPK/JNK in SUM149 cells. In conclusion, this study shows that the secretome of HCMV⁺ TAMs enhances the proliferation, invasion, colony formation, and BCSC properties by activating the phosphorylation of p-STAT3, p-AMPKα, p-PRAS40, and p-SAPK/JNK intracellular signaling molecules in IBC cells.

• Avon Foundation for Women

• RT-PCR
• cancer stem cell
• immune
• lung adenocarcinoma
• muti-omics analysis
• nomogram
• stem cell index

• Adenocarcinoma of Lung/genetics
• Adenocarcinoma of Lung/pathology
• Biomarkers, Tumor/metabolism
• Gene Expression Regulation, Neoplastic
• Humans
• Lung Neoplasms/pathology
• Nomograms
• Prognosis
• RNA, Messenger/metabolism

• Natural Science Foundation of Chongqing

Metabolic disorders are associated with increased incidence, aggressive phenotype and poor outcome of breast cancer (BC) patients. For instance, hyperinsulinemia is an independent risk factor for BC and the insulin/insulin receptor (IR) axis is involved in BC growth and metastasis. Of note, the anti-diabetic metformin may be considered in comprehensive therapeutic approaches in BC on the basis of its antiproliferative effects obtained in diverse pre-clinical and clinical studies.

Bioinformatics analysis were performed using the information provided by The Invasive Breast Cancer Cohort of The Cancer Genome Atlas (TCGA) project. The naturally immortalized BC cell line, named BCAHC-1, as well as cancer-associated fibroblasts (CAFs) derived from BC patients were used as model systems. In order to identify further mechanisms that characterize the anticancer action of metformin in BC, we performed gene expression and promoter studies as well as western blotting experiments. Moreover, cell cycle analysis, colony and spheroid formation, actin cytoskeleton reorganization, cell migration and matrigel drops evasion assays were carried out to provide novel insights on the anticancer properties of metformin.

We first assessed that elevated expression and activation of IR correlate with a worse prognostic outcome in estrogen receptor (ER)-positive BC. Thereafter, we established that metformin inhibits the insulin/IR-mediated activation of transduction pathways, gene changes and proliferative responses in BCAHC-1 cells. Then, we found that metformin interferes with the insulin-induced expression of the metastatic gene CXC chemokine receptor 4 (CXCR4), which we found to be associated with poor disease-free survival in BC patients exhibiting high levels of IR. Next, we ascertained that metformin prevents a motile phenotype of BCAHC-1 cells triggered by the paracrine liaison between tumor cells and CAFs upon insulin activated CXCL12/CXCR4 axis.

Our findings provide novel mechanistic insights regarding the anti-proliferative and anti-migratory effects of metformin in both BC cells and important components of the tumor microenvironment like CAFs. Further investigations are warranted to corroborate the anticancer action of metformin on the tumor mass toward the assessment of more comprehensive strategies halting BC progression, in particular in patients exhibiting metabolic disorders and altered insulin/IR functions.

The online version contains supplementary material available at 10.1186/s12967-022-03463-y.

Ovarian cancer (OC) is one of the most common gynecological malignancies. It is associated with a difficult diagnosis and poor prognosis. Our study aimed to analyze tumor stemness to determine the prognosis feature of patients with OC. At this job, we selected the gene expression and the clinical profiles of patients with OC in the TCGA database. We calculated the stemness index of each patient using the one-class logistic regression (OCLR) algorithm and performed correlation analysis with immune infiltration. We used consensus clustering methods to classify OC patients into different stemness subtypes and compared the differences in immune infiltration between them. Finally, we established a prognostic signature by Cox and LASSO regression analysis. We found a significant negative correlation between a high stemness index and immune score. Pathway analysis indicated that the differentially expressed genes (DEGs) from the low- and high-mRNAsi groups were enriched in multiple functions and pathways, such as protein digestion and absorption, the PI3K-Akt signaling pathway, and the TGF-β signaling pathway. By consensus cluster analysis, patients with OC were split into two stemness subtypes, with subtype II having a better prognosis and higher immune infiltration. Furthermore, we identified 11 key genes to construct the prognostic signature for patients with OC. Among these genes, the expression levels of nine, including SFRP2, MFAP4, CCDC80, COL16A1, DUSP1, VSTM2L, TGFBI, PXDN, and GAS1, were increased in the high-risk group. The analysis of the KM and ROC curves indicated that this prognostic signature had a great survival prediction ability and could independently predict the prognosis for patients with OC. We established a stemness index-related risk prognostic module for OC, which has prognostic-independent capabilities and is expected to improve the diagnosis and treatment of patients with OC.

• Female
• Humans
• Logistic Models
• Neoplastic Stem Cells
• Ovarian Neoplasms/diagnosis
• Ovarian Neoplasms/genetics
• Prognosis

• DNA methylation
• WGCNA
• machine learning
• pancreatic cancer
• prognosis
• tumor immunity

Cancer stem cells (CSCs) can induce recurrence and chemotherapy resistance of lung adenocarcinoma (LUAD). Reliable markers identified based on CSC characteristic of LUAD may improve patients' chemotherapy response and prognosis. OCLR was used to calculate mRNA expression-based stemness index (mRNAsi) of LUAD patients' data in TCGA. Association analysis of mRNAsi was performed with clinical features, somatic mutation, and tumor immunity. A prognostic prediction model was established with LASSO Cox regression. Kaplan-Meier Plotter (KM-plotter) and time-dependent ROC were applied to assess signature performance. For LUAD, univariate and multivariate Cox analysis was performed to identify independent prognostic factors. LUAD tissues showed a noticeably higher mRNAsi in than nontumor tissues, and it showed significant differences in T, N, M, AJCC stages, and smoking history. The most frequently mutated gene was TP53, with a higher mRNAsi relating to more frequent mutation of TP53. The mRNAsi was significantly negatively correlated with immune score, stromal score, and ESTIMATE score in LUAD. The blue module was associated with mRNAsi. The 5-gene signature was confirmed as an independent indicator of LUAD prognosis that could promote personalized treatment of LUAD and accurately predict overall survival (OS) of LUAD patients.

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored.

Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs.

Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes.

Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

• HCC
• ICGC data portal
• metabolism
• mitophagy
• the cancer genome atlas program

• CDH1
• biomarker
• breast cancer
• miRNAs
• oncogene

• Antigens, CD/genetics
• Breast Neoplasms/diagnosis
• Breast Neoplasms/genetics
• Cadherins/genetics
• Carcinogenesis
• Female
• Humans
• MicroRNAs/genetics
• Oncogenes

• fine-mapping
• genome-wide association study
• pan-cancer
• risk estimation
• summary statistics

• Cancer stem cell
• LAML
• Machine learning
• RFC4
• RFC5
• mRNAsi

• OCLR
• glioblastoma
• mRNAsi
• prognosis
• stemness indices

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.

• colon cancer
• machine learning
• prognosis
• pyroptosis
• tumor microenvironment

Background: Cancer stem cells (CSCs), which are characterized by self-renewal and plasticity, are highly correlated with tumor metastasis and drug resistance. To fully understand the role of CSCs in colorectal cancer (CRC), we evaluated the stemness traits and prognostic value of stemness-related genes in CRC.

Methods: In this study, the data from 616 CRC patients from The Cancer Genome Atlas (TCGA) were assessed and subtyped based on the mRNA expression-based stemness index (mRNAsi). The correlations of cancer stemness with the immune microenvironment, tumor mutational burden (TMB), and N6-methyladenosine (m6A) RNA methylation regulators were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify the crucial stemness-related genes and modules. Furthermore, a prognostic expression signature was constructed using the Lasso-penalized Cox regression analysis. The signature was validated via multiplex immunofluorescence staining of tissue samples in an independent cohort of 48 CRC patients.

Results: This study suggests that high-mRNAsi scores are associated with poor overall survival in stage IV CRC patients. Moreover, the levels of TMB and m6A RNA methylation regulators were positively correlated with mRNAsi scores, and low-mRNAsi scores were characterized by increased immune activity in CRC. The analysis identified 34 key genes as candidate prognosis biomarkers. Finally, a three-gene prognostic signature (PARPBP, KNSTRN, and KIF2C) was explored together with specific clinical features to construct a nomogram, which was successfully validated in an external cohort.

Conclusion: There is a unique correlation between CSCs and the prognosis of CRC patients, and the novel biomarkers related to cell stemness could accurately predict the clinical outcomes of these patients.

• N6-methyladenosine
• cancer stem cell
• colorectal cancer
• machine learning
• tumor immune microenvironment

As per the latest Globocan statistics, the high prevalence rate of breast cancer in low- and middle-income countries has led to it becoming the most common cancer to be diagnosed, hence posing a major public health challenge. As per this data, more than 11.7% of the estimated new cancer cases in 2020 were due to breast cancer. A small but significant subpopulation of cells with self- renewing ability are present in the tumor stroma and have been given the nomenclature of cancer stem cells (CSCs). These cells display a high degree of plasticity owing to their ability to transition from the slowly cycling quiescent phase to the actively proliferating phenotype. This attribute of CSCs allows them to differentiate into various cell types having diverse functions. Breast CSCs have a pivotal role in development, metastasis, treatment resistance and relapse of breast cancers. This review focuses on the pathways regulating breast CSC maintenance and the current strategies that are being explored for directing the development of novel, targeted, therapeutic approaches for limiting and eradicating this aberrant stem cell population.

• Molecular genomics
• Breast cancer stem cells
• Tumor microenvironment
• Molecular pathways
• Targeted therapy
• Inhibitors

Serous ovarian cancer (SOC) is a main histological subtype of ovarian cancer, in which cancer stem cells (CSC) are responsible for its chemoresistance. However, the underlying modulation mechanisms of chemoresistance led by cancer stemness are still undefined. We aimed to investigate potential drug-response indicators among stemness-associated biomarkers in advanced SOC samples. The mRNA expression-based stemness index (mRNAsi) of The Cancer Genome Atlas (TCGA) was evaluated and corrected by tumor purity. Weighted gene co-expression network analysis (WGCNA) was utilized to explore the gene modules and key genes involved in stemness characteristics. We found that mRNAsi and corrected mRNAsi scores were both greater in tumors of Grade 3 and 4 than that of Grade 1 and 2. Forty-two key genes were obtained from the most significant mRNAsi-related gene module. Functional annotation revealed that these key genes were mainly involved in the mitotic division. Thirteen potential platinum-response indicators were selected from the genes enriched to platinum-response associated pathways. Among them, we identified 11 genes with prognostic value of progression-free survival (PFS) in advanced SOC patients treated with platinum and 7 prognostic genes in patients treated with a combination of platinum and taxol. The expressions of the 13 key genes were also validated between platinum-resistant and -sensitive SOC samples of advanced stages in two Gene Expression Omnibus (GEO) datasets. The results revealed that CDC20 was a potential platinum-sensitivity indicator in advanced SOC. These findings may provide a new insight for chemotherapies in advanced SOC patients clinically.

• SOC
• TCGA
• WGCNA
• mRNAsi
• platinum response
• stemness

• Breast cancer
• Breast cancer stem cells
• Metastasis
• Signaling pathways
• Tumorigenesis
• miRNAs

• Breast Neoplasms/genetics
• Epithelial-Mesenchymal Transition
• Female
• Hedgehog Proteins
• Humans
• Neoplastic Stem Cells
• Signal Transduction

• glioma
• immune infiltration
• molecular subtypes
• prognostic signature
• stemness index

Ovarian cancer (OC) is the most lethal gynecological malignancy. It has been reported that cancer stem cells (CSCs) play a crucial role in disseminated metastases in abdominal cavity and chemotherapy resistance of high-grade serous OC. However, the overall gene expression features of OC stem cells have not been clarified.

Expression datasets of 379 OC samples and 88 normal tissues were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype Tissue Expression (GTEx) project. Differentially expressed genes (DEGs) were screened using the “limma” package in R software. Among the DEGs, modules and hub genes that were highly related to messenger RNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi indices were identified via weighted gene co-expression network analysis (WGCNA). These hub genes were considered to be associated with OC stem cells. The Gene Ontology (GO) project and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the main biological processes that hub genes participated in. Finally, Connectivity Map (CMap) was used to predict compounds that disturb the hub genes.

We identified 2,253 DEGs; of these, 31 had a significantly positive correlation to mRNAsi indices and were upregulated in OC, while 41 of them had a significantly negative correlation with mRNAsi indices and were downregulated in OC. Correlation analysis indicated that hub genes from the same module composed a dense interaction network. GO and KEGG enrichment analysis demonstrated that hub genes primarily play roles in cell division and proliferation. Moreover, the compounds that may disturb hub genes were identified. Of these, 11 compounds, including MS-275, DL-thiorphan, and GW-8510, which have never been studied in OC stem cells, were screened as underlying treatments targeting OC stem cells.

Altogether, 72 hub genes that were closely linked to OC stem cell characteristics were found to mainly participate in cell division and proliferation. Moreover, compounds that disturb these hub gens were identified and can be considered underlying targets for inhibiting OC stem cells.

• Ovarian cancer (OC)
• cancer cell stemness
• connectivity map (CMap)
• mRNAsi
• weighted gene co-expression network analysis (WGCNA)

• canccer stem cell
• classification
• cutaneous melanoma
• prognosis
• stemness feature

• Breast cancer
• CAF
• Cancer stem cell
• Epithelial-mesenchymal transition
• Non-coding RNAs
• PD-1/PD-L1
• Transcription factor
• Tumor microenvironment

• Cancer stem cells
• drug resistance
• interleukin-8
• migration/invasion
• self-renewal

Astaxanthin (AST) is a product made from marine organisms that has been used as an anti-cancer supplement. It reduces pontin expression and induces apoptosis in SKBR3, a breast cancer cell line. Using Western blotting and qRT-PCR analyses, this study revealed that in the T47D and BT20 breast cancer cell lines, AST inhibits expression of pontin and mutp53, as well as the Oct4 and Nanog cancer stem cell (CSC) stemness genes. In addition, we explored the mechanism by which AST eradicates breast cancer cells using pontin siRNAs. Pontin knockdown by pontin siRNA reduced proliferation, Oct4 and Nanog expression, colony and spheroid formation, and migration and invasion abilities in breast cancer cells. In addition, reductions in Oct4, Nanog, and mutp53 expression following rottlerin treatment confirmed the role of pontin in these cells. Therefore, pontin may play a central role in the regulation of CSC properties and in cell proliferation following AST treatment. Taken together, these findings demonstrate that AST can repress CSC stemness genes in breast cancer cells, which implies that AST therapy could be used to improve the efficacy of other anti-cancer therapies against breast cancer cells.

• BT20
• Nanog
• Oct4
• T47D
• astaxanthin
• cancer stem cells
• mutp53
• pontin
• siRNA

• ATPases Associated with Diverse Cellular Activities/genetics
• ATPases Associated with Diverse Cellular Activities/metabolism
• Antineoplastic Agents/pharmacology
• Breast Neoplasms/drug therapy
• Breast Neoplasms/genetics
• Breast Neoplasms/metabolism
• Breast Neoplasms/pathology
• Carrier Proteins/genetics
• Carrier Proteins/metabolism
• Cell Movement/drug effects
• Cell Proliferation/drug effects
• DNA Helicases/genetics
• DNA Helicases/metabolism
• Female
• Gene Expression Regulation, Neoplastic
• Humans
• Mutation
• Nanog Homeobox Protein/genetics
• Nanog Homeobox Protein/metabolism
• Neoplasm Invasiveness
• Neoplastic Stem Cells/drug effects
• Neoplastic Stem Cells/metabolism
• Neoplastic Stem Cells/pathology
• Octamer Transcription Factor-3/genetics
• Octamer Transcription Factor-3/metabolism
• Tumor Suppressor Protein p53/genetics
• Tumor Suppressor Protein p53/metabolism
• Xanthophylls/pharmacology

• The Cancer Genome Atlas
• cancer cell stemness indices
• head and neck squamous cell carcinomas
• predictive models
• weighted gene co-expression network analysis

Cancer stem cells (CSCs) with self-renewal play an important role in tumor initiation and progression and are associated with drug resistance in cancer therapy. Here, we investigated the characteristics of stem cell-related genes in colorectal cancer (CRC) based on datasets from The Cancer Genome Atlas (TCGA) and Oncomine. We found that the stemness indices were significantly overexpressed in CRC tissues and were associated with patient survival. Weighted gene co-expression network analysis (WGCNA) was performed to determine the modules of stemness and featured genes. Significant modules and 8 genes (BUB1, BUB1B, CHEK1, DNA2, KIF23, MCM10, PLK4, and TTK) were selected according to the inclusion criteria. Expression analyses of transcription and protein levels confirmed internal correlation and their relevance with the tumor. Functional analysis of these genes demonstrated their enrichment in pathways, including checkpoint, chromosomal region and protein serine/threonine kinase activity. These results suggested that the characteristics of the featured genes fit well with CRC pathology and could provide new strategies for individual prevention and treatment.

• WGCNA
• biomarker
• cancer stem cell
• colorectal cancer
• stemness index