Analysis of prognostic genes in the tumor microenvironment of lung adenocarcinoma

Prognostic role of long non-coding RNA USP30-AS1 in ovarian cancer: insights into immune cell infiltration in the tumor microenvironment

Ovarian cancer represents a formidable gynecologic malignancy bearing a dismal prognosis owing to the dearth of reliable early detection approaches and a high recurrence rate. Long non-coding RNAs (lncRNAs) have garnered immense attention as key orchestrators involved in diverse biological processes and take part in cancer initiation and progression. The present study investigated the potential significance of lncRNA USP30-AS1 in ovarian cancer prognosis, as well as its putative association with immune cell infiltration in tumor immune microenvironment (TIME). By analyzing publicly available datasets, we identified six lncRNAs with prognostic prediction ability, including USP30-AS1. The results revealed a significant positive correlation of USP30-AS1 expression with the infiltration of immune cells such as Th1 cells, TFH, CD8 T cells, B cells, antigen-presenting dendritic cells (aDC), and plasmacytoid dendritic cells (pDC) in ovarian cancer specimens. These findings provide compelling evidence of the potential involvement of lncRNA in the regulation of the TME in ovarian carcinoma. The outcomes from this study underscore the potential of USP30-AS1 as a promising prognostic biomarker for ovarian cancer. Additionally, the findings offer significant insights into the plausible role of lncRNAs in modulating immune activities, thus adding to our understanding of the disease biology. Additional investigations are necessary to unravel the molecular mechanisms underpinning these connections and validate the results seen in independent cohorts and experimental models.

Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

BACKGROUND

Tumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has not been fully elucidated.

METHODS

A series of bioinformatic analyses and machine learning strategies were performed to construct APM-related gene signatures to guide personalized treatment for BRCA patients. A single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF) algorithm was used to divide the cohort into different clusters and the fgsea algorithm was applied to investigate the altered signaling pathways. Random survival forest (RSF) and the least absolute shrinkage and selection operator (Lasso) Cox regression analysis were combined to construct an APM-related risk score (APMrs) signature to predict overall survival. Furthermore, a nomogram and decision tree were generated to improve predictive accuracy and risk stratification for individual patients. Based on Tumor Immune Dysfunction and Exclusion (TIDE) method, random forest (RF) and Lasso logistic regression model were combined to establish an APM-related immunotherapeutic response score (APMis). Finally, immune infiltration, immunomodulators, mutational patterns, and potentially applicable drugs were comprehensively analyzed in different APM-related risk groups. IHC staining was used to assess the expression of APM-related genes in clinical samples.

RESULTS

In this study, APMrs and APMis showed favorable performances in risk stratification and therapeutic prediction for BRCA patients. APMrs exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. APMrs was closely associated with distinct mutational patterns, immune cell infiltration and immunomodulators expression. Furthermore, the two APM-related gene signatures were independently validated in external cohorts with prognosis or immunotherapeutic responses. Potential applicable drugs and targets were mined in the APMrs-high group. APM-related genes were further validated in our in-house samples.

CONCLUSION

The APM-related gene signatures established in our study could improve the personalized assessment of survival risk and guide ICB decision-making for BRCA patients.

Identification of a novel survival predictor, CSF2RB, for female lung cancer in never smokers (LCNS) by a bioinformatics analysis

Lung cancer in never smokers (LCNS) has been considered as a separate disease and the 7th cause of cancer-related death worldwide. However, limited research has focused on "female" cohorts, which have presented a higher incidence rate. In this study, the microarray data of lung cancer tissues derived from 54 female lung cancer patients, consisting of 43 nonsmokers and 11 smokers, were selected from GSE2109 dataset. A total of 249 differentially expressed genes (DEGs) including 102 up- and 147 down-regulated genes were identified and further analyzed for gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment. By constructing protein-protein interaction (PPI) network and calculating key modules, 10 hub genes were screened out. The module analysis of the PPI network presented that the progression of female LCNS was significantly associated with immune response as chemokine activity and lipopolysaccharide response, and these biological processes (BP) might be mediated by chemokine signaling pathway and cytokine-cytokine receptor interaction. Then, survival analysis by Kaplan-Meier (K-M) Plotter online platform presented down-regulated gene colony stimulating factor 2 receptor beta common subunit (CSF2RB) of female LCNS might be involved in poor clinical outcome. Female LCNS with high expression of CSF2RB might be relevant with relative risk reduction of mortality, longer median survival time and higher 5-year survival rate, while female LCNS with low expression of CSF2RB might be implicated in a poor clinical outcome. In short, our results support CSF2RB to be a candidate survival predictor for female LCNS.

Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the one of the most common subtypes in lung cancer. Although various targeted therapies have been used in the clinical practice, the 5-year overall survival rate of patients is still low. Thus, it is urgent to identify new therapeutic targets and develop new drugs for the treatment of the LUAD patients.

METHODS

Survival analysis was used to identify the prognostic genes. Gene co-expression network analysis was used to identify the hub genes driving the tumor development. A profile-based drug repositioning approach was used to repurpose the potentially useful drugs for targeting the hub genes. MTT and LDH assay were used to measure the cell viability and drug cytotoxicity, respectively. Western blot was used to detect the expression of the proteins.

FINDINGS

We identified 341 consistent prognostic genes from two independent LUAD cohorts, whose high expression was associated with poor survival outcomes of patients. Among them, eight genes were identified as hub genes due to their high centrality in the key functional modules in the gene-co-expression network analysis and these genes were associated with the various hallmarks of cancer (e.g., DNA replication and cell cycle). We performed drug repositioning analysis for three of the eight genes (CDCA8, MCM6, and TTK) based on our drug repositioning approach. Finally, we repurposed five drugs for inhibiting the protein expression level of each target gene and validated the drug efficacy by performing in vitro experiments.

INTERPRETATION

We found the consensus targetable genes for the treatment of LUAD patients with different races and geographic characteristics. We also proved the feasibility of our drug repositioning approach for the development of new drugs for disease treatment.

Mechanics of Lung Development

We summarize how skeletal muscle and lung developmental biology fields have been bridged to benefit from mouse genetic engineering technologies and to explore the role of fetal breathing-like movements (FBMs) in lung development, by using skeletal muscle-specific mutant mice. It has been known for a long time that FBMs are essential for the lung to develop properly. However, the cellular and molecular mechanisms transducing the mechanical forces of muscular activity into specific genetic programs that propel lung morphogenesis (development of the shape, form and size of the lung, its airways, and gas exchange surface) as well as its differentiation (acquisition of specialized cell structural and functional features from their progenitor cells) are only starting to be revealed. This chapter is a brief synopsis of the cumulative findings from that ongoing quest. An update on and the rationale for our recent International Mouse Phenotyping Consortium (IMPC) search is also provided.

Combination of Tumor Mutational Burden and DNA Damage Repair Gene Mutations with Stromal/Immune Scores Improved Prognosis Stratification in Patients with Lung Adenocarcinoma

Background

Both the tumor environment and the genomic landscape of lung cancer may shape patient responses to treatments, including immunotherapy, but their joint impacts on lung adenocarcinoma (LUAD) prognosis are underexplored.

Methods

RNA sequencing data and whole-exome sequencing results were downloaded from the TCGA database, and only LUAD-related data were included in this study. Based on gene expression data, the ESTIMATE algorithm was used to estimate stromal and immune scores, and CIBERSORT analysis was used for quantification of the relative abundances of immune cells. Somatic mutations were used for calculating tumor mutation burden (TMB). Specific mutations in genes involved in DNA damage repair (DDR) pathways were identified. The individual and joint associations of stromal and immune score, TMB, and DDR gene mutations with 5-year survival were analyzed by the Kaplan–Meier method and multivariate Cox model.

Results

LUAD patients with a high (>highest 25%) stromal or immune score had prolonged survival as compared to those with a low (<lowest 25%) score (log-rank P=0.05 and 0.035, respectively). Patients with both high stromal and immune scores had the most favorable survival. Although the survival differences between patients with high (>highest 25%) and low (<lowest 25%) TMB, or between patients with mutant- and wild-type DDR genes were not statistically significant, a survival benefit from high TMB or DDR gene mutations was observed in patients with high stromal or immune scores.

Conclusion

A comprehensive evaluation of transcriptomic signatures and genomic biomarkers may provide a novel avenue for improving prognosis stratification in LUAD.

An Immune-Related Long Noncoding RNA Pair as a New Biomarker to Predict the Prognosis of Patients in Breast Cancer

Background: Immune-related long non-coding RNAs (irlncRNAs) might remodel the tumor immune microenvironment by changing the inherent properties of tumor cells and the expression of immune genes, which have been used to predict the efficacy of immunotherapy and the prognosis of various tumors. However, the value of irlncRNAs in breast cancer (BRCA) remains unclear.

Materials and Methods: Initially, transcriptome data and immune-related gene sets were downloaded from The Cancer Genome Atlas (TCGA) database. The irlncRNAs were extracted from the Immunology Database and Analysis Portal (ImmPort) database. Differently expressed irlncRNAs (DEirlncRNAs) were further identified by utilizing the limma R package. Then, univariate and multivariate Cox regression analyses were conducted to select the DEirlncRNAs associated with the prognosis of BRCA patients. In addition, the univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to determine the DEirlncRNA pairs with the independent prediction capability of prognosis in BRCA patients. Finally, the chosen DEirlncRNA pair would be evaluated in terms of survival time, clinicopathological characteristics, tumor-infiltrating immune cells, immune checkpoints (ICs), signaling pathways, and potential small-molecule drugs.

Results: A total of 21 DEirlncRNA pairs were extracted, and among them, lncRNA MIR4435-2HG and lncRNA U62317.1 were chosen to establish a risk signature that served as an independent prognostic biomarker in BRCA patients. Patients in the high-risk group had a worse prognosis than those in the low-risk group, and they also had an abundance of infiltration of CD4+ T and CD8+ T cells to enhance the immune response to tumor cells. Furthermore, the risk signature showed a strong correlation with ICs, signaling pathways, and potential small-molecule drugs.

Conclusion: Our research revealed that the risk signature independent of specific DEirlncRNA pair expression was closely associated with the prognosis and tumor immune microenvironment in BRCA patients and had the potential to function as an independent prognostic biomarker and a predictor of immunotherapy for BRCA patients, which would provide new insights for BRCA accurate treatment.

Predicting Differences in Treatment Response and Survival Time of Lung Adenocarcinoma Patients Based on a Prognostic Risk Model of Glycolysis-Related Genes

Background: Multiple factors influence the survival of patients with lung adenocarcinoma (LUAD). Specifically, the therapeutic outcomes of treatments and the probability of recurrence of the disease differ among patients with the same stage of LUAD. Therefore, effective prognostic predictors need to be identified.

Methods: Based on the tumor mutation burden (TMB) data obtained from The Cancer Genome Atlas (TCGA) database, LUAD patients were divided into high and low TMB groups, and differentially expressed glycolysis-related genes between the two groups were screened. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to obtain a prognostic model. A receiver operating characteristic (ROC) curve and a calibration curve were generated to evaluate the nomogram that was constructed based on clinicopathological characteristics and the risk score. Two data sets (GSE68465 and GSE11969) from the Gene Expression Omnibus (GEO) were used to verify the prognostic performance of the gene. Furthermore, differences in immune cell distribution, immune-related molecules, and drug susceptibility were assessed for their relationship with the risk score.

Results: We constructed a 5-gene signature (FKBP4, HMMR, B4GALT1, SLC2A1, STC1) capable of dividing patients into two risk groups. There was a significant difference in overall survival (OS) times between the high-risk group and the low-risk group (p < 0.001), with the low-risk group having a better survival outcome. Through multivariate Cox analysis, the risk score was confirmed to be an independent prognostic factor (HR = 2.709, 95% CI = 1.981–3.705, p < 0.001), and the ROC curve and nomogram exhibited accurate prediction performance. Validation of the data obtained in the GEO database yielded similar results. Furthermore, there were significant differences in sensitivity to immunotherapy, cisplatin, paclitaxel, gemcitabine, docetaxel, gefitinib, and erlotinib between the low-risk and high-risk groups.

Conclusion: Our results reveal that glycolysis-related genes are feasible predictors of survival and the treatment response of patients with LUAD.

Comprehensive analysis of DOK family genes expression, immune characteristics, and drug sensitivity in human tumors

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The expression of DOK family genes is related to overall survival (OS), clinical stage, tumor mutation, methylation, CNV, and SNV.

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DOK family genes are significantly associated with poor prognosis of UVM.

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DOK1-DOK3 has obvious correlation with tumor immunity and tumor microenvironment.

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DOK family gene is significantly related to tumor stemness and drug sensitivity.

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The expression of DOK family genes is related to the activation of EMT and hormone ER pathways, and is related to the inhibition of DNA damage response, cell cycle, and hormone AR pathways.

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DOK1 and DOK3, DOK2 and DOK3 have the significant correlation.

Introduction

Objectives

Methods

Results

Conclusions

HNRNPC, a predictor of prognosis and immunotherapy response based on bioinformatics analysis, is related to proliferation and invasion of NSCLC cells

BACKGROUND

Little is known about the relationship between N6-methyladenosine (m6A)-related genes and tumor immune microenvironment (TIME) in non-small cell lung cancer (NSCLC). It is unclear which m6A regulators are essential for NSCLC progression. The aim of this work was to excavate the role of m6A-related genes in the TIME and progression of NSCLC.

METHODS

Based on bioinformatics analysis, heterogeneous nuclear ribonucleoprotein C (HNRNPC) was considered as the most influential m6A regulator. Further study was investigated using patient samples, stable cell lines, and xenograft mice models.

RESULTS

The differentially expressed profiles of m6A-related genes were established in NSCLC, and the NSCLC samples were clustered into two subtypes with different immune infiltration and survival time. Next, we found that the risk score (RS) based on m6A-related genes was a predictor of prognosis and immunotherapy response for NSCLC, in which HNRNPC was considered as the most influential m6A regulator. In NSCLC patients, we confirmed that HNRNPC predicted poor prognosis and correlated with tumor invasion and lymph node metastasis. RNA-seq data revealed that HNRNPC was involved in cell growth, cell migration, extracellular matrix organization and angiogenesis. In vitro, we verified that HNRNPC knockdown attenuated the cell proliferation, clonogenicity, invasion and migration. In vivo, HNRNPC knockdown inhibited the tumor growth and lung metastasis. Additionally, HNRNPC knockdown was associated with high CD8 + T cell infiltration, along with elevated CD4 + T cell infiltration, collagen production and angiogenesis.

CONCLUSIONS

M6A regulator HNRNPC, a predictor of prognosis and immunotherapy response based on bioinformatics analysis, is related to proliferation and invasion of NSCLC cells.

Biological Function and Clinical Value of VPS13A in Pan-Cancer Based on Bioinformatics Analysis

Purpose

Vacuolar protein sorting-associated protein 13A (VPS13A) has been shown to be associated with rhabdomyosarcoma, gastric cancer and ovarian cancer, but the pan cancer analysis of VPS13A is still lacking, and the bioinformatics function of VPS13A has not been studied yet.

Methods

We used TCGA and GEO databases to investigate the distribution, expression and prognosis of VPS13A in 33 tumors for the first time. We used TIMER2, ULCAN databases to obtain the expression differences of VPS13A in tumor tissues and corresponding normal tissues, and further obtain the gene expression in different pathological stages of tumors from the GEPIA database. Mutation types and survival analysis of VPS13A were obtained from cBioPortal database. The relationship between VPS13A and immune infiltration was explored using TIMER2. We used the String website to obtain VPS13A binding proteins and draw the protein–protein interaction network map. JVENN was used for cross analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for gene enrichment analysis.

Results

VPS13A is highly expressed in most tumors, and gene expression is associated with prognosis in patients with tumors. The expression level of VPS13A was correlated with the infiltration depth of CD8+T cells in DLBC, LUAD, SKCM and TGCT, and was correlated with carcinoma-associated fibroblasts in BRCA, CESC, LIHC and THYM. Compared with normal tissue, VPS13A methylation levels were higher in some primary tumors. KEGG gene enrichment indicates that VPS13A is involved in RNA degradation, autophagy, cell senescence, cell cycle, apoptosis and other pathways.

Conclusion

VPS13A is closely related to the occurrence and progression of tumors and can be used as a biomarker for tumor screening and diagnosis. The level of VPS13A expression and the presence of mutations affect the prognosis of patients with certain cancers, which can be determined by early genetic testing.

Construction of competing endogenous RNA interaction network as prognostic markers in metastatic melanoma

Malignant melanoma (MM) is a malignant tumor originating from melanocytes, with high aggressiveness, high metastasis and extremely poor prognosis. MM accounts for 4% of skin cancers and 80% of mortality, and the median survival of patients with metastatic melanoma is only about 6 months, with a five-year survival rate of less than 10%. In recent years, the incidence of melanoma has gradually increased and has become one of the serious diseases that endanger human health. Competitive endogenous RNA (ceRNA) is the main model of the mechanism by which long chain non-coding RNAs (lncRNAs) play a regulatory role in the disease. LncRNAs can act as a "sponge", competitively attracting small RNAs (micoRNAs; miRNAs), thus interfering with miRNA function, and affect the expression of target gene messenger RNAs (mRNAs), ultimately promoting tumorigenesis and progression. Bioinformatics analysis can identify potentially prognostic and therapeutically relevant differentially expressed genes in MM, finding lncRNAs, miRNAs and mRNAs that are interconnected through the ceRNA network, providing further insight into gene regulation and prognosis of metastatic melanoma. Weighted co-expression networks were used to identify lncRNA and mRNA modules associated with the metastatic phenotype, as well as the co-expression genes contained in the modules. A total of 17 lncRNAs, six miRNAs, and 11 mRNAs were used to construct a ceRNA interaction network that plays a regulatory role in metastatic melanoma patients. The prognostic risk model was used as a sorter to classify the survival prognosis of melanoma patients. Four groups of ceRNA interaction triplets were finally obtained, which miR-3662 might has potential implication for the treatment of metaststic melanoma patients, and futher experiments confirmed the regulating relationship and phenotype of this assumption. This study provides new targets to regulate metastatic process, predict metastatic potential and indicates that the miR-3662 can be used in the treatment of melanoma.

Identification of Target PTEN-Based miR-425 and miR-576 as Potential Diagnostic and Immunotherapeutic Biomarkers of Colorectal Cancer With Liver Metastasis

A major complication of colorectal cancer (CRC), one of the most common and fatal types of cancers, is secondary liver metastasis. For patients with this fate, there are very few biomarkers available in clinical application, and the disease remains incurable. Recently, increasing studies demonstrated that tumorigenesis and development are closely related to immune escape, indicating that the roles of immune-related indicators might have been neglected in the past in colorectal cancer liver metastases (CRLM). Here, we unveil that elevated miR-425 and miR-576 promote CRLM through inhibiting PTEN-mediated cellular immune function. Specifically, miR-425 and miR-576 were identified for their significant upregulation in CRLM compared with the primary CRC tissues based on GSE81581 (n = 8) and GSE44121 (n = 18) datasets. Besides, we determined that the two microRNAs (miRNAs) coparticipated in restraining P53 and transforming growth factor beta (TGF-β) signaling pathways associated with tumor metastasis, and both shortened the overall survival of the patients with metastatic susceptibility. Notably, in situ hybridization on relatively large samples of paired CRC tissues (n = 157) not only substantiated that the expression of miR-425 and miR-576 was dramatically upregulated in CRLM but also revealed that they were closely related to tumor deterioration, especially liver metastases. Moreover, we further confirmed that the combination of miR-425 and miR-576 was an effective predictive model for liver metastases and poor clinical outcomes. Mechanically, downregulated PTEN (GSE81558, n = 6) was verified to be a shared target of miR-425 and miR-576 acting as metastasis-related oncogenes, on account of the presence of binding sites (+2928-+2934 and +4371-+4378, respectively) and the collaborative suppression of P53/TGF-β signaling in CRLM, which was further confirmed in CRC cells (HCT116 and SW480) based on systematic molecular biology experiments. Importantly, the target PTEN was strongly associated with microsatellite instability, tumor microenvironment, and immune cell infiltration. Thus, we speculate that miR-425 and miR-576 are novel biomarkers for CRLM prevention and immunotherapy and upstream inhibitors of the PTEN-P53/TGF-β function axis.

Prognostic Significance of Gene Signature of Tertiary Lymphoid Structures in Patients With Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a highly mortal cancer. Tertiary lymphoid structures (TLS) are ectopic lymphoid organs with similar morphological and molecular characters to secondary lymphoid organ. The aim of this study is to investigate the prognostic effect of a gene signature associated with TLSs, including B-cell-specific genes.

Methods

Clinical data of 515 LUAD patients in the TGCA cohort were used to examine the relationship of TLS signature with immune microenvironment, tumor mutational burden (TMB), and driver gene mutations. Patients were divided into the TLS signature high group and TLS signature low group, and comparative analysis of survival and its influencing factors between the two groups was performed. The resulting data were then validated in the GSE37745 cohort.

Results

TLS signature high group had significantly better overall survival (OS) and progression-free interval (PFI) as well as significantly higher infiltration of immune cell subsets, cancer immune cycle (CIC) signature except for immunogram score2 (IGS2), and expression of major checkpoint genes than the TLS signature low group. Notably, while TLS signature was not markedly associated with TMB and mutation frequencies of driver genes, there were significant differences in overall survival of patients with given mutation status of EGFR, KRAS, BRAF and TP53 genes between the TLS signature high and low groups.

Conclusion

This study provided evidence that LUAD patients with high TLS signature had a favorable immune microenvironment and better prognosis, suggesting that TLS signature is an independent positive prognostic factor for LUAD patients.

Association of RGS20 expression with the progression and prognosis of renal cell carcinoma

Regulator of G protein signaling 20 (RGS20) has been shown to be highly expressed in various types of cancer. The present study aimed to investigate the effects of RGS20 in patients with renal cell carcinoma (RCC) and in RCC cells. Bioinformatics analysis was performed to analyze the role of RGS20 in RCC. Quantitative PCR and western blotting were used to determine the mRNA and protein expression levels of RGS20 in cells, respectively. After RGS20 inhibition, the proliferation, apoptosis, migration and invasiveness of A-498 cells were tested using MTT assay, EdU assay, propidium iodide staining, Annexin V-FITC/PI kit, wound healing assay and Transwell assay. High RGS20 expression was closely associated with the progression and immune infiltration of RCC, and may be considered as an independent indicator of poor prognosis in RCC. After knocking down RGS20, the proliferation, migration and invasiveness of cells were impaired, the cell cycle was arrested at the G₀/G₁ phase, and the level of apoptosis was increased. In addition, the mRNA expression levels of securin, CDC20 and cyclin B1 were decreased in RGS20-knockdown cells. RGS20 expression was significantly associated with the infiltration level of activated CD4 T cells, type 1 T helper cells and activated dendritic cells. In summary, RGS20 expression was associated with RCC progression and poor prognosis; thus, it may be used to estimate the prognosis of RCC and may serve as a new potential treatment strategy for RCC.

[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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Prediction of tumor purity from gene expression data using machine learning

MOTIVATION

Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity.

RESULTS

We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system.

AVAILABILITY

The machine learning models constructed for this study are available at https://github.com/BonilKoo/ML_purity.

CCR4, CCR8, and P2RY14 as Prognostic Factors in Head and Neck Squamous Cell Carcinoma Are Involved in the Remodeling of the Tumor Microenvironment

The tumor microenvironment (TME) plays a critical role in the initiation and progression of cancer. However, the specific mechanism of its regulation in head and neck squamous cell carcinoma (HNSCC) remains unclear. In this study, we first applied the ESTIMATE method to calculate the immune and stromal scores in patients’ tumor tissues from The Cancer Genome Atlas (TCGA) database. GSE41613, GSE30784, and GSE37991 data sets from the Gene Expression Omnibus (GEO) database were recruited for further validation. Differentially expressed genes (DEGs) were identified and then analyzed by Cox regression analysis and protein-protein interaction (PPI) network construction. DEGs significantly associated with prognosis and TME will be identified as hub genes. These genes were also validated at the protein level by immunohistochemical analysis of 10 pairs of primary tumor tissues and the adjacent normal tissues from our institution. The relationship between hub genes expression and immune cell fraction estimated by CIBERSORT software was also examined. 275 DEGs were significantly associated with TME. CCR4, CCR8, and P2RY14 have then identified as hub genes by intersection Cox and PPI analysis. Further investigation revealed that the expression of CCR4, CCR8, and P2RY14 was negatively correlated with clinicopathological characteristics (clinical stage, T stage) and positively associated with survival in HNSCC patients, especially in male patients. The expression of CCR8 and P2RY14 was lower in males than in females. CCR8 and P2RY14 were differentially expressed in tumor tissues than normal tissues, and the results were validated at the protein level by immunohistochemistry experiments. Gene set enrichment analysis (GSEA) showed that the high expression groups’ hub genes were mainly enriched for immune-related activities. In the low-expression groups, genes were primarily enriched in metabolic pathways. CIBERSORT results showed that the expression of these genes was all negatively correlated with the fraction of memory B cells and positively correlated with the fraction of the other four cells, including naive B cells, resting T cells CD4 memory, T cells follicular helper, and T cells regulatory (Tregs). The results suggest that CCR4, CCR8, and P2RY14 may be responsible for maintaining the immune dominance of TME, thus leading to a better prognosis.