Identification of the Roles of a Stemness Index Based on mRNA Expression in the Prognosis and Metabolic Reprograming of Pancreatic Ductal Adenocarcinoma

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.

Identification of Crucial Cancer Stem Cell Genes Linked to Immune Cell Infiltration and Survival in Hepatocellular Carcinoma

Hepatocellular carcinoma is characterized by high recurrence rates and poor prognosis. Cancer stem cells contribute to tumor heterogeneity, treatment resistance, and recurrence. This study aims to identify key genes associated with stemness and immune cell infiltration in HCC. We analyzed RNA sequencing data from The Cancer Genome Atlas to calculate mRNA expression-based stemness index in HCC. A weighted gene co-expression network analysis was performed to identify stemness-related gene modules. A single-sample gene set enrichment analysis was used to evaluate immune cell infiltration. Key genes were validated using RT-qPCR. The mRNAsi was significantly higher in HCC tissues compared to adjacent normal tissues and correlated with poor overall survival. WGCNA and subsequent analyses identified 10 key genes, including minichromosome maintenance complex component 2, cell division cycle 6, forkhead box M1, NIMA-related kinase 2, Holliday junction recognition protein, DNA topoisomerase II alpha, denticleless E3 ubiquitin protein ligase homolog, maternal embryonic leucine zipper kinase, protein regulator of cytokinesis 1, and kinesin family member C1, associated with stemness and low immune cell infiltration. These genes were significantly upregulated in HCC tissues. A functional enrichment analysis revealed their involvement in cell cycle regulation. This study identified 10 key genes related to stemness and immune cell infiltration in HCC. These genes, primarily involved in cell cycle regulation, may serve as potential targets for developing more effective treatments to reduce HCC recurrence and improve patient outcomes.

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.

Development and validation of a hypoxia-stemness-based prognostic signature in pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is one of the most aggressive and fatal gastrointestinal malignancies with high morbidity and mortality worldwide. Accumulating evidence has revealed the clinical significance of the interaction between the hypoxic microenvironment and cancer stemness in pancreatic cancer progression and therapies. This study aims to identify a hypoxia-stemness index-related gene signature for risk stratification and prognosis prediction in PAAD.

Methods: The mRNA expression-based stemness index (mRNAsi) data of PAAD samples from The Cancer Genome Atlas (TCGA) database were calculated based on the one-class logistic regression (OCLR) machine learning algorithm. Univariate Cox regression and LASSO regression analyses were then performed to establish a hypoxia-mRNAsi-related gene signature, and its prognostic performance was verified in both the TCGA-PAAD and GSE62452 corhorts by Kaplan-Meier and receiver operating characteristic (ROC) analyses. Additionally, we further validated the expression levels of signature genes using the TCGA, GTEx and HPA databases as well as qPCR experiments. Moreover, we constructed a prognostic nomogram incorporating the eight-gene signature and traditional clinical factors and analyzed the correlations of the risk score with immune infiltrates and immune checkpoint genes.

Results: The mRNAsi values of PAAD samples were significantly higher than those of normal samples (p < 0.001), and PAAD patients with high mRNAsi values exhibited worse overall survival (OS). A novel prognostic risk model was successfully constructed based on the eight-gene signature comprising JMJD6, NDST1, ENO3, LDHA, TES, ANKZF1, CITED, and SIAH2, which could accurately predict the 1-, 3-, and 5-year OS of PAAD patients in both the training and external validation datasets. Additionally, the eight-gene signature could distinguish PAAD samples from normal samples and stratify PAAD patients into low- and high-risk groups with distinct OS. The risk score was closely correlated with immune cell infiltration patterns and immune checkpoint molecules. Moreover, calibration analysis showed the excellent predictive ability of the nomogram incorporating the eight-gene signature and traditional clinical factors.

Conclusion: We developed a hypoxia-stemness-related prognostic signature that reliably predicts the OS of PAAD. Our findings may aid in the risk stratification and individual treatment of PAAD patients.

Key gene network related to primary ciliary dyskinesia in hippocampus of patients with Alzheimer’s disease revealed by weighted gene co-expression network analysis

Background

Alzheimer’s disease (AD) is closely related to aging, showing an increasing incidence rate for years. As one of the main brain regions involved in AD, hippocampus has been extensively studied due to its association with many human diseases. However, little is known about its association with primary ciliary dyskinesia (PCD).

Material and Methods

The microarray data of hippocampus on AD were retrieved from the Gene Expression Omnibus (GEO) database to construct the co-expression network by weighted gene co-expression network analysis (WGCNA). The gene network modules associated with AD screened with the common genes were further annotated based on Gene Ontology (GO) database and enriched based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The protein-protein interaction (PPI) network was constructed based on STRING database to identify the hub genes in the network.

Results

Genes involved in PCD were identified in the hippocampus of AD patients. Functional analysis revealed that these genes were mainly enriched in ciliary tissue, ciliary assembly, axoneme assembly, ciliary movement, microtubule based process, microtubule based movement, organelle assembly, axoneme dynamin complex, cell projection tissue, and microtubule cytoskeleton tissue. A total of 20 central genes, e.g., DYNLRB2, ZMYND10, DRC1, DNAH5, WDR16, TTC25, and ARMC4 were identified as hub genes related to PCD in hippocampus of AD patients.

Conclusion

Our study demonstrated that AD and PCD have common metabolic pathways. These common pathways provide novel evidence for further investigation of the pathophysiological mechanism and the hub genes suggest new therapeutic targets for the diagnosis and treatment of AD and PCD.

Subjects

Bioinformatics, Cell Biology, Molecular Biology, Neurology.

Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most lethal malignancies and is currently lacking in effective biomarkers to assist in diagnosis and therapy. The aim of this study is to investigate hub genes and develop a risk signature for predicting prognosis of LUAD patients.

METHODS

RNA-sequencing data and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes associated with mRNA expression-based stemness indices (mRNAsi) in TCGA. We utilized LASSO Cox regression to assemble our predictive model. To validate our predictive model, me applied it to an external cohort.

RESULTS

mRNAsi index was significantly associated with the tissue type of LUAD, and high mRNAsi scores may have a protective influence on survival outcomes seen in LUAD patients. WGCNA indicated that the turquoise module was significantly correlated with the mRNAsi. We identified a 9-gene signature (CENPW, MCM2, STIL, RACGAP1, ASPM, KIF14, ANLN, CDCA8, and PLK1) from the turquoise module that could effectively identify a high-risk subset of these patients. Using the Kaplan-Meier survival curve, as well as the time-dependent receiver operating characteristic (tdROC) analysis, we determined that this gene signature had a strong predictive ability (AUC = 0.716). By combining the 9-gene signature with clinicopathological features, we were able to design a predictive nomogram. Finally, we additionally validated the 9-gene signature using two external cohorts from GEO and the model proved to be of high value.

CONCLUSION

Our study shows that the 9-gene mRNAsi-related signature can predict the prognosis of LUAD patient and contribute to decisions in the treatment and prevention of LUAD patients.

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.

LncRNAs specifically overexpressed in endocervical adenocarcinoma are associated with an unfavorable recurrence prognosis and the immune response

Background

Cervical cancer is the fourth most common gynecological tumor in terms of both the incidence and mortality of females worldwide. Cervical squamous cell carcinoma (CSCC) accounts for 70–80% of cervical cancers, and endocervical adenocarcinoma (EAC) accounts for 20–25%. Unlike CSCC, EAC has worse clinical outcomes and prognosis. In this study, we explored the relationship between various types of long noncoding RNAs (lncRNAs) and pathological types of cervical cancer.

Methods

RNA sequencing (RNA-Seq) and clinical data from The Cancer Genome Atlas (TCGA) were used in this study. A single-sample gene set enrichment analysis (ssGSEA) and the ESTIMATE package were used to assess lncRNA activity and immune responses, respectively. RT-qPCR was performed to verify our findings.

Results

We explored the relationship between various types of lncRNAs and pathological types of cervical cancer. A series of long intergenic noncoding RNAs (lincRNAs) and antisense RNAs, which are the major types of lncRNAs, were identified to be specifically expressed in EAC and associated with a poor recurrence prognosis in patients with cervical cancer, suggesting that they might serve as independent prognostic markers of recurrence in patients with cervical cancer. RT-qPCR was performed to verify the 10 EAC-specific lncRNAs in cervical cancer samples we collected. Furthermore, the overexpression of these lncRNAs was positively correlated with EAC pathology levels but negatively correlated with immune responses in the microenvironment of cervical cancer.

Conclusions

These lncRNAs potentially represent new biomarkers for the prediction of the recurrence prognosis and help obtain deeper insights into potential immunotherapeutic approaches for treating cervical cancer.