Identification of cancer stem cell-related biomarkers in lung adenocarcinoma by stemness index and weighted correlation network analysis

A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning

Endometrial cancer (EC) is a fatal gynecologic tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in EC. In this study, we explored the prognostic value of cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, and its correlation with immune infiltrates in EC. Transcriptome and somatic mutation profiles of EC were downloaded from TCGA database. Based on their stemness signature and DEGs, EC patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS and DFS than Stemness Subtype II. Subtype I also displayed better clinicopathological features, and genomic variations demonstrated different somatic mutation from subtype II. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. In the end, three machine learning algorithms were applied to construct a 7-gene stemness subtype risk model, which were further validated in an external independent EC cohort in our hospital. This novel stemness-based classification could provide a promising prognostic predictor for EC and may guide physicians in selecting potential responders for preferential use of immunotherapy. This novel stemness-dependent classification method has high value in predicting the prognosis, and also provides a reference for clinicians in selecting sensitive immunotherapy methods for EC patients.

LINC01614 is a promising diagnostic and prognostic marker in HNSC linked to the tumor microenvironment and oncogenic function

Background

Tumor initiation and metastasis influence tumor immune exclusion and immunosuppression. Long non-coding RNA (lncRNA) LINC01614 is associated with the prognosis and metastasis of several cancers. However, the relationship between LINC01614 and cancer immune infiltration and the biofunction of LINC01614 in head and neck squamous cell carcinoma (HNSC) remain unclear.

Methods

The Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets were used to analyze the expression difference and diagnostic value of LINC01614 in normal and tumor tissues. The correlation of pan-cancer prognosis and tumor stage of LINC01614 was analyzed based on the TCGA database. The pan-cancer association of LINC01614 expression with the tumor microenvironment (TME) including immune infiltration, expression of immune-related genes, and genomic instability parameters, was analyzed using the Spearman correlation method. The correlation between LINC01614 and tumor stemness evaluation indicators, RNA methylation-related genes, and drug resistance was also analyzed. The functional analysis of LINC01614 was performed using the clusterProfiler R package. The protein-protein interaction (PPI) network and ceRNA network of LINC01614 co-expressed genes and miRNA were constructed and visualized by STRING and Cytoscape, respectively. Finally, the cell location and influence of LINC01614 on cell proliferation and metastasis of HNSC cell lines were evaluated using FISH, CCK-8, wound-healing assay, and transwell assay.

Results

LINC01614 was found to be overexpressed in 23 cancers and showed a highly sensitive prediction value in nine cancers (AUC >0.85). LINC01614 dysregulation was associated with tumor stage in 12 cancers and significantly influenced the survival outcomes of 26 cancer types, with only Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), uterine corpus endometrial carcinoma (UCEC), and bladder urothelial carcinoma (BLCA) showing a benign influence. LINC01614 was also associated with immune cell infiltration, tumor heterogeneity, cancer stemness, RNA methylation modification, and drug resistance. The potential biological function of LINC01614 was verified in HNSC, and it was found to play important roles in proliferation, immune infiltration, immunotherapy response, and metastasis of HNSC.

Conclusion

LINC01614 may serve as a cancer diagnosis and prognosis biomarker and an immunotherapy target for specific cancers.

Integrative analysis developing and validating potential candidate biomarkers for cancer stemness features of pan-renal cell carcinoma

In our study, 49 key genes significantly associated with renal cell carcinoma (RCC) stemness were obtained. Next, we developed a molecular prognostic signature associated with stemness features of pan-RCC. The difference in OS between high-risk and low-risk group was statistically significant (P < 0.05). The area under ROC curve for 1-years OS, 5-years OS and 10-years OS was 0.759, 0.712 and 0.918, respectively. The results of validation in TCGA cohort and ICGC cohort revealed the predictive capability of this signature. Further, we selected three genes and further validation showed that these three hub genes were potential hub biomarkers for pan-RCC stemness features.

ALDH2 as a potential stem cell-related biomarker in lung adenocarcinoma: Comprehensive multi-omics analysis

Lung adenocarcinoma (LUAD) is the most prevalent lung cancer and one of the leading causes of death. Previous research found a link between LUAD and Aldehyde Dehydrogenase 2 (ALDH2), a member of aldehyde dehydrogenase gene (ALDH) superfamily. In this study, we identified additional useful prognostic markers for early LUAD identification and targeting LUAD therapy by analyzing the expression level, epigenetic mechanism, and signaling activities of ALDH2 in LUAD patients. The obtained results demonstrated that ALDH2 gene and protein expression significantly downregulated in LUAD patient samples. Furthermore, The American Joint Committee on Cancer (AJCC) reported that diminished ALDH2 expression was closely linked to worse overall survival (OS) in different stages of LUAD. Considerably, ALDH2 showed aberrant DNA methylation status in LUAD cancer. ALDH2 was found to be downregulated in the proteomic expression profile of several cell biology signaling pathways, particularly stem cell-related pathways. Finally, the relationship of ALDH2 activity with stem cell-related factors and immune system were reported. In conclusion, the downregulation of ALDH2, abnormal DNA methylation, and the consequent deficit of stemness signaling pathways are relevant prognostic and therapeutic markers in LUAD.

Bioinformatic Analysis and In Vitro and In Vivo Experiments Reveal That Fibrillarin Participates in the Promotion of Lung Metastasis in Hepatocellular Carcinoma

Lung metastasis, the most frequent metastatic pattern in hepatocellular carcinoma, is an important contributor to poor prognosis. However, the mechanisms responsible for lung metastasis in hepatocellular carcinoma remain unknown. Aiming to explore these mechanisms, weighted gene coexpression network analysis (WGCNA) was firstly used to find hub genes related to lung metastasis. Then, we obtained 67 genes related to lung metastasis in hepatocellular carcinoma which were mainly related to ribosomal pathways and functions, and a protein interaction network analysis identified that fibrillarin (FBL) might be an important hub gene. Furthermore, we found that FBL is highly expressed in hepatocellular carcinoma and that its high expression increases the rate of lung metastasis and indicates a poor prognosis. Knockdown of FBL could significantly reduce proliferation and stemness as well as inhibiting the migration and invasion of hepatocellular carcinoma cells. Moreover, we found that FBL might be involved in the regulation of MYC and E2F pathways in hepatocellular carcinoma. Finally, we demonstrated that the knockdown of FBL could suppress hepatocellular carcinoma cell growth in vivo. In conclusion, ribosome-biogenesis-related proteins, especially Fibrillarin, play important roles in lung metastasis from hepatocellular carcinoma.

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.

RecQ Helicase Somatic Alterations in Cancer

Named the “caretakers” of the genome, RecQ helicases function in several pathways to maintain genomic stability and repair DNA. This highly conserved family of enzymes consist of five different proteins in humans: RECQL1, BLM, WRN, RECQL4, and RECQL5. Biallelic germline mutations in BLM, WRN, and RECQL4 have been linked to rare cancer-predisposing syndromes. Emerging research has also implicated somatic alterations in RecQ helicases in a variety of cancers, including hematological malignancies, breast cancer, osteosarcoma, amongst others. These alterations in RecQ helicases, particularly overexpression, may lead to increased resistance of cancer cells to conventional chemotherapy. Downregulation of these proteins may allow for increased sensitivity to chemotherapy, and, therefore, may be important therapeutic targets. Here we provide a comprehensive review of our current understanding of the role of RecQ DNA helicases in cancer and discuss the potential therapeutic opportunities in targeting these helicases.

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.

Development of a 5-Gene Signature to Evaluate Lung Adenocarcinoma Prognosis Based on the Features of Cancer Stem Cells

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

Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

Background

Gastric cancer (GC) is a highly molecular heterogeneous tumor with poor prognosis. Epithelial-mesenchymal transition (EMT) process and cancer stem cells (CSCs) are reported to share common signaling pathways and cause poor prognosis in GC. Considering about the close relationship between these two processes, we aimed to establish a gene signature based on both processes to achieve better prognostic prediction in GC.

Methods

The gene signature was constructed by univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses by using The Cancer Genome Atlas (TCGA) GC cohort. We performed enrichment analyses to explore the potential mechanisms of the gene signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves were implemented to assess its prognostic value in TCGA cohort. The prognostic value of gene signature on overall survival (OS), disease-free survival (DFS), and drug sensitivity was validated in different cohorts. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of the prognostic value of gene signature for OS and DFS prediction was performed in the Fudan cohort.

Results

A prognostic signature including SERPINE1, EDIL3, RGS4, and MATN3 (SERM signature) was constructed to predict OS, DFS, and drug sensitivity in GC. Enrichment analyses illustrated that the gene signature has tight connection with the CSC and EMT processes in GC. Patients were divided into two groups based on the risk score obtained from the formula. The Kaplan-Meier analyses indicated high-risk group yielded significantly poor prognosis compared with low-risk group. Pearson’s correlation analysis indicated that the risk score was positively correlated with carboplatin and 5-fluorouracil IC50 of GC cell lines. Multivariate Cox regression analyses showed that the gene signature was an independent prognostic factor for predicting GC patients’ OS, DFS, and susceptibility to adjuvant chemotherapy.

Conclusions

Our SERM prognostic signature is of great value for OS, DFS, and drug sensitivity prediction in GC, which may give guidance to the development of targeted therapy for CSC- and EMT-related gene in the future.

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

The lung tumor microenvironment, which is composed of heterogeneous cell populations, plays an important role in the progression of lung cancer and is closely related to therapeutic efficacy. Increasing evidence has shown that stromal components play a key role in regulating tumor invasion, metastasis and drug resistance. Therefore, a better understanding of stromal components in the tumor microenvironment is helpful for the diagnosis and treatment of lung cancer. Rapid advances in technology have brought our understanding of disease into the genetic era, and single-cell RNA sequencing has enabled us to describe gene expression profiles with unprecedented resolution, enabling quantitative analysis of gene expression at the single-cell level to reveal the correlations among heterogeneity, signaling pathways, drug resistance and microenvironment molding in lung cancer, which is important for the treatment of this disease. In this paper, several common single-cell RNA sequencing methods and their advantages and disadvantages are briefly introduced to provide a reference for selection of suitable methods. Furthermore, we review the latest progress of single-cell RNA sequencing in the study of stromal cells in the lung tumor microenvironment.

LncRNA FAM83A-AS1 facilitates tumor proliferation and the migration via the HIF-1α/ glycolysis axis in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD), the major subtype of lung cancer, is among the leading cause of cancer-related death worldwide. Energy-related metabolic reprogramming metabolism is a hallmark of cancer shared by numerous cancer types, including LUAD. Nevertheless, the functional pathways and molecular mechanism by which FAM83A-AS1 acts in metabolic reprogramming in lung adenocarcinoma have not been fully elucidated.

Methods: We used transwell, wound-healing scratch assay, and metabolic assays to explore the effect of FAM83A-AS1 in LUAD cell lines. Western blotting, Co-IP assays, and ubiquitination assays were used to detect the effects of FAM83A-AS1 on HIF-1α expression, degradation, and its binding to VHL. Moreover, an in vivo subcutaneous tumor formation assay was used to detect the effect of FAM83A-AS1 on LUAD.

Results: Herein, we identified FAM83A-AS1 as a metabolism-related lncRNA, which was highly correlated with glycolysis, hypoxia, and OXPHOS pathways in LUAD patients using bioinformatics analysis. In addition, we uncovered that FAM83A-AS1 could promote the migration and invasion of LUAD cells, as well as influence the stemness of LUAD cells in vivo and vitro. Moreover, FAM83A-AS1 was shown to promote glycolysis in LUAD cell lines in vitro and in vivo, and was found to influence the expression of genes related to glucose metabolism. Besides, we revealed that FAM83A-AS1 could affect glycolysis by regulating HIF-1α degradation. Finally, we found that FAM83A-AS1 knockdown could inhibit tumor growth and suppress the expression of HIF-1α and glycolysis-related genes in vivo.

Conclusion: Our study demonstrates that FAM83A-AS1 contributes to LUAD proliferation and stemness via the HIF-1α/glycolysis axis, making it a potential biomarker and therapeutic target in LUAD patients.

miR-328-3p promotes migration and invasion by targeting H2AFX in head and neck squamous cell carcinoma

Migration and invasion are the initial step in the metastatic process, while metastasis is responsible for the poor prognosis of head and neck squamous cell carcinoma (HNSCC). Since miRNA has been found as an important regulator of gene expression at the post-transcriptional level in various diseases including carcinoma, exploring the role of miRNA in cancer metastasis will facilitate the target therapy of advanced HNSCC. MiR-328-3p has been reported to be an onco-miRNA or a tumor suppressor in several cancers. However, the role of miR-328-3p in HNSCC migration and invasion remains undefined. In this study, we first demonstrated that miR-328-3p enhanced migration and invasion of HNSCC in vitro, accompanying with a promotion of epithelial-mesenchymal transition (EMT) and mTOR activity. Meanwhile, we confirmed that miR-328-3p directly targeted the 3'UTR of H2A histone family, member X (H2AFX), which served as a tumor suppressor in migration and invasion of HNSCC. Moreover, H2AFX could partially reverse the migration and invasion of HNSCC caused by miR-328-3p. Overall, our results indicated that miR-328-3p enhanced migration and invasion of HNSCC through targeting H2AFX and activated the mTOR pathway.

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.

Cancer Stemness Associated With Prognosis and the Efficacy of Immunotherapy in Adrenocortical Carcinoma

Background

Cancer stem cells (CSCs) have been proven to influence drug resistance, recurrence, and metastasis in tumors. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in adrenocortical carcinoma.

Methods

RNA-seq data and clinical characteristics were downloaded from The Cancer Genome Atlas (TCGA). The stemness indexes, mDNAsi and mRNAsi, were calculated to classify all samples into low-score and high-score groups. Two algorithms, based on the R language, ESTIMATE and single-sample Gene Set Enrichment Analysis (ssGSEA) were used to assess the immune cell infiltration states of adrenocortical carcinoma patients. Weighted Gene Co-expression Network Analysis (WGCNA) was used to find genes that were related to the stemness of cancer. By bioinformatics methods, the correlations between biomarkers capable of predicting immune checkpoint inhibitors (ICIs) responses and stemness of cancer were explored.

Results

High-mRNAsi predicted shorter overall survival (OS) and a higher metastatic trend in adrenocortical carcinoma (ACC) patients. Compared with the low-mRNAsi group, the high-mRNAsi group had a lower ImmuneScore and StromalScroe. Twenty-two stemness-related prognostic genes were obtained by WGCNA, which focused on the function of the cell cycle and cell mitosis. Immune cell infiltration, especially CD8+T cell, increased in the low-mRNAsi group compared with the high-mRNAsi group. Lower expression of PD-L1, CTLA-4, and TIGHT was evaluated in the high-mRNAsi group.

Conclusions

ACC patients with high-mRNAsi have poor prognosis and less immune cell infiltration. Combined with the finding of lower expression of CTLA-4, TIGHT, and PD-L1 in the high-mRNAsi group, we came to the conclusion that stemness index is a potential biomarker to predict the effectiveness of ICIs.

[Progress in Single-cell RNA Sequencing of Lung Adenocarcinoma]

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and one of the main causes of cancer-related deaths. In the past decade, with the widespread use of computed tomography (CT) in routine screening for lung cancer, the incidence of LUAD presenting as small pulmonary nodules radiologically, has increased remarkably. The mechanisms of the occurrence and progression of LUADs are complex, and the prognoses of patients with LUAD vary significantly. Although significant progress has been made in targeted therapy and immunotherapy for LUADs in recent years, the drug resistance of tumor cells has not been effectively overcome, which limits the benefits of patients. With the accomplishment of the Human Genome Project, sequencing-based genomic and transcriptomics have come into the field of clinical and scientific researches. Single-cell sequencing, as a new type of sequencing method that has captured increasing attention recently, can perform specific analysis of cell populations at single-cell level, which can reveal the unique changes of each cell type. Single-cell sequencing can also provide accurate assessment on heterogeneous stromal cells and cancer cells, which is helpful to reveal the complexity of molecular compositions and differences between non- and malignant tissues. To sum up, it is an urgent need for clinicians and basic scientists to deeply understand the pathogenesis and development of LUAD, the heterogeneity of tumor microenvironment (TME) and the mechanism of drug resistance formation through single-cell sequencing, so as to discover new therapeutic targets. In this paper, we reviewed and summarized the application and progress in single-cell sequencing of LUADs.
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Mechanisms of Progression and Heterogeneity in Multiple Nodules of Lung Adenocarcinoma

Lung cancer remains the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) is thought to be caused by precursor lesions of atypical adenoma-like hyperplasia and may have extensive in situ growth before infiltration. To explore the relevant factors in heterogeneity and evolution of lung adenocarcinoma subtypes, the authors perform single-cell RNA sequencing (scRNA-seq) on tumor and normal tissue from five multiple nodules' LUAD patients and conduct a thorough gene expression profiling of cancer cells and cells in their microenvironment at single-cell level. This study gives a deep understanding of heterogeneity and evolution in early glandular neoplasia of the lung. This dataset leads to discovery of the changes in the immune microenvironment during the development of LUAD, and the development process from adenocarcinoma in situ (AIS) to invasive adenocarcinoma (IAC). This work sheds light on the direction of early tumor development and whether they are homologous.

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.

CLIP4 Shows Putative Tumor Suppressor Characteristics in Breast Cancer: An Integrated Analysis

Background: CAP-Gly domain containing linker protein family member 4 (CLIP4) plays an important role in cancers. However, its expression, prognostic value, and biological effect in breast cancer remain unclear. Methods: Data on patients diagnosed with breast cancer were retrieved from the TCGA-BRCA and other public omics databases. The expression profile of CLIP4 was analyzed using Oncomine, bc-GenExMiner, and TCGA. The prognostic value of CLIP4 was determined by Kaplan-Meier Plotter and Human Protein Atlas. Identification of genes co-expressed with CLIP4 and potential mechanism analyses were performed using UALCAN, STRING, Metascape, and GSEA. The epigenetic characteristics of CLIP4 were determined by DiseaseMeth and MEXPRESS. Results: CLIP4 was downregulated and its expression was negatively correlated with estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor type 2 (HER2) status, Nottingham prognostic index (NPI), and Scarff-Bloom-Richardson (SBR) grade in breast cancer, whereas it was positively linked to basal-like and triple negative breast cancer status. Ectopic expression of CLIP4 was related with poor prognosis. In the analysis of genes co-expressed with CLIP4, GSEA showed that the Hedgehog (Hh), JAK-STAT, ERBB, Wnt signaling pathway, cell adhesion molecules, and pathways in cancer were dissimilarly enriched in the CLIP4 expression high phenotype. Analysis of the genetics and epigenetics of CLIP4 indicated that its expression was negatively correlated with DNA methylation. Conclusion: Methylated CLIP4 may be a novel prognostic and therapeutic biomarker for breast cancer.

Identification of cancer stem cell-related biomarkers in intestinal-type and diffuse-type gastric cancer by stemness index and weighted correlation network analysis

Background

Cancer stem cells (CSCs) play an important role in drug resistance, recurrence, and metastasis of tumors. Considering the heterogeneity of tumors, this study aimed to explore the key genes regulating stem cells in intestinal-type and diffuse-type gastric cancer.

Methods

RNA-seq data and related clinical information were downloaded from The Cancer Genome Atlas (TCGA). WGCNA was used to clustered differentially expressed genes with similar expression profiles to form modules. Furtherly, based on the mRNA expression-based stemness index (mRNAsi), significant modules and key genes were identified. Next, the expression of key genes was further verified by the Oncomine database.

Results

MRNAsi scores of GC were significantly higher than that of normal tissue. Additionally, mRNAsi scores of intestinal-type GC (IGC) were significantly higher than that of diffuse-type GC (DGC). WGCNA showed that the blue module of IGC and the brown module of DGC were both the most significantly associated with mRNAsi. We screened out 16 and 43 key genes for IGC and DGC and found that these genes were closely related, respectively. Functional analysis showed the relationship between the key genes confirmed in the Oncomine database and the fate of cells.

Conclusions

In this study, 16 and 43 genes related to the characteristics of CSCs were identified in IGC and DGC, respectively. These genes were both associated with cell cycle, which could serve as therapeutic targets for the inhibition of stem cells from both types of GC.

Identification of a Novel Cancer Stemness-Associated ceRNA Axis in Lung Adenocarcinoma via Stemness Indices Analysis

The aim of this study was to identify a novel cancer stemness-related ceRNA regulatory axis in lung adenocarcinoma (LUAD) via weighted gene coexpression network analysis of a stemness index. The RNA sequencing expression profiles of 513 cancer samples and 60 normal samples were obtained from the TCGA database. Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) were identified with R software. Functional enrichment analysis was conducted using DAVID 6.8. The ceRNA network was constructed via multiple bioinformatics analyses, and the correlations between possible ceRNAs and prognosis were analyzed using Kaplan–Meier plots. WGCNA was then applied to distinguish key genes related to the mRNA expression-based stemness index (mRNAsi) in LUAD. After combining the weighted gene coexpression and ceRNA networks, a novel ceRNA regulatory axis was identified, and its biological functions were explored in vitro and vivo. In total, 1,825 DElncRNAs, 291 DEmiRNAs, and 3,742 DEmRNAs were identified. Functional enrichment analysis revealed that the DEmRNAs might be associated with LUAD onset and progression. The ceRNA network was constructed with 14 lncRNAs, 10 miRNAs, and 52 mRNAs. Kaplan–Meier analysis identified 2 DEmiRNAs, 5 DElncRNAs, and 41 DEmRNAs with remarkable prognostic power. One gene (MFAP4) in the ceRNA network was found to be closely related to mRNAsi by using WGCNA. Functional investigation further confirmed that the C8orf34-as1/miR-671-5p/MFAP4 regulatory axis has important functions in LUAD cell migration and stemness. This study provides a deeper understanding of the lncRNA–miRNA–mRNA ceRNA network and, more importantly, reveals a novel ceRNA regulatory axis, which may provide new insights into novel molecular therapeutic targets for inhibiting LUAD stem characteristics.

Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the leading fatal malignancy with high morbidity and mortality worldwide. However, due to its complicated mechanism and lack of effective clinical therapeutics, early diagnosis and prognosis are still unsatisfactory. Most of the previous studies focused on cancer stem cells (CSCs), the relationship between cancer stemness (stem-like characteristics) and anti-tumor immunity has not been clearly revealed. Therefore, this study aimed to comprehensively analyze the role of cancer stemness and tumor microenvironment (TME) in LUAD using weighted gene co-expression network analysis (WGCNA). We constructed a gene co-expression network, identified key modules, and hub genes, and further explored the relationship between hub gene expression and cancer immunological characteristics through a variety of algorithms, including Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and Gene Set Enrichment Analysis (GSEA). The hub genes were renamed stemness related genes (SRGs), whose functions were examined at the transcription and protein levels through survival analysis with additional samples, Oncomine database, immunohistochemistry, single cell RNA sequencing (scRNA-seq) and single-sample Gene Set Enrichment Analysis (ssGSEA). Subsequently, Tumor Immune Dysfunction and Exclusion (TIDE) and Connectivity Map (CMap) were implemented for treatment and prognosis analyses. As a result, 15 co-expressed SRGs (CCNA2, CCNB1, CDC20, CDCA5, CDCA8, FEN1, KIF2C, KPNA2, MCM6, NUSAP1, RACGAP1, RRM2, SPAG5, TOP2A, and TPX2) were identified. The overexpression of which was discovered to be associated with reduced immune infiltration in LUAD. It was discovered that there was a general negative correlation between cancer stemness and immunity. The expression of SRGs could probably affect our tumor occurrence, progression, the efficacy of chemotherapy and immunotherapy, and clinical outcomes. In conclusion, the 15 SRGs reported in our study may be used as potential candidate biomarkers for prognostic indicators and therapeutic targets after further validation.

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.