Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients

Construction of a Prognostic Model based on CSC-related Genes in Patients with Colorectal Cancer

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Lack of efficient biomarkers for prognosis has limited the improvement of survival outcome in patients with CRC. Numerous studies have demonstrated the important roles of cancer stem cells (CSCs) in both treatment resistance and disease recurrence of CRC. Thus, the current study aims to construct a prognostic model based on expression level of CSC-related genes for precise molecular subtyping of CRC patients with different prognoses, TME infiltration patterns and therapeutic responses. The RNA sequencing data and clinical information were obtained from UCSC Xena database, followed by identification of differential expressed genes, univariate Cox regression, and LASSO regression to identify prognostic CSC-related genes and construct a novel prognostic risk scoring model consisting of 21 CSC-related genes. The patients in high-risk group suffered poor survival outcome (P<0.0001). Moreover, the performance of CSC-related prognostic model was validated in individual GEO datasets including GSE41258 and GSE39582 (P<0.05). Furthermore, patients with high-risk score exhibited lower response rate to immune checkpoint inhibitors as compared to those in low-risk group (17.4% vs. 28.2%), indicating the potential of CSC-related prognostic model to predict the immunotherapy response. Collectively, our findings provide an effective model to predict the immunotherapy response and survival outcome in patients with CRC.

Integrative analysis of a novel signature incorporating metabolism and stemness-related genes for risk stratification and assessing clinical outcomes and therapeutic responses in lung adenocarcinoma

BACKGROUND

Metabolism and stemness-related genes (msRGs) are critical in the development and progression of lung adenocarcinoma (LUAD). Nevertheless, reliable prognostic risk signatures derived from msRGs have yet to be established.

METHODS

In this study, we downloaded and analyzed RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We employed univariate and multivariate Cox regression analyses, along with least absolute shrinkage and selection operator (LASSO) regression analysis, to identify msRGs that are linked to the prognosis of LUAD and to develop the prognostic risk signature. The prognostic value was evaluated using Kaplan-Meier analysis and log-rank tests. We generated receiver operating characteristic (ROC) curves to evaluate the predictive capability of the prognostic signature. To estimate the relative proportions of infiltrating immune cells, we utilized the CIBERSORT algorithm and the MCPCOUNTER method. The prediction of the half-maximal inhibitory concentration (IC50) for commonly used chemotherapy drugs was conducted through ridge regression employing the "pRRophetic" R package. The validation of our analytical findings was performed through both in vivo and in vitro studies.

RESULTS

A novel five-gene prognostic risk signature consisting of S100P, GPX2, PRC1, ARNTL2, and RGS20 was developed based on the msRGs. A risk score derived from this gene signature was utilized to stratify LUAD patients into high- and low-risk groups, with the former exhibiting significantly poorer overall survival (OS). A nomogram was constructed incorporating the risk score and other clinical characteristics, showcasing strong capabilities in estimating the OS rates for LUAD patients. Furthermore, we observed notable differences in the infiltration of various immune cell subtypes, as well as in responses to immunotherapy and chemotherapy, between the low-risk and high-risk groups. Results from gene set enrichment analysis (GSEA) and in vitro studies indicated that the prognostic signature gene ARNTL2 influenced the prognosis of LUAD patients, primarily through the activation of the PI3K/AKT/mTOR signaling pathway.

CONCLUSIONS

Utilizing this gene signature for risk stratification could help with clinical treatment management and improve the prognosis of LUAD patients.

Gene Expression Profiling Regulated by lncRNA H19 Using Bioinformatic Analyses in Glioma Cell Lines

BACKGROUND/AIM

Glioma, the most common type of primary brain tumor, is characterized by high malignancy, recurrence, and mortality. Long non-coding RNA (lncRNA) H19 is a potential biomarker for glioma diagnosis and treatment due to its overexpression in human glioma tissues and its involvement in cell division and metastasis regulation. This study aimed to identify potential therapeutic targets involved in glioma development by analyzing gene expression profiles regulated by H19.

MATERIALS AND METHODS

To elucidate the role of H19 in A172 and U87MG glioma cell lines, cell counting, colony formation, and wound healing assays were conducted. RNA-seq data analysis and bioinformatics analyses were performed to reveal the molecular interactions of H19.

RESULTS

Cell-based experiments showed that elevated H19 levels were related to cancer cell survival, proliferation, and migration. Bioinformatics analyses identified 2,084 differentially expressed genes (DEGs) influenced by H19 which are involved in cancer progression. Specifically, ANXA5, CLEC18B, RAB42, CXCL8, OASL, USP18, and CDCP1 were positively correlated with H19 expression, while CSDC2 and FOXO4 were negatively correlated. These DEGs were predicted to function as oncogenes or tumor suppressors in gliomas, in association with H19.

CONCLUSION

These findings highlight H19 and its associated genes as potential diagnostic and therapeutic targets for gliomas, emphasizing their clinical significance in patient survival.

Stemness genes and miR-1247-3p expression associate with clinicopathological parameters and prognosis in lung adenocarcinoma

Lung cancer makes up one-fourth of all cancer-related mortality with the highest mortality rate among all cancers. Despite recent scientific advancements in cancer therapeutics, the 5-year survival rate of lung adenocarcinoma (LUAD) cancer patients remains below 15 percent. It has been suggested that the high mortality rate of LUAD is linked to the acquisition of progenitor-like cells with stem-like characteristics that assist the whole tumor in regulating immune cell infiltration. To examine this hypothesis further, this study mined several databases to explore the presence of stemness-related genes and miRNAs in LUAD cancers. We examine their association with immune and accessory cell infiltration rates and patient survival. We found 3 stem cell-related genes, ORC1L, KIF20A, and DLGAP5, present in LUAD that also correlate with changes in immune infiltration rates and reduced patient survival rates. Additionally, the modulation in myeloid-derived suppressor cell (MDSC) infiltration and miRNA hsa-mir-1247-3p mediated targeting of tumor suppressor SLC24A4 and oncogenes RAB3B and HJURP appears to primarily regulate LUAD patient survival. Given these findings, hsa-mir-1247-3p and/or its associated gene targets may offer a promising avenue to enhance patient survivability.

MAGE-A3 regulates tumor stemness in gastric cancer through the PI3K/AKT pathway

Gastric cancer remains a malignant disease of the digestive tract with high mortality and morbidity worldwide. However, due to its complex pathological mechanisms and lack of effective clinical therapies, the survival rate of patients after receiving treatment is not satisfactory. A increasing number of studies have focused on cancer stem cells and their regulatory properties. In this study, we first constructed a co-expression network based on the WGCNA algorithm to identify modules with different degrees of association with tumor stemness indices. After selecting the most positively correlated modules of the stemness index, we performed a consensus clustering analysis on gastric cancer samples and constructed the co-expression network again. We then selected the modules of interest and applied univariate COX regression analysis to the genes in this module for preliminary screening. The results of the screening were then used in LASSO regression analysis to construct a risk prognostic model and subsequently a sixteen-gene model was obtained. Finally, after verifying the accuracy of the module and screening for risk genes, we identified MAGE-A3 as the final study subject. We then performed in vivo and in vitro experiments to verify its effect on tumor stemness and tumour proliferation. Our data supports that MAGE-A3 is a tumor stemness regulator and a potent prognostic biomarker which can help the prediction and treatment of gastric cancer patients.

Integrative Analysis Revealed Stemness Features and a Novel Stemness-Related Classification in Colorectal Cancer Patients

Cancer stem cells play crucial roles in colorectal cancer (CRC) tumorigenesis and treatment response. This study aimed to determine the value of the mRNA stemness index (mRNAsi) in CRC and introduce a stemness-related classification to predict the outcome of patients. mRNAsi scores and RNA sequence data of CRC patients were analyzed. We found that high mRNAsi scores were related to early-stage CRC and a better patient prognosis. Two stemness-based subtypes (subtype I and II) were identified. Patients in subtype I presented a significantly better prognosis than those in subtype II. Patients in these two subtype groups presented significantly different tumor immunity scores and immune cell infiltration patterns. Genomic variations revealed that patients in subtype I had a lower tumor mutation burden than those in subtype II. A three-gene stemness subtype predictor was established, showing good diagnostic value in discriminating patients in different subtypes. A prognostic signature based on five stemness-related genes was established and validated in two independent cohorts and clinical samples, showing a better predictive performance than other clinical parameters. We concluded that mRNAsi scores were associated with the clinical outcome in CRC patients. The stemness-related classification was a promising prognostic predictor for CRC patients.

The Value of the Stemness Index in Ovarian Cancer Prognosis

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.

Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy

Metabolic signatures are frequently observed in cancer and are starting to be recognized as important regulators for tumor progression and therapy. Because metabolism genes are involved in tumor initiation and progression, little is known about the metabolic genomic profiles in low-grade glioma (LGG). Here, we applied bioinformatics analysis to determine the metabolic characteristics of patients with LGG from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We also performed the ConsensusClusterPlus, the CIBERSORT algorithm, the Estimate software, the R package "GSVA," and TIDE to comprehensively describe and compare the characteristic difference between three metabolic subtypes. The R package WGCNA helped us to identify co-expression modules with associated metabolic subtypes. We found that LGG patients were classified into three subtypes based on 113 metabolic characteristics. MC1 patients had poor prognoses and MC3 patients obtained longer survival times. The different metabolic subtypes had different metabolic and immune characteristics, and may have different response patterns to immunotherapy. Based on the metabolic subtype, different patterns were exhibited that reflected the characteristics of each subtype. We also identified eight potential genetic markers associated with the characteristic index of metabolic subtypes. In conclusion, a comprehensive understanding of metabolism associated characteristics and classifications may improve clinical outcomes for LGG.

A Novel Cancer Stemness-Related Signature for Predicting Prognosis in Patients with Colon Adenocarcinoma

Objective

To explore the cancer stemness features and develop a novel cancer stemness-related prognostic signature for colon adenocarcinoma (COAD).

Methods

We downloaded the mRNA expression data and clinical data of COAD from TCGA database and GEO database. Stemness index, mRNAsi, was utilized to investigate cancer stemness features. Weighted gene coexpression network analysis (WGCNA) was used to identify cancer stemness-related genes. Univariate and multivariate Cox regression analyses were applied to construct a prognostic risk cancer stemness-related signature. We then performed internal and external validation. The relationship between cancer stemness and COAD immune microenvironment was investigated.

Results

COAD patients with higher mRNAsi score or EREG-mRNAsi score have significant longer overall survival (OS). We identified 483 differently expressed genes (DEGs) between the high and low mRNAsi score groups. We developed a cancer stemness-related signature using fifteen genes (including RAB31, COL6A3, COL5A2, CCDC80, ADAM12, VGLL3, ECM2, POSTN, DPYSL3, PCDH7, CRISPLD2, COLEC12, NRP2, ISLR, and CCDC8) for prognosis prediction of COAD. Low-risk score was associated with significantly preferable OS in comparison with high-risk score, and the area under the ROC curve (AUC) for OS prediction was 0.705. The prognostic signature was an independent predictor for OS of COAD. Macrophages, mast cells, and T helper cells were the vital infiltration immune cells, and APC costimulation and type II IFN response were the vital immune pathways in COAD.

Conclusions

We developed and validated a novel cancer stemness-related prognostic signature for COAD, which would contribute to understanding of molecular mechanism in COAD.