mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

LINC01572 promotes the malignant progression of lung adenocarcinoma by modulating p53 mediated by miRNA-338-5p/TTK axis

OBJECTIVES

Lung cancer is one of the malignant tumors that threaten human health seriously. Long non-coding RNA (lncRNA) is an important factor affecting tumorigenesis and development. However, the mechanism of lncRNA in lung cancer progression remains to be further explored.

METHODS

In this study, the TCGA database was analyzed, and LINC01572 was found to be increased in lung adenocarcinoma (LUAD) tissues. Thereafter, with the help of databases including lncBase, TargetScan, and mirDIP, as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, LINC01572/miRNA-338-5p/TTK regulatory axis and downstream p53 signaling pathway were excavated. qRT-PCR was adopted to detect levels of LINC01572, miRNA-338-5p, and TTK in LUAD cells. The role that LINC01572 played in LUAD cells was validated by CCK-8 assay, flow cytometry, colony formation, Transwell, and scratch healing assays. The binding ability between LINC01572/TTK and miRNA-338-5p was then verified by dual-luciferase and RIP analysis.

KEY FINDINGS

The results of this study demonstrated that LINC01572 was elevated in LUAD cells compared with normal cells. The overexpression of LINC01572 promoted the proliferative and migratory properties of LUAD cells but inhibited cell apoptosis. The inhibition of LINC01572 resulted in the opposite result. In addition, rescue experiments revealed that LINC01572, as a molecular sponge of miRNA-338-5p, targeted TTK to manipulate p53 for facilitating LUAD cell malignant progression. Apart from this, we constructed a mouse xenograft model and confirmed that the knockdown of LINC01572 hindered the growth of LUAD solid tumors in vivo.

CONCLUSIONS

Our findings illuminated the molecular mechanism of LINC01572 influencing LUAD and provided new insights for targeted therapy of LUAD cells.

A literature review: machine learning-based stem cell investigation

Background and Objective

Stem cell (SC) is a crucial factor of the human organ that is significantly important for clinical solutions. However, consideration of SC in the therapeutic or disease classification process is complex in terms of accurate classification and prediction. To overcome this issue, Machine learning (ML) is the most effective technique that is frequently used in cell-based clinical applications for diagnosis, treatment, and disease identification. Recently it has been implemented for SC observation which is a crucial factor for clinical solutions. Thus, the objective of this review work is to represent the effectiveness of ML techniques for SC observation from clinical perspectives with current challenges and future direction for further improvement.

Methods

In this study, we conducted a short review of ML-based applications in SCs investigation and classification for the improvement of clinical solutions. We explored studies from five scientific databases (Web of Science, Google Scholar, Scopus, ScienceDirect, and PubMed) with several keywords related to the objective of our research study. After primary and secondary screening, 15 articles were utilized for this research study and summarized the observation results in terms of ten aspects (year of publication, focused area, objective, experimented datasets, selected ML classifiers, experimental procedure, classification parameter, overall performance in terms of accuracy, advancements, and limitations) with their current limitations and future improvement directions.

Key Content and Findings

The majority of the existing literature review works are limited to focusing on specific SC-based investigation, limited evaluation attributes, and lack of challenges and future improvement suggestions. Also, most of the review work didn't consider the investigation of the effectiveness of the ML technique in SC biology. Therefore, in this paper, we investigate existing literature related to the development of clinical solutions considering ML techniques, in the area of SC and cell culture processes and highlight current challenges and future directions.

Conclusions

The majority of studies focused on the disease identification process and implemented the convolutional neural network and support vector machine techniques. The prime limitations of the investigated studies are related to the focused area, investigated SCs, the small number of experimental datasets, and validation techniques. None of the studies provided complete evidence to determine an optimal ML technique for SC to build classification or predictive models. Therefore, further concern is required to develop and improve the developed solutions including other ML techniques, large datasets, and advanced evaluation processes.

PAX2 mediated upregulation of ESPL1 contributes to cisplatin resistance in bladder cancer through activating the JAK2/STAT3 pathway

Extra spindle-polar body like 1 (ESPL1) is associated with the development of a variety of cancers, including bladder cancer, and is closely related to chemoresistance. In this study, we aimed to reveal the role of ESPL1 in bladder cancer progression and cisplatin (DDP) resistance. First, ESPL1 was found to be highly expressed in tumor tissues and cells of bladder cancer, and more highly expressed in cisplatin resistant tumor tissues or cells. The binding of PAX2 in ESPL1 promoter region was predicted by Jaspar database and verified by Ch-IP analysis and the luciferase reporter gene assay. Next, cisplatin-resistant T24 cells (T24/DDP) were established and transfected with ESPL1 siRNA (si-ESPL1) or overexpression vector (pcDNA-ESPL1) or co-transfected with PAX2 siRNA (si-PAX2) or overexpression vector (pcDNA-PAX2), and then treated with DDP or AG490, an inhibitor of JAK2. The results showed that silencing ESPL1 significantly reduced T24/DDP cell viability, colony formation and invasion, enhanced sensitivity to DDP, and induced cell apoptosis. Silencing PAX2 decreased ESPL1 expression, enhanced sensitivity to DDP, and induced apoptosis of T24/DDP cells, and inhibited activation of JAK2/STAT3 pathway. Overexpressing ESPL1 reversed the effect of PAX2 silencing on T24/DDP cells, while AG490 counteracted the reversal effect of overexpressing ESPL1. Finally, a xenograft tumor model was established and found that silencing ESPL1 or DDP treatment inhibited tumor growth, while silencing ESPL1 combined with DDP treatment had the best effect. In summary, this study suggested that PAX2-mediated ESPL1 transcriptional activation enhanced cisplatin resistance in bladder cancer by activating JAK2/STAT3 pathway.

Prognostic and immunotherapeutic potential of regulatory T cell-associated signature in ovarian cancer

Tumour-induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg-associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single-cell RNA sequencing (scRNA-Seq) for the identification of Treg-specific biomarkers, this investigation employed single-sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co-expression network analysis (WGCNA) facilitated the identification of Treg-correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg-associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs-associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

Exploring the molecular and immune-landscape of lung cancer associated with cystic airspaces

To explore the molecular biological characteristics of lung cancer associated with cystic airspaces (LCCA) and its potential roles on prognosis. A total of 165 LCCAs and 201 non-LCCAs were enrolled in this study. Bulk RNA sequencing was implemented in eight LCCAs and nine non-LCCAs to explore the differentially expressed genes. TCGA data were used to analyze LCCA-specific genes that associated with overall survival (OS). The median age was 60 (IQR 53 to 65) years in LCCA cohort. We found LCCA were predominant in men and had less visceral pleura invasion (VPI) or lympho-vascular invasion (LVI). Moreover, LCCA presented with higher histological heterogeneity. Kaplan-Meier analysis showed that patients of age more than 60 and positive VPI had significantly less PFS in LCCA. Cox regression suggested that LCCA, micropapillary subtype proportion and VPI were the independent risk factors for PFS. LCCA had up-regulated pathways associated with EMT, angiogenesis and cell migration. In addition, LCCA displayed higher levels of immunosuppressor infiltration (M2 macrophages, CAFs and MDSCs) and distinct cell death and metabolic patterns. BCR/TCR repertoire analysis revealed less BCR richness, clonality and high-abundance shared clonotypes in LCCA. Finally, Cox regression analysis identified that four cystic-specific genes, KCNK3, NRN1, PARVB and TRHDE-AS1, were associated with OS of lung adenocarcinoma (LUAD). And cystic-specific risk scores (CSRSs) were calculated to construct a nomogram, which performance well. Our study for the first time indicated significantly distinct molecular biological and immune characteristics between LCCA and non-LCCA, which provide complementary prognostic values in early-stage non-small cell lung cancer (NSCLC).

StemnesScoRe: an R package to estimate the stemness of glioma cancer cells at single-cell resolution

Background/aim

Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain tumor and one of the deadliest among all cancers. The high plasticity of glioma cancer stem cells and the resistance they develop against multiple modalities of therapy, along with their high heterogeneity, are the main challenges faced during treatment of glioblastoma. Therefore, a better understanding of the stemness characteristics of glioblastoma cells is needed. With the development of various single-cell technologies and increasing applications of machine learning, indices based on transcriptomic and/or epigenomic data have been developed to quantitatively measure cellular states and stemness. In this study, we aimed to develop a glioma-specific stemness score model using scATAC-seq data for the first time.

Materials and methods

We first applied three powerful machine-learning algorithms, i.e. random forest, gradient boosting, and extreme gradient boosting, to glioblastoma scRNA-seq data to discover the most important genes associated with cellular states. We then identified promoter and enhancer regions associated with these genes. After downloading the scATAC-seq peaks and their read counts for each patient, we identified the overlapping regions between the single-cell peaks and the peaks of genes obtained through machine-learning algorithms. Then we calculated read counts that were mapped to these overlapping regions. We finally developed a model capable of estimating the stemness score for each glioma cell using overlapping regions and the importance of genes predictive of glioblastoma cellular states. We also created an R package, accessible to all researchers regardless of their coding proficiency.

Results

Our results showed that mesenchymal-like stem cells display higher stemness scores compared to neural-progenitor-, oligodendrocyte-progenitor-, and astrocyte-like cells.

Conclusion

scATAC-seq can be used to assess heterogeneity in glioblastoma and identify cells with high stemness characteristics. The package is publicly available at https://github.com/Necla/StemnesScoRe and includes documentation with implementation of a real-data experiment.

The m6A reader IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties, and immune resistance of non-small-cell lung cancer stem cells

N6-methyladenosine (m6A) modification is the most common internal modification in eukaryotic mRNA and an important mechanism for post-transcriptional regulation of genes. This study focuses on the role of the m6A reader insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) in the malignant behaviors of non-small-cell lung cancer (NSCLC) cells and especially the cancer stem cells (CSCs). We obtained IGF2BP1 as an aberrantly upregulated gene linking to poor survival of patients with NSCLC by bioinformatics, and then confirmed increased IGF2BP1 expression in NSCLC tissues and cells, especially in the enriched CSCs. Knockdown of IGF2BP1 suppressed proliferation, mobility and epithelial-mesenchymal transition activity of NSCLC cells and CSCs, and it reduced stemness, self-renewal ability, xenograft tumorigenesis and immune resistance of the CSCs. IGF2BP1 was predicted to have a positive correlation with BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and it upregulated BUB1B expression through m6A modification. Further overexpression of BUB1B in CSCs counteracted the effects of IGF2BP1 silencing and restored the malignant phenotype, self-renewal, and immune resistance of CSCs in vitro and in vivo. Taken together, this work demonstrates that IGF2BP1 manipulates BUB1B expression to affect malignant behaviors, stem cell properties and immune resistance of NSCLC stem cells.

Development of a 5-mRNAsi-related gene signature to predict the prognosis of colon adenocarcinoma

Aim

To create a prognosis model based on mRNA-based stem index (mRNAsi) for evaluating the prognostic outcomes of colon adenocarcinoma (COAD).

Background

Generation of heterogeneous COAD cells could be promoted by the self-renewal and differentiation potential of cancer stem cells (CSCs). Biomarkers contributing to the development of COAD stem cells remained to be discovered.

Objective

To develop and validate an mRNAsi-based risk model for estimating the prognostic outcomes of patients suffering from COAD.

Methods

Samples were collected from Rectal Adenocarcinoma (TCGA-READ) PanCancer Atlas datasets, The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD), and the GSE87211 dataset. MRNAsi was calculated by one-class logistic regression (OCLR) algorithm. Under the criterion of correlation greater than 0.4, genes related to mRNAsi were screened and clustered. Meanwhile, differentially expressed genes (DEGs) between molecular subtypes were identified to establish a risk model. According to the median risk score value for immunotherapy and results from immune cell infiltration and clinicopathological analyses, clusters and patients were divided into high-RiskScore and low-RiskScore groups. Cell apoptosis and viability were detected by flow cytometer and Cell Counting Kit-8 (CCK-8) assay, respectively.

Results

A negative correlation between mRNAsi and clinical stages was observed. Three clusters of patients (C1, C2, and C3) were defined based on a total of 165 survival-related mRNAsi genes. Specifically, C1 patients had greater immune cell infiltration and a poorer prognosis. A 5-mRNAsi-gene signature (HEYL, FSTL3, FABP4, ADAM8, and EBF4) served as a prediction index for COAD prognosis. High-RiskScore patients had a poorer prognosis and higher level of immune cell infiltration. In addition, the five genes in the signature all showed a high expression in COAD cells. Knocking down HEYL promoted COAD cell apoptosis and inhibited viability.

Conclusion

Our mRNAsi risk model could better predict the prognosis of COAD patients.

An Exosome-Related Long Non-coding RNA (lncRNA)-Based Signature for Prognosis and Therapeutic Interventions in Lung Adenocarcinoma

Background The poor prognosis of lung adenocarcinoma (LUAD) has been confirmed by a large number of studies, so it is necessary to construct a prognosis model. In addition, exosome is closely related to tumors, but there are few studies on exosome-related long non-coding RNA (lncRNA) (ExolncRNA). Methods In this study, we designed a prognostic model, exosome-related lncRNA-based signature (ExoLncSig), using ExolncRNA expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA). ExolncRNAs were identified through univariate and multivariate and Lasso analyses. Subsequently, based on the ExoLncSig, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, immune function and immunotherapy analysis, drug screening, and so on were performed. Results AC026355.2, AC108136.1, AL590428.1, and LINC01312 were examined to establish the ExoLncSig. Gene enrichment analysis identified potential prognostic markers and therapeutic targets, including human leukocyte antigen (HLA), parainflammation, chemokine receptor (CCR), antigen-presenting cell (APC) co-inhibition, cancer-associated fibroblast (CAF), and myeloid-derived suppressor cell (MDSC). Moreover, we ascertained that the high-risk subgroup exhibits heightened susceptibility to pharmaceutical agents. Conclusion Our findings indicate that ExoLncSig holds promise as a valuable prognostic marker in LUAD. Furthermore, the immunogenic properties of ExolncRNAs may pave the way for the development of a therapeutic vaccine against LUAD.

Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma

Objective

Cancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism.

Methods

LUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells.

Results

A total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122.

Conclusion

Our study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.

ZDHHC11B is decreased in lung adenocarcinoma and inhibits tumorigenesis via regulating epithelial-mesenchymal transition

PURPOSE

The role and mechanism of zinc finger DHHC protein 11B (ZDHHC11B) in lung adenocarcinoma (LUAD) remain unclear. We, thus, analyzed the expression pattern, biological function, and potential mechanism of ZDHHC11B in LUAD.

METHODS

The expression level and prognostic value of ZDHHC11B were evaluated based on The Cancer Genome Atlas (TCGA) database and further confirmed in LUAD tissues and cells. The effect of ZDHHC11B on the malignant biological progression of LUAD was evaluated in vitro and in vivo. Gene set enrichment analysis (GSEA) and western blot were used to explore the molecular mechanisms of ZDHHC11B.

RESULTS

In vitro, ZDHHC11B inhibited the proliferation, migration, and invasion of LUAD cells and induced the apoptosis of LUAD cells. In addition, ZDHHC11B inhibited the growth of tumors in nude mice. GSEA revealed that ZDHHC11B expression is positively correlated with epithelial-mesenchymal transition (EMT). Western blot analysis demonstrated that molecular markers of EMT were inhibited under ZDHHC11B overexpression conditions.

CONCLUSIONS

Our findings indicated that ZDHHC11B plays a significant role in inhibiting tumorigenesis via EMT. In addition, ZDHHC11B may be a candidate molecular target for LUAD treatment.

Combined immune and DDR pathway classifier: A novel pathway-based classification aimed at tailoring personalized therapies for acute myeloid leukemia patients

Acute myeloid leukemia (AML) constitutes a group of lethal hematological malignancies with high heterogeneity, resulting in widely variable outcomes of targeted therapy and immunotherapy. A better basic understanding of the molecular pathways of AML would help greatly in tailoring treatments to patients. Here, we propose a novel subtyping protocol for AML combination therapy. Three datasets, namely, the TCGA-LAML, BeatAML and Leucegene datasets, were used in this study. Single-sample GSEA (ssGSEA) was performed to calculate the expression scores of 15 pathways, including immune-related, stromal-related, DNA damage repair (DDR)-related and oncogenic pathways. The consensus clustering was used to classify AML based on pathway score data. We identified four phenotypic clusters-IM+DDR-, IM-DDR-, IM-DDR+ and IM+DDR+-representing distinct pathway expression profiles. The IM+DDR- subtype exhibited the most robust immune function, and patients of IM+DDR- subtype were likely to derive the greatest benefit from immunotherapy. Patients in IM+DDR+ subtype had the second highest immune scores and the highest DDR scores, suggesting that combination therapy (immune + DDR-targeted therapy) is the optimal treatment. For patients of IM-DDR- subtype, we recommend the combination of venetoclax and PHA-665752. A-674563 and dovitinib could be combined with DDR inhibitors to treat patients in IM-DDR+ subtype. Moreover, single-cell analysis revealed that there are more immune cells clustered in the IM+DDR- subtype and higher number of monocyte-like cells, which exert immunosuppressive effects, in the IM+DDR+ subtype. These findings can be applied for molecular stratification of patients and might contribute to the development of personalized targeted therapies for AML.

Identification and validation of m6A-GPI signatures as a novel prognostic model for colorectal cancer

In order to develop an N6-methyladenosine-related gene prognostic index (m6A-GPI) that can predict the prognosis in colorectal cancer (CRC), we obtained m6A-related differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) and m6Avar database, seven genes were screened by weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analysis. Then, m6A-GPI was constructed based on the risk score. Survival analysis indicated that patients in the lower m6A-GPI group have more prolonged disease-free survival (DFS), and different clinical characteristic groups (tumor site and stage) also showed differential risk scores. In the analysis of the molecular characteristics, the risk score is positively associated with homologous recombination defects (HRD), copy number alterations (CNA), and the mRNA expression-based stemness index (mRNAsi). In addition, m6A-GPI also plays an essential role in tumor immune cell infiltration. The immune cell infiltration in the low m6A-GPI group is significantly higher in CRC. Moreover, we found that CIITA, one of the genes in m6A-GPI was up-regulated in CRC tissues based on real-time RT-PCR and Western blot. m6A-GPI is a promising prognostic biomarker that can be used to distinguish the prognosis of CRC patients in CRC.

A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers

Background

Stem cells play an important role in acute myeloid leukemia (AML). However, their precise effect on AML tumorigenesis and progression remains unclear.

Methods

The present study aimed to characterize stem cell-related gene expression and identify stemness biomarker genes in AML. We calculated the stemness index (mRNAsi) based on transcription data using the one-class logistic regression (OCLR) algorithm for patients in the training set. According to the mRNAsi score, we performed consensus clustering and identified two stemness subgroups. Eight stemness-related genes were identified as stemness biomarkers through gene selection by three machine learning methods.

Results

We found that patients in stemness subgroup I had a poor prognosis and benefited from nilotinib, MK-2206 and axitinib treatment. In addition, the mutation profiles of these two stemness subgroups were different, which suggested that patients in different subgroups had different biological processes. There was a strong significant negative correlation between mRNAsi and the immune score (r= -0.43, p<0.001). Furthermore, we identified eight stemness-related genes that have potential to be biomarkers, including SLC43A2, CYBB, CFP, GRN, CST3, TIMP1, CFD and IGLL1. These genes, except IGLL1, had a negative correlation with mRNAsi. SLC43A2 is expected to be a potential stemness-related biomarker in AML.

Conclusion

Overall, we established a novel stemness classification using the mRNAsi score and eight stemness-related genes that may be biomarkers. Clinical decision-making should be guided by this new signature in prospective studies.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

Background

Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs).

Methods

We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD.

Results

We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells.

Conclusion

Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

Platelet-Based Liquid Biopsies through the Lens of Machine Learning

Liquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability to the model. In this work, we have used RNA sequencing data of tumor-educated platelets (TEPs) and performed a binary classification (cancer vs. no-cancer). First, we compiled a large-scale dataset with more than a thousand donors. Further, we used different convolutional neural networks (CNNs) and boosting methods to evaluate the classifier performance. We have obtained an impressive result of 0.96 area under the curve. We then identified different clusters of splice variants using expert knowledge from the Kyoto Encyclopedia of Genes and Genomes (KEGG). Employing boosting algorithms, we identified the features with the highest predictive power. Finally, we tested the robustness of the models using test data from novel hospitals. Notably, we did not observe any decrease in model performance. Our work proves the great potential of using TEP data for cancer patient classification and opens the avenue for profound cancer diagnostics.

Stemness-related lncRNAs signature as a biologic prognostic model for head and neck squamous cell carcinoma

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are particularly important for tumor cell growth and migration, and recurrence and drug resistance, including head and neck squamous cell carcinoma (HNSCC). The purpose of this study was to explore stemness-related lncRNAs (SRlncRNAs) that could be used for prognosis of patients with HNSCC. HNSCC RNA sequencing data and matched clinical data were obtained from TCGA database, and stem cell characteristic genes related to HNSCC mRNAsi were obtained from the online database by WGCNA analysis, respectively. Further, SRlncRNAs were obtained. Then, the prognostic model was constructed to forecast patient survival through univariate Cox regression and LASSO-Cox method based on SRlncRNAs. Kaplan-Meier, ROC and AUC were used to evaluate the predictive ability of the model. Moreover, we probed the underlying biological functions, signalling pathways and immune status hidden within differences in prognosis of patients. We explored whether the model could guide personalized treatments included immunotherapy and chemotherapy for HNSCC patients. At last, RT-qPCR was performed to analyze the expressions levels of SRlncRNAs in HNSCC cell lines. A SRlncRNAs signature was identified based on 5 SRlncRNAs (AC004943.2, AL022328.1, MIR9-3HG, AC015878.1 and FOXD2-AS1) in HNSCC. Also, risk scores were correlated with the abundance of tumor-infiltrating immune cells, whereas HNSCC-nominated chemotherapy drugs were considerably different from one another. The final finding was that these SRlncRNAs were abnormally expressed in HNSCCCS according to the results of RT-qPCR. These 5 SRlncRNAs signature, as a potential prognostic biomarker, can be utilized for personalized medicine in HNSCC patients.

An Analysis Regarding the Association Between Proteasome (PSM) and Hepatocellular Carcinoma (HCC)

Background

The Proteasome (PSM) is a large multi-catalytic protease complex consisting of a 20S core particle and a 19S regulatory particle whose main function is to accept and degrade ubiquitinated substrates, are now considered as one of the potential regulators of tumor proliferation, and stemness maintenance. However, to date, studies on the relationship between PSM and hepatocellular carcinoma (HCC) are limited.

Methods

This study used a bioinformatics approach combining validation experiments to investigate the biological mechanisms that may be related with PSM. A series of experiments in vivo and in vitro were performed to explore the function of the 26S proteasome non-ATPase regulatory subunit 13 (PSMD13) in HCC.

Results

HCC patients can be divided into two clusters. Cluster 1 (C1) patients having a significantly worse prognosis than Cluster (C2). Two subtypes had significant differences in proliferation-related signaling. In particular, the frequency of TP53 mutation was significantly higher in C1 than in C2. In addition, PSM-associated genes were highly consistent with the expression of DNA repair-related signatures, suggesting a potential link between PSM and genomic instability. We also found that downregulation of PSMD13 expression significantly inhibited stemness of tumor cells and impaired the Epithelial mesenchymal transition (EMT) process. Finally, the correlation between the PSMD13 and Ki67 was found to be strong.

Conclusion

PSM is a valid predictor of prognosis and therapeutic response in patients with HCC disease. Furthermore, PSMD13 may be a potential therapeutic target.

Integrated analysis of the relation to tumor immune microenvironment and predicted value of Stonin1 gene for immune checkpoint blockage and targeted treatment in kidney renal clear cell carcinoma

BACKGROUND

Stonin1 (STON1) is an endocytic protein but its role in cancer remains unclear. Here, we investigated the immune role of STON1 in kidney renal clear cell carcinoma (KIRC).

METHODS

We undertook bioinformatics analyses of the expression and clinical significance of STON1 in KIRC through a series of public databases, and the role of STON1 in the tumor microenvironment and the predictive value for immunotherapy and targeted treatment in KIRC were identified with R packages. STON1 expression was validated in clinical KIRC tissues as well as in KIRC and normal renal tubular epithelial cells.

RESULTS

Through public databases, STON1 mRNA was found to be significantly downregulated in KIRC compared with normal controls, and decreased STON1 was related to grade, TNM stage, distant metastasis and status of KIRC patients. Compared with normal controls, STON1 was found to be downregulated in KIRC tissues and cell lines. Furthermore, OncoLnc, Kaplan-Meier, and GEPIA2 analyses also suggested that KIRC patients with high STON1 expression had better overall survival. The high STON1 group with enriched immune cells had a more favorable prognosis than the low STON1 group with decreased immune cells. Single sample Gene Set Enrichment Analysis and Gene Set Variation Analysis indicated that STON1 creates an immune non-inflamed phenotype in KIRC. Moreover, STON1 was positively associated with mismatch repair proteins and negatively correlated with tumor mutational burden. Furthermore, Single sample Gene Set Enrichment Analysis algorithm and Pearson analysis found that the low STON1 group was more sensitive to immune checkpoint blockage whereas the high STON1 group was relatively suitable for targeted treatment.

CONCLUSIONS

Decreased STON1 expression in KIRC leads to clinical progression and poor survival. Mechanically, low STON1 expression is associated with an aberrant tumor immune microenvironment. Low STON1 is likely to be a favorable indicator for immunotherapy response but adverse indicator for targeted therapeutics in KIRC.

Systemic analysis of the DNA replication regulator origin recognition complex in lung adenocarcinomas identifies prognostic and expression significance

BACKGROUND

DNA replication alteration is a hallmark of patients with lung adenocarcinoma (LUAD) and is frequently observed in LUAD progression. Origin recognition complex (ORC) 1, ORC2, ORC3, ORC4, ORC5, and ORC6 form a replication-initiator complex to mediate DNA replication, which plays a key role in carcinogenesis, while their roles in LUAD remain poorly understood.

METHODS

The mRNA and protein expression of ORCs was confirmed by the GEPIA, HPA, CPTAC, and TCGA databases. The protein-protein interaction network was analyzed by the GeneMANIA database. Functional enrichment was confirmed by the Metascape database. The effects of ORCs on immune infiltration were validated by the TIMER database. The prognostic significance of ORCs in LUAD was confirmed by the KM-plot and GENT2 databases. DNA alteration and protein structure were determined in the cBioProtal and PDB databases. Moreover, the protein expression and prognostic value of ORCs were confirmed in our LUAD data sets by immunohistochemistry (IHC) staining.

RESULTS

ORC mRNA and protein were significantly increased in patients with LUAD compared with corresponding normal tissue samples. The results of IHC staining analysis were similar result to those of the above bioinformatics analysis. Furthermore, ORC1 and ORC6 had significant prognostic values for LUAD patients. Furthermore, the ORC cooperatively promoted LUAD development by driving DNA replication, cellular senescence, and metabolic processes.

CONCLUSION

The ORC, especially ORC1/6, has important prognostic and expression significance for LUAD patients.

Establishment of Bactrian Camel Induced Pluripotent Stem Cells and Prediction of Their Unique Pluripotency Genes

Induced pluripotent stem cells (iPSCs) can differentiate into all types of cells and can be used in livestock for research on biological development, genetic breeding, and in vitro genetic resource conservation. The Bactrian camel is a large domestic animal that inhabits extreme environments and holds value in the treatment of various diseases and the development of the local economy. Therefore, we transferred four mouse genes (Oct4, Sox2, Klf4, and c-Myc) into Bactrian camel fetal fibroblasts (BCFFs) using retroviruses with a large host range to obtain Bactrian camel induced pluripotent stem cells (bciPSCs). They were comprehensively identified based on cell morphology, pluripotency gene and marker expression, chromosome number, transcriptome sequencing, and differentiation potential. The results showed the pluripotency of bciPSCs. However, unlike stem cells of other species, late formation of stem cell clones was observed; moreover, the immunofluorescence of SSEA1, SSEA3, and SSEA4 were positive, and teratoma formation took four months. These findings may be related to the extremely long gestation period and species specificity of Bactrian camels. By mining RNA sequence data, 85 potential unique pluripotent genes of Bactrian camels were predicted, which could be used as candidate genes for the production of bciPSC in the future. Among them, ASF1B, DTL, CDCA5, PROM1, CYTL1, NUP210, Epha3, and SYT13 are more attractive. In conclusion, we generated bciPSCs for the first time and obtained their transcriptome information, expanding the iPSC genetic information database and exploring the applicability of iPSCs in livestock. Our results can provide an experimental basis for Bactrian camel ESC establishment, developmental research, and genetic resource conservation.

Contribution of labor related gene subtype classification on heterogeneity of polycystic ovary syndrome

OBJECTIVE

As one of the most common endocrine disorders in women of reproductive age, polycystic ovary syndrome (PCOS) is highly heterogeneous with varied clinical features and diverse gestational complications among individuals. The patients with PCOS have 2-fold higher risk of preterm labor which is associated with substantial infant morbidity and mortality and great socioeconomic cost. The study was designated to identify molecular subtypes and the related hub genes to facilitate the susceptibility assessment of preterm labor in women with PCOS.

METHODS

Four mRNA datasets (GSE84958, GSE5090, GSE43264 and GSE98421) were obtained from Gene Expression Omnibus database. Twenty-eight candidate genes related to preterm labor or labor were yielded from the researches and our unpublished data. Then, we utilized unsupervised clustering to identify molecular subtypes in PCOS based on the expression of above candidate genes. Key modules were generated with weighted gene co-expression network analysis R package, and their hub genes were generated with CytoHubba. The probable biological function and mechanism were explored through Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis. In addition, STRING and Cytoscape software were used to identify the protein-protein interaction (PPI) network, and the molecular complex detection (MCODE) was used to identify the hub genes. Then the overlapping hub genes were predicted.

RESULTS

Two molecular subtypes were found in women with PCOS based on the expression similarity of preterm labor or labor-related genes, in which two modules were highlighted. The key modules and PPI network have five overlapping five hub genes, two of which, GTF2F2 and MYO6 gene, were further confirmed by the comparison between clustering subgroups according to the expression of hub genes.

CONCLUSIONS

Distinct PCOS molecular subtypes were identified with preterm labor or labor-related genes, which might uncover the potential mechanism underlying heterogeneity of clinical pregnancy complications in women with PCOS.

Integrative stemness characteristics associated with prognosis and the immune microenvironment in lung adenocarcinoma

BACKGROUND

To comprehensively analyze the stemness characteristics related to prognosis and the immune microenvironment in lung adenocarcinoma (LUAD).

METHODS

The OCLR machine learning method was used to calculate the stemness index (mRNAsi) of the LUAD samples. DEGs common between the low mRNAsi, normal, and high mRNAsi groups were screened and the immune-stemness genes were obtained. Then the PPI network was created and enrichment analyses were performed. Moreover, different subtypes based on immune-stemness genes associated with prognosis were identified, and the relationships between LUAD stemness and TIME variables were systematically analyzed, followed by TMB analysis.

RESULTS

Patients in the high mRNAsi groups with poor prognosis were screened along with 144 immune-stemness genes. IL-6, FPR2, and RLN3 showed a higher degree in the PPI network. A total of 26 immune-stemness genes associated with prognosis were screened. Two clusters were obtained (cluster 1 and cluster 2). Survival analysis revealed that patients in cluster 2 had a poor prognosis. A total of 12 immune cell subpopulations exhibited significant differences between cluster 1 and cluster 2 (P < 0.05). A total of 10 immune checkpoint genes exhibited significantly higher expression in cluster 1 (P < 0.05) than in cluster 2. Further, the TMB value in cluster 2 was higher than that in cluster 1 (P < 0.05).

CONCLUSION

Immune-stemness genes, including L-6, FPR2, and RLN3, might play significant roles in LUAD development via cytokine-cytokine receptor interaction, neuroactive ligand‒receptor interaction, and the JAK‒STAT pathway. Immune-stemness genes were related to tumor-infiltrating immune cells, TMB, and expression of immune checkpoint gene.

Down-Regulation of lncRNA MBNL1-AS1 Promotes Tumor Stem Cell-like Characteristics and Prostate Cancer Progression through miR-221-3p/CDKN1B/C-myc Axis

The recurrence, progression, and drug resistance of prostate cancer (PC) is closely related to the cancer stem cells (CSCs). Therefore, it is necessary to find the key regulators of prostate cancer stem cells (PCSCs). Here, we analyzed the results of a single-class logistic regression machine learning algorithm (OCLR) to identify the PCSC-associated lncRNA MBNL1-AS1. The effects of MBNL1-AS1 on the stemness of CSCs was assessed using qPCR, western blot and sphere-forming assays. The role of MBNL1-AS1 in mediating the proliferation and invasion of the PC cell lines was examined using Transwell, wounding-healing, CCK-8, EdU and animal assays. Dual-luciferase and ChIRP assays were used to examine the molecular mechanism of MBNL1-AS1 in PCSCs. MBNL1-AS1 was shown to be negatively correlated with stemness index (mRNAsi), and even prognosis, tumor progression, recurrence, and drug resistance in PC patients. The knockdown of MBNL1-AS1 significantly affected the stemness of the PC cells, and subsequently their invasive and proliferative abilities. Molecular mechanism studies suggested that MBNL1-AS1 regulates CDKN1B through competitive binding to miR-221-3p, which led to the inhibition of the Wnt signaling pathway to affect PCSCs. In conclusion, our study identified MBNL1-AS1 as a key regulator of PCSCs and examined its mechanism of action in the malignant progression of PC.

A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma

Backgrounds: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis, and immune escape of cancers. We aim to investigate Long non-coding RNAs (lncRNAs) that are correlated to NETs to find some potentially useful biomarkers for lung adenocarcinoma (LUAD), and to explore their correlations with immunotherapy and chemotherapy, as well as the tumor microenvironment. Methods: Based on the The Cancer Genome Atlas (TCGA) database, we identified the prognosis-related lncRNAs which are associated with NETs using cox regression. The patients were then separated into two clusters based on the expression of NETs-associated lncRNAs to perform tumor microenvironment analysis and immune-checkpoint analysis. Least absolute shrinkage and selection operator (LASSO) regression was then performed to establish a prognostic signature. Furthermore, nomogram analysis, tumor mutation burden analysis, immune infiltration analysis, as well as drug sensitivity analysis were performed to test the signature. Results: Using univariate cox regression, we found 10 NETs-associated lncRNAs that are associated with the outcomes of LUAD patients. Also, further analysis which separated the patients into 2 clusters showed that the 10 lncRNAs had significant correlations with the tumor microenvironment. Using LASSO regression, we finally constructed a signature to predict the outcomes of the patients based on 4 NETs-associated lncRNAs. The 4 NETs-associated lncRNAs were namely SIRLNT, AL365181.3, FAM83A-AS1, and AJ003147.2. Using Kaplan-Meier (K-M) analysis, we found that the risk model was strongly associated with the survival outcomes of the patients both in the training group and in the validation group 1 and 2 (p < 0.001, p = 0.026, and p < 0.01). Using receiver operating characteristic (ROC) curve, we tested the sensitivity combined with the specificity of the model and found that the risk model had a satisfactory level of 1-year, 3-year, and 5-year concordance index (C-index) (C = 0.661 in the training group, C = 0.679 in validation group 1, C = 0.692 in validation group 2). We also explored the immune microenvironment and immune checkpoint correlation of the risk model and found some significant results. Conclusion: We constructed a NETs-associated lncRNA signature to predict the outcome of patients with LUAD, which is associated with immunephenoscores and immune checkpoint-gene expression.

MCM2 in human cancer: functions, mechanisms, and clinical significance

Background

Aberrant DNA replication is the main source of genomic instability that leads to tumorigenesis and progression. MCM2, a core subunit of eukaryotic helicase, plays a vital role in DNA replication. The dysfunction of MCM2 results in the occurrence and progression of multiple cancers through impairing DNA replication and cell proliferation.

Conclusions

MCM2 is a vital regulator in DNA replication. The overexpression of MCM2 was detected in multiple types of cancers, and the dysfunction of MCM2 was correlated with the progression and poor prognoses of malignant tumors. According to the altered expression of MCM2 and its correlation with clinicopathological features of cancer patients, MCM2 was thought to be a sensitive biomarker for cancer diagnosis, prognosis, and chemotherapy response. The anti-tumor effect induced by MCM2 inhibition implies the potential of MCM2 to be a novel therapeutic target for cancer treatment. Since DNA replication stress, which may stimulate anti-tumor immunity, frequently occurs in MCM2 deficient cells, it also proposes the possibility that MCM2 targeting improves the effect of tumor 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.

PLK1 Is a Potential Prognostic Factor Associated with the Tumor Microenvironment in Lung Adenocarcinoma

More than 40% of lung cancers are lung adenocarcinoma (LUAD) worldwide. However, the prognosis of LUAD is poor for the lack of effective treatment methods. Our study identified PLK1 as a novel prognosis biomarker and treatment target for LUAD. Based on the Cancer Genome Atlas (TCGA) database, differentially expressed genes (DEGs) from 551 LUAD cases were analyzed for the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. To explore the biological pathways and the tumor-infiltrating immune cells (TICs) using gene set variation analysis (GSVA) and the CIBERSORT, as well as to analyze DEGs, a protein-protein interaction (PPI) network and Cox regression analysis were performed. Validation of DEGs was achieved through quantitative real-time PCR (qPCR) and immunoblotting. DEGs associated with the cell cycle were sorted out. Cell cycle scores were positively correlated with age, clinical stages, and metastasis and negatively correlated with overall survival of LUAD patients. PPI and Cox analyses showed that PLK1 could be a prognostic factor for LUAD patients. CIBERSORT analysis revealed a positive correlation between the transcription level of PLK1 and the function of CD8+ and activated memory CD4+ T cells, as well as a negative correlation with activated natural killer cells. Furthermore, PLK1 overexpression increased immune cytotoxicity, as measured by the cytolytic activity score, IFN- score, and IFN- level. There is a strong correlation between PLK1 and key features of TICs, indicating its potential as a promising prognostic biomarker for LUAD.

Stemness Analysis Uncovers That The Peroxisome Proliferator-Activated Receptor Signaling Pathway Can Mediate Fatty Acid Homeostasis In Sorafenib-Resistant Hepatocellular Carcinoma Cells

Hepatocellular carcinoma (HCC) stem cells are regarded as an important part of individualized HCC treatment and sorafenib resistance. However, there is lacking systematic assessment of stem-like indices and associations with a response of sorafenib in HCC. Our study thus aimed to evaluate the status of tumor dedifferentiation for HCC and further identify the regulatory mechanisms under the condition of resistance to sorafenib. Datasets of HCC, including messenger RNAs (mRNAs) expression, somatic mutation, and clinical information were collected. The mRNA expression-based stemness index (mRNAsi), which can represent degrees of dedifferentiation of HCC samples, was calculated to predict drug response of sorafenib therapy and prognosis. Next, unsupervised cluster analysis was conducted to distinguish mRNAsi-based subgroups, and gene/geneset functional enrichment analysis was employed to identify key sorafenib resistance-related pathways. In addition, we analyzed and confirmed the regulation of key genes discovered in this study by combining other omics data. Finally, Luciferase reporter assays were performed to validate their regulation. Our study demonstrated that the stemness index obtained from transcriptomic is a promising biomarker to predict the response of sorafenib therapy and the prognosis in HCC. We revealed the peroxisome proliferator-activated receptor signaling pathway (the PPAR signaling pathway), related to fatty acid biosynthesis, that was a potential sorafenib resistance pathway that had not been reported before. By analyzing the core regulatory genes of the PPAR signaling pathway, we identified four candidate target genes, retinoid X receptor beta (RXRB), nuclear receptor subfamily 1 group H member 3 (NR1H3), cytochrome P450 family 8 subfamily B member 1 (CYP8B1) and stearoyl-CoA desaturase (SCD), as a signature to distinguish the response of sorafenib. We proposed and validated that the RXRB and NR1H3 could directly regulate NR1H3 and SCD, respectively. Our results suggest that the combined use of SCD inhibitors and sorafenib may be a promising therapeutic approach.

Exploration of Novel Immunological Terms in Lung Cancer With Large Populations: Implications for Immunotherapy

Background

Ideal biomarkers to predict the response to immunotherapy in lung cancer are still lacking. Therefore, there is a need to explore effective biomarkers in large populations.

Objective

The objective of this study is to explore novel immunological classifications that are associated with immunotherapy response through the ssGSEA algorithm.

Methods

Six independent lung cancer cohorts were collected for analysis including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the EMBL-EBI database. The ssGSEA algorithm was performed to extract immune terms. Then, TCGA samples were involved as a training group and other cohorts were used as a validation group. After LASSO and Cox regression, prognostic associated immune terms were extracted and an immune-related risk score (IRS) signature was constructed. Furthermore, the association between IRS signature and clinical data, genome features, stemness indices analysis, tumor immune microenvironment, immunotherapy efficiency, and targeted therapy response was also investigated.

Results

A total of 1,997 samples were enrolled in this study including six large lung cancer cohorts. Fifty-four immune terms were calculated through the ssGSEA algorithm in combined cohorts. Then, a nine-immune-term risk score model named IRS signature was established to predict the prognosis in combined cohorts. We classified patients into high-risk and low-risk subgroups according to the cutoff point. Subsequently, analysis of clinical data and genome features indicated that the patients in the high-IRS group tend to have advanced clinical features (clinical stage and T classification), as well as a higher level of copy number variation burden, higher tumor burden mutation, and higher tumor stemness indices. Immune landscape analysis demonstrated that high-IRS groups exhibited lower immune cell infiltration and immune-suppressive state. More importantly, the predicted result of the Tumor Immune Dysfunction and Exclusion analysis showed that high-IRS groups might be more insensitive to immunotherapy. Meanwhile, we have also identified that high-IRS groups were associated with better efficiency of several targeted drugs.

Conclusion

To summarize, we identified a novel IRS model based on nine immune terms, which was quantified by the ssGSEA algorithm. This model had good efficacy in predicting overall survival and immunotherapy response in non-small cell lung cancer patients, which might be an underlying biomarker.

Characterization of Necroptosis-Related Molecular Subtypes and Therapeutic Response in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high morbidity and mortality and is usually associated with therapeutic resistance and poor prognosis because of individual biological heterogeneity. There is an unmet need to screen for reliable parameters, especially immunotherapy-related biomarkers to predict the patient’s outcomes. Necroptosis is a special caspase-independent form of necrotic cell death associated with the pathogenesis, progression, and prognosis of multiple tumors but the potential connection between necroptosis-related genes (NRGs) and LUAD still remains unclear. In this study, we expounded mutational and transcriptional alterations of 67 NRGs in 522 LUAD samples and proposed a consensus-clustering subtype of these patients into two cohorts with distinct immunological and clinical prognosis characteristics. Cluster B patients were associated with a better prognosis and characterized by relatively lower expression of NRGs, higher immune scores in the tumor microenvironment (TME), more mild clinical stages, and downregulated expression of immunotherapy checkpoints. Subsequently, the NRG score was further established to predict the overall survival (OS) of LUAD patients using univariate Cox, LASSO, and multivariate Cox regression analyses. The immunological characteristics and potential predictive capability of NRG scores were further validated by 583 LUAD patients in external datasets. In addition to better survival and immune-activated conditions, low-NRG-score cohorts exhibited a significant positive correlation with the mRNA stem index (mRNAsi) and tumor mutation burden (TMB) levels. Combined with classical clinical characteristics and NRG scores, we successfully defined a novel necroptosis-related nomogram to accurately predict the 1/3/5-year survival rate of individual LUAD patients, and the potential predictive capability was further estimated and validated in multiple test datasets with high AUC values. Integrated transcriptomic analysis helps us seek vital NRGs and supplements a novel clinical application of NRG scores in predicting the overall survival and therapeutic benefits for LUAD patients.

Immune- and Stemness-Related Genes Revealed by Comprehensive Analysis and Validation for Cancer Immunity and Prognosis and Its Nomogram in Lung Adenocarcinoma

Objective

Lung adenocarcinoma (LUAD) is a familiar lung cancer with a very poor prognosis. This study investigated the immune- and stemness-related genes to develop model related with cancer immunity and prognosis in LUAD.

Method

The Cancer Genome Atlas (TCGA) was utilized for obtaining original transcriptome data and clinical information. Differential expression, prognostic value, and correlation with clinic parameter of mRNA stemness index (mRNAsi) were conducted in LUAD. Significant mRNAsi-related module and hub genes were screened using weighted gene coexpression network analysis (WGCNA). Meanwhile, immune-related differential genes (IRGs) were screened in LUAD. Stem cell index and immune-related differential genes (SC-IRGs) were screened and further developed to construct prognosis-related model and nomogram. Comprehensive analysis of hub genes and subgroups, involving enrichment in the subgroup [gene set enrichment analysis (GSEA)], gene mutation, genetic correlation, gene expression, immune, tumor mutation burden (TMB), and drug sensitivity, used bioinformatics and reverse transcription polymerase chain reaction (RT-PCR) for verification.

Results

Through difference analysis, mRNAsi of LUAD group was markedly higher than that of normal group. Clinical parameters (age, gender, and T staging) were ascertained to be highly relevant to mRNAsi. MEturquoise and MEblue were found to be the most significant modules (including positive and negative correlations) related to mRNAsi via WGCNA. The functions and pathways of the two mRNAsi-related modules were mainly enriched in tumorigenesis, development, and metastasis. Combining stem cell index–related differential genes and immune-related differential genes, 30 prognosis-related SC-IRGs were screened via Cox regression analysis. Then, 16 prognosis-related SC-IRGs were screened to construct a LASSO regression model at last. In addition, the model was successfully validated by using TCGA-LUAD and GSE68465, whereas c-index and the calibration curves were utilized to demonstrate the clinical value of our nomogram. Following the validation of the model, GSEA, immune cell correlation, TMB, clinical relevance, etc., have found significant difference in high- and low-risk groups, and 16-gene expression of the SC-IRG model also was tested by RT-PCR. ADRB2, ANGPTL4, BDNF, CBLC, CX3CR1, and IL3RA were found markedly different expression between the tumor and normal group.

Conclusion

The SC-IRG model and the prognostic nomogram could accurately predict LUAD survival. Our study used mRNAsi combined with immunity that may lay a foundation for the future research studies in LUAD.

Identification of Hub Genes for Early Diagnosis and Predicting Prognosis in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is among the most common digestive system malignancies worldwide, and its pathogenesis and gene signatures remain unclear. This study explored the genetic characteristics and molecular mechanisms underlying colon cancer development. Three gene expression data sets were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was used to determine differentially expressed genes (DEGs) between COAD and normal tissues. Then, the intersection of the data sets was obtained. Metascape was used to perform the functional enrichment analyses. Next, STRING was used to build protein-protein interaction (PPI) networks. Hub genes were identified and analysed using Cytoscape. Next, survival analysis and expression analysis of the hub genes were performed. ROC curve analysis was performed for further test of the diagnostic efficacy. Finally, alterations in the hub genes were predicted and analysed by cBioPortal. Altogether, 436 DEGs were detected. The DEGs were mainly enriched in cell cycle phase transition, nuclear division, meiotic nuclear division, and cytokinesis. Based on PPI networks, 20 hub genes were selected. Among them, 6 hub genes (CCNB1, CCNA2, AURKA, NCAPG, DLGAP5, and CENPE) showed significant prognostic value in colon cancer (P < 0.05), while 5 hub genes (CDK1, CCNB1, CCNA2, MAD2L1, and DLGAP5) were associated with early colon cancer diagnosis and ROC curve analysis showed good diagnostic accuracy. In conclusion, integrated bioinformatics analysis was used to identify hub genes that reveal the potential mechanism of carcinogenesis and progression of colon cancer. The hub genes might be novel biomarkers for early diagnosis, treatment, and prognosis of colon cancer.

Biological Characterization and Clinical Value of OAS Gene Family in Pancreatic Cancer

Background

OAS gene family plays an important role in antiviral process, but its role in pancreatic cancer has not yet been studied.

Methods

We analyzed the expression, prognostic value and biological function of the OAS gene family in human pancreatic cancer through comprehensive bioinformatic analysis and cellular level validation.

Results

OAS family was highly expressed in pancreatic cancer, and this high expression significantly affected the clinical stage and prognosis of the tumor. OAS gene family was closely related to the immune infiltration of pancreatic cancer, especially neutrophils and dendritic cells, and many immune-related factors and pathways are enriched in the tumor, such as type I interferon signaling pathway and NOD-like receptor signaling pathway.

Conclusion

Taken together, high expression of OAS family is closely related to poor prognosis of pancreatic cancer. OAS gene family may serve as the biomarker and even therapeutic target of pancreatic cancer.

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.

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.

Identification of Prognostic Biomarkers in Patients With Malignant Rhabdoid Tumor of the Kidney Based on mTORC1 Signaling Pathway-Related Genes

Background: Malignant rhabdoid tumor of the kidney (MRTK) is an infrequent malignant tumor in childhood, accounting for approximately 2% of all childhood kidney tumors. Although the development of current treatments, the overall survival (OS) rate of MRTK patients is only 25%. The aim of this research was to explore the prognostic value of genes associated with the mTORC1 signaling pathway in MRTK.

Methods: The transcriptome data of MRTK samples were downloaded from the TARGET database. The 200 genes of HALLMARK_MTORC1_SIGNALING were downloaded from the Molecular Signatures Database (MSigDB). Furthermore, we applied gene set variation analysis (GSVA) to screen differentially expressed gene sets between the MRTK and normal samples. The 200 genes were combined with differentially expressed genes (DEGs) identified from differentially expressed gene sets. Then, a gene signature of mTORC1 pathway-related genes (mTRGs) was constructed in MRTK. The molecular mechanism of prognostic factors in MRTK was further analyzed using gene set enrichment analysis (GSEA). The target drugs based on these prognostic factors were explored from The Comparative Toxicogenomics Database (CTD). Moreover, six paired fresh tumor tissues and paraneoplastic tissues from children with MRTK were collected to validate the expressions of P4HA1, MLLT11, AURKA, and GOT1 in clinical samples via real-time fluorescence quantitative PCR and Western blot.

Results: A four-gene signature (P4HA1, MLLT11, AURKA, and GOT1) related to the mTORC1 pathway was developed in MRTK, which divided the MRTK patients into high-risk and low-risk groups. The patients with high-risk scores were strongly associated with reduced OS. Receiver operating characteristic (ROC) analysis indicated a good prediction performance of the four biomarker signatures. GSEA revealed that the mTOR signaling pathway was significantly enriched. The risk score was demonstrated to be an independent predictor for MRTK outcome. According to the correlation of tumor stem cell index and prognostic factors, the target drugs were obtained for the treatment of MRTK patients. Furthermore, the expressions of RT-qPCR and Western blot were consistent with RNA-sequencing data such that their expressions were significantly elevated in tumor tissues.

Conclusion: A total of four genes (P4HA1, MLLT11, AURKA, and GOT1) were screened as prognostic markers, further providing a new understanding for the treatment of patients with MRTK.

[Lung Cancer Stem-like Cells and Drug Resistance]

Lung cancer remains the leading cause of cancer-related death world-wide. Therapy resistance and relapse are considered major reasons contributing to the poor survival rates of lung cancer. Accumulated evidences have demonstrated that a small subpopulation of stem-like cells existed within lung cancer tissues and cell lines, possessing the abilities of self-renewal, multipotent differentiation and unlimited proliferation. These lung cancer stem-like cells (LCSCs) can generate tumors with high effeciency in vivo, survive cytotoxic therapies, and eventually lead to therapy resistance and recurrence. In this review, we would like to present recent knowledges on LCSCs, including the origins where they come from, the molecular features to identify them, and key mechanisms for them to survive and develop resistance, in order to provide a better view for targeting them in future clinic.
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Circular RNA CDR1as Alleviates Cisplatin-Based Chemoresistance by Suppressing MiR-1299 in Ovarian Cancer

Cisplatin (CDDP) chemoresistance seriously affects the prognosis and survival of patients with ovarian cancer (OC). Previous research has shown that circular RNA CDR1as is biologically associated with a large number of cancers. However, the molecular mechanism underlying the role of CDR1as in CDDP chemoresistance in OC remains unclear. Here, we investigated the mechanism of CDR1as in CDDP-resistant OC. First, we employed bioinformatics analysis and quantitative real-time PCR (qRT-PCR) to determine the expression of CDR1as and related RNAs in CDDP-sensitive and -resistant OC tissues and cells. Then, functional experiments were used to determine cell proliferation, invasion, migration, and apoptosis in CDDP chemoresistance and parent OC cells in vitro. The effect of CDR1as in CDDP chemoresistance OC progression was tested in nude mice in vivo. Moreover, dual-luciferase assays and RNA immunoprecipitation (RIP) were performed to confirm the interactions of CDR1as and related RNAs. Finally, we used Western blotting to determine protein expression levels. Our findings interpret the underlying mechanisms of the CDR1as/miR-1299/PPP1R12B axis and shed light on the clinical applications for CDDP-chemoresistant OC.

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

Background

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

Methods

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

Results

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

Conclusions

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

Identification of biomarkers for acute leukemia via machine learning-based stemness index

Traditional methods to understand leukemia stem cell (LSC)'s biological characteristics include constructing LSC-like cells and mouse models by transgenic or knock-in methods. However, there are some potential pitfalls in using this method, such as retroviral insertion mutagenesis, non-physiological level gene expression, non-physiological expansion, and difficulty to construct. The mRNAsi index for each sample of the Cancer Genome Atlas (TCGA) could avoid these potential pitfalls by machine learning. In this work, we aimed to construct a network of LSC genes utilizing the mRNAsi. First, mRNAsi value was analyzed with expressions distributions, survival analysis, age, and gender in acute myeloid leukemia (AML) samples. Then, we used the weighted gene co-expression network analysis (WGCNA) to construct modules of stemness genes. The correlation of the LSC genes transcription and interplay among LSC proteins was analyzed. We performed functional and pathway enrichment analysis to annotate stemness genes. Survival analysis further identified prognostic biomarkers by clinical data of TCGA and the Gene Expression Omnibus (GEO) database. We found that the result of mRNAsi overall survival is not significant, which may be due to the heterogeneity of AML in the stage of myeloid differentiation, French-American-British (FAB) classification systems. Enrichment analysis indicated that the stemness genes were biologically clustered as a group and mainly associated with cell cycle and mitosis. Moreover, 10 key genes (SNRNP40, RFC4, RFC5, CDC6, HSPE1, PA2G4, SNAP23P, DARS2, MIS18A, and HPRT1) were screened by survival analysis with the data from TCGA and GEO. Among them, RFC4 and RFC5 were the distinguished biomarkers for their double-validated prognostic value in both databases. Additionally, the expression of RFC4 and RFC5 had the same trend as mRNAsi score in FAB subtypes. In conclusion, our result demonstrated that mRNAsi based LSC-related genes were found to have strong interactions as a cluster. These genes, especially RFC4 and RFC5, could be the therapeutic targets for inhibiting the stemness characteristics of AML. This work is also a comprehensive pipeline for future cancer stem cell studies.

Expression and Clinical Significance of Origin Recognition Complex Subunit 6 in Breast Cancer – A Comprehensive Bioinformatics Analysis

Objective

We aimed to investigate the expression, diagnostic and prognostic values, and potential molecular mechanisms of the origin recognition complex (ORC) in breast cancer (BC).

Methods

Kaplan–Meier estimation was used to assess the prognostic value of ORC genes, and Oncomine, TCGA, GEO and ULCAN databases were used to analyze their expression in BC. Wilcoxon rank-sum tests were used to evaluate the relationship between ORC gene expression levels and BC clinicopathological features. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of ORC genes in BC. Survival analysis was performed using Kaplan–Meier estimation and Cox regression. A nomogram was constructed to predict 1-, 3-, and 5-year survival probabilities in BC. Gene set enrichment analysis (GSEA) and immune infiltration were used to investigate potential molecular mechanisms of the ORC.

Results

ORC1L and ORC6L were highly expressed in BC compared with healthy tissue, while ORC5L expression patterns were inconsistent; no significant differences in ORC2L, ORC3L or ORC4L expression were observed between BC and healthy tissues. ORC1L and ORC6L expression levels were significantly correlated with age, tumor (T) stage and molecular subtype; ORC5L expression was significantly correlated with age and number of nearby lymph nodes with cancer (N stage). ORC6L expression had the highest diagnostic value in BC and was an independent prognostic factor for poor overall survival (OS). ORC6L may be involved in cell cycle progression and may regulate cancer signaling pathways, including NF-κB, P53, and WNT, in BC. ORC6L expression was also associated with immune infiltration.

Conclusion

ORC1L and ORC6L are highly expressed in BC; ORC6L has a high diagnostic value and is an independent prognostic factor for poor OS. ORC6L may be involved in the initiation and progression of BC by regulating cell cycle progression, promoting cancer signaling pathway activation, and influencing tumor immune cell infiltration.

A Systematic Analysis Identifies Key Regulators Involved in Cell Proliferation and Potential Drugs for the Treatment of Human Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common and malignant cancer types. Abnormal cell proliferation, exemplified by cell cycle and cell division dysregulation, is one of the most prominent hallmarks of cancer and is responsible for recurrence, metastasis, and resistance to cancer therapy. However, LUAD-specific gene regulation and clinical significance remain obscure. Here, by using both tissues and cells from LUAD and normal lung samples, 434 increased and 828 decreased genes of biological significance were detected, including 127 cell cycle-associated genes (95 increased and 32 decreased), 66 cell division-associated genes (56 increased and 10 decreased), and 81 cell proliferation-associated genes (34 increased and 47 decreased). Among them, 12 increased genes (TPX2, CENPF, BUB1, PLK1, KIF2C, AURKB, CDKN3, BUB1B, HMGA2, CDK1, ASPM, and CKS1B) and 2 decreased genes (TACC1 and MYH10) were associated with all the three above processes. Importantly, 2 (CDKN3 and CKS1B) out of the 11 increased genes (except HMGA2) are previously uncharacterized ones in LUAD and can potentially be prognostic markers. Moreover, PLK1 could be a promising therapeutic target for LUAD. Besides, protein–protein interaction network analysis showed that CDK1 and CDC20 were the hub genes, which might play crucial roles in cell proliferation of LUAD. Furthermore, transcriptional regulatory network analysis suggested that the transcription factor E2F1 could be a key regulator in controlling cell proliferation of LUAD via expression modulation of most cell cycle-, cell division-, and cell proliferation-related DEGs. Finally, trichostatin A, hycanthone, vorinostat, and mebeverine were identified as four potential therapeutic agents for LUAD. This work revealed key regulators contributing to cell proliferation in human LUAD and identified four potential therapeutic agents for treatment strategy.

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.

Potential Role of S-Palmitoylation in Cancer Stem Cells of Lung Adenocarcinoma

S-palmitoylation, catalyzed by a family of 23 zinc finger Asp-His-His-Cys (DHHC) domain-containing (ZDHHC) protein acyltransferases localized on the cell membrane. However, stemness genes modulated by ZDHHCs in lung adenocarcinoma (LUAD) remain to be defined. Previously, we have constructed a network of cancer stem cell genes, including INCENP, based on mRNA stemness indices (mRNAsi) of LUAD. INCENP has the function of a chromosomal passenger complex locating to centromeres, which is performed by the conserved region of its N-terminal domain. INCENP protein with a deletion of the first non-conserved 26 amino acid sequence failed to target centromeres. However, the exact function of the deleted sequence has not been elucidated. To identify novel cancer stem cell-relevant palmitoylated proteins and responsible ZDHHC enzymes in LUAD, we analyzed multi-omics data obtained from the database of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA). ZDHHC5 is distinguished from the ZDHHC family for being up-regulated in mRNA and protein levels and associated with malignant prognosis. ZDHHC5 was positively associated with INCENP, and the correlation score increased with LUAD stages. CSS-Palm results showed Cys¹⁵ was the S-palmitoylation site of INCENP. Interestingly, Cys¹⁵ locates in the 1-26 aa sequence of INCENP, and is a conserved site across species. As INCENP is a nuclear protein, we predicted that the nuclear localization signal of ZDHHC5 was specific to the importin αβ pathway, and the result of immunofluorescence proves that ZDHHC5 is located in the nucleoplasm, in addition to the plasma membrane. Therefore, our study indicates the S-palmitoylation of INCENP mediated by ZDHHC5 as a potential mechanism of S-palmitoylation to modulate CSCs in LUAD.

Whole-Exome Sequencing on Circulating Tumor Cells Explores Platinum-Drug Resistance Mutations in Advanced Non-small Cell Lung Cancer

Circulating tumor cells (CTCs) have important applications in clinical practice on early tumor diagnosis, prognostic prediction, and treatment evaluation. Platinum-based chemotherapy is a fundamental treatment for non-small cell lung cancer (NSCLC) patients who are not suitable for targeted drug therapies. However, most patients progressed after a period of treatment. Therefore, revealing the genetic information contributing to drug resistance and tumor metastasis in CTCs is valuable for treatment adjustment. In this study, we enrolled nine NSCLC patients with platinum-based chemotherapy resistance. For each patient, 10 CTCs were isolated when progression occurred to perform single cell-level whole-exome sequencing (WES). Meanwhile the patients' paired primary-diagnosed formalin-fixed and paraffin-embedded samples and progressive biopsy specimens were also selected to perform WES. Comparisons of distinct mutation profiles between primary and progressive specimens as well as CTCs reflected different evolutionary mechanisms between CTC and lymph node metastasis, embodied in a higher proportion of mutations in CTCs shared with paired progressive lung tumor and hydrothorax specimens (4.4-33.3%) than with progressive lymphatic node samples (0.6-11.8%). Functional annotation showed that CTCs not only harbored cancer-driver gene mutations, including frequent mutations of EGFR and TP53 shared with primary and/or progressive tumors, but also particularly harbored cell cycle-regulated or stem cell-related gene mutations, including SHKBP1, NUMA1, ZNF143, MUC16, ORC1, PON1, PELP1, etc., most of which derived from primary tumor samples and played crucial roles in chemo-drug resistance and metastasis for NSCLCs. Thus, detection of genetic information in CTCs is a feasible strategy for studying drug resistance and discovering new drug targets when progressive tumor specimens were unavailable.

The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma

Objectives

This study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes.

Materials and Methods

The stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real‐time polymerase chain reaction (qRT‐PCR).

Results

TTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.

Conclusion

Stemness-related gene expression profiles may be a potential biomarker for HNSCC.

Identification of a lncRNA prognostic signature-related to stem cell index and its significance in colorectal cancer

Background: The relationship between long noncoding RNAs (lncRNAs) and the mRNA stemness index (mRNAsi) in colorectal cancer (CRC) is still unclear. Materials & methods: The mRNAsi, mRNAsi-related lncRNAs and their clinical significance were analyzed by bioinformatic approaches in The Cancer Genome Atlas (TCGA)-COREAD dataset. Results: mRNAsi was negatively related to pathological features but positively related to overall survival and recurrence-free survival in CRC. A five mRNAsi-related lncRNAs prognostic signature was further developed and showed independent prognostic factors related to overall survival in CRC patients, due to the five mRNAsi-related lncRNAs involved in several pathways of the cancer stem cells and malignant cancer cell phenotypes. Conclusion: The present study highlights the potential roles of mRNAsi-related lncRNAs as alternative prognostic markers.

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

Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.