Bioinformatics Analysis Reveals an Association Between Cancer Cell Stemness, Gene Mutations, and the Immune Microenvironment in Stomach Adenocarcinoma

Human-like adrenal features in Chinese tree shrews revealed by multi-omics analysis of adrenal cell populations and steroid synthesis

The Chinese tree shrew ( Tupaia belangeri chinensis) has emerged as a promising model for investigating adrenal steroid synthesis, but it is unclear whether the same cells produce steroid hormones and whether their production is regulated in the same way as in humans. Here, we comprehensively mapped the cell types and pathways of steroid metabolism in the adrenal gland of Chinese tree shrews using single-cell RNA sequencing, spatial transcriptome analysis, mass spectrometry, and immunohistochemistry. We compared the transcriptomes of various adrenal cell types across tree shrews, humans, macaques, and mice. Results showed that tree shrew adrenal glands expressed many of the same key enzymes for steroid synthesis as humans, including CYP11B2, CYP11B1, CYB5A, and CHGA. Biochemical analysis confirmed the production of aldosterone, cortisol, and dehydroepiandrosterone but not dehydroepiandrosterone sulfate in the tree shrew adrenal glands. Furthermore, genes in adrenal cell types in tree shrews were correlated with genetic risk factors for polycystic ovary syndrome, primary aldosteronism, hypertension, and related disorders in humans based on genome-wide association studies. Overall, this study suggests that the adrenal glands of Chinese tree shrews may consist of closely related cell populations with functional similarity to those of the human adrenal gland. Our comprehensive results (publicly available at http://gxmujyzmolab.cn:16245/scAGMap/) should facilitate the advancement of this animal model for the investigation of adrenal gland disorders.

Developing a prognosis and chemotherapy evaluating model for colon adenocarcinoma based on mitotic catastrophe-related genes

Mitotic catastrophe (MC) is a novel form of cell death that plays an important role in the treatment and drug resistance of colon adenocarcinoma (COAD). However, MC related genes in COAD treatment and prognosis evaluation are rarely studied. In this study, the transcriptome data, somatic mutation and copy number variation data were obtained from The Cancer Genome Atlas (TCGA) database. The mitotic catastrophe related genes (MCRGs) were obtained from GENCARDS website. Differential gene analysis was conducted with LIMMA package. Univariate Cox regression analysis was used to identify prognostic related genes. Mutation analysis was performed and displayed by maftools package. RCircos package was used for localizing the position of genes on chromosomes. "Glmnet" R package was applied for constructing a risk model via the LASSO regression method. Consensus clustering analyses was implemented for clustering different subtypes. Functional enrichment analysis through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods, immune infiltration analysis via single sample gene set enrichment analysis (ssGSEA), tumor mutation burden and drug sensitivity analysis by pRRophetic R package were also carried out for risk model or molecular subtype's assessment. Additionally, the connections between the expression of hub genes and overall survival (OS) were obtained from online Human Protein Atlas (HPA) website. Real-Time Quantitative Polymerase Chain Reaction (RT‑qPCR) further validated the expression of hub genes. A total of 207 differentially expressed MCRGs were selected in the TCGA cohort, 23 of which were significantly associated with OS in COAD patients. Subsequently, we constructed risk score prognostic models with 5 hub MCRGs, including SYCE2, SERPINE1, TRIP6, LIMK1, and EEPD1. The high-risk patients suffered from poorer prognosis. Furthermore, we developed a nomogram that gathered age, sex, staging, and risk score to accurately forecast the clinical survival outcomes in 1, 3, and 5 years. The results of functional enrichment suggested a significant correlation between MCRGs characteristics and cancer progression, with important implications for the immune microenvironment. Moreover, patients who displayed high TMB and high risk score showed worse prognosis, and risk characteristics were associated with different chemotherapeutic agents. Finally, RT‑qPCR verified the increased expression of the five MCRGs in clinical samples. The five MCRGs in the prognostic signature were associated with prognosis, and could be treated as reliable prognostic biomarkers and therapeutic targets for COAD patients with distinct clinicopathological characteristics, thereby providing a foundation for the precise application of pertinent drugs in COAD patients.

Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma

BACKGROUND

Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC.

METHODS

Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets.

RESULTS

LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses.

CONCLUSION

The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.

Iberverin exhibits antineoplastic activities against human hepatocellular carcinoma via DNA damage-mediated cell cycle arrest and mitochondrial-related apoptosis

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence and mortality rates in the world. Isothiocyanates (ITCs), bioactive substances present primarily in the plant order Brassicales, have been proved to be promising candidates for novel anti-HCC drugs with chemopreventive and anticancer activities. Iberverin, a predominant ITC isolated from the seeds of oxheart cabbage, has been discovered with anticancer property in lung cancer cells. However, the roles of iberverin in HCC remain elusive. In the present study, the effect and potential mechanisms of iberverin against human HCC were dissected. We demonstrated that low concentrations of iberverin inhibited cell proliferation, suppressed migration and induced mitochondrial-related apoptosis in vitro, and hampered tumorigenicity in vivo, with no obvious toxicity. Furthermore, we found that iberverin treatment induced DNA damage and G2/M phase arrest. Iberverin treatment also caused increased intracellular reactive oxygen species formation and glutathione depletion. Taken together, these results suggest that iberverin promotes mitochondrial-mediated apoptosis and induces DNA damage and G2/M cell cycle arrest in HCC by enhancing oxidative stress. Our findings provide better understanding of the anti-HCC mechanisms of ITCs and the potential for the natural product iberverin as a promising new anti-HCC biotherapeutic.

Low-density lipoprotein receptor promotes crosstalk between cell stemness and tumor immune microenvironment in breast cancer: a large data-based multi-omics study

BACKGROUND

Tumor cells with stemness in breast cancer might facilitate the immune microenvironment's suppression process and led to anti-tumor immune effects. The primary objective of this study was to identify potential targets to disrupt the communication between cancer cell stemness and the immune microenvironment.

METHODS

In this study, we initially isolated tumor cells with varying degrees of stemness using a spheroid formation assay. Subsequently, we employed RNA-seq and proteomic analyses to identify genes associated with stemness through gene trend analysis. These stemness-related genes were then subjected to pan-cancer analysis to elucidate their functional roles in a broader spectrum of cancer types. RNA-seq data of 3132 patients with breast cancer with clinical data were obtained from public databases. Using the identified stemness genes, we constructed two distinct stemness subtypes, denoted as C1 and C2. We subsequently conducted a comprehensive analysis of the differences between these subtypes using pathway enrichment methodology and immune infiltration algorithms. Furthermore, we identified key immune-related stemness genes by employing lasso regression analysis and a Cox survival regression model. We conducted in vitro experiments to ascertain the regulatory impact of the key gene on cell stemness. Additionally, we utilized immune infiltration analysis and pan-cancer analysis to delineate the functions attributed to this key gene. Lastly, single-cell RNA sequencing (scRNA-seq) was employed to conduct a more comprehensive examination of the key gene's role within the microenvironment.

RESULTS

In our study, we initially identified a set of 65 stemness-related genes in breast cancer cells displaying varying stemness capabilities. Subsequently, through survival analysis, we pinpointed 41 of these stemness genes that held prognostic significance. We observed that the C2 subtype exhibited a higher stemness capacity compared to the C1 subtype and displayed a more aggressive malignancy profile. Further analysis using Lasso-Cox algorithm identified LDLR as a pivotal immune-related stemness gene. It became evident that LDLR played a crucial role in shaping the immune microenvironment. In vitro experiments demonstrated that LDLR regulated the cell stemness of breast cancer. Immune infiltration analysis and pan-cancer analysis determined that LDLR inhibited the proliferation of immune cells and might promote tumor cell progression. Lastly, in our scRNA-seq analysis, we discovered that LDLR exhibited associations with stemness marker genes within breast cancer tissues. Moreover, LDLR demonstrated higher expression levels in tumor cells compared to immune cells, further emphasizing its relevance in the context of breast cancer.

CONCLUSION

LDLR is an important immune stemness gene that regulates cell stemness and enhances the crosstalk between breast cancer cancer cell stemness and tumor immune microenvironment.

CRISPR-cas9 screening identified lethal genes enriched in Hippo kinase pathway and of predictive significance in primary low-grade glioma

BACKGROUND

Low-grade gliomas (LGG) are a type of brain tumor that can be lethal, and it is essential to identify genes that are correlated with patient prognosis. In this study, we aimed to use CRISPR-cas9 screening data to identify key signaling pathways and develop a genetic signature associated with high-risk, low-grade glioma patients.

METHODS

The study used CRISPR-cas9 screening data to identify essential genes correlated with cell survival in LGG. We used RNA-seq data to identify differentially expressed genes (DEGs) related to cell viability. Moreover, we used the least absolute shrinkage and selection operator (LASSO) method to construct a genetic signature for predicting overall survival in patients. We performed enrichment analysis to identify pathways mediated by DEGs, overlapping genes, and genes shared in the Weighted correlation network analysis (WGCNA). Finally, the study used western blot, qRT-PCR, and IHC to detect the expression of hub genes from signature in clinical samples.

RESULTS

The study identified 145 overexpressed oncogenes in low-grade gliomas using the TCGA database. These genes were intersected with lethal genes identified in the CRISPR-cas9 screening data from Depmap database, which are enriched in Hippo pathways. A total of 19 genes were used to construct a genetic signature, and the Hippo signaling pathway was found to be the predominantly enriched pathway. The signature effectively distinguished between low- and high-risk patients, with high-risk patients showing a shorter overall survival duration. Differences in hub gene expression were found in different clinical samples, with the protein and mRNA expression of REP65 being significantly up-regulated in tumor cells. The study suggests that the Hippo signaling pathway may be a critical regulator of viability and tumor proliferation and therefore is an innovative new target for treating cancerous brain tumors, including low-grade gliomas.

CONCLUSION

Our study identified a novel genetic signature associated with high-risk, LGG patients. We found that the Hippo signaling pathway was significantly enriched in this signature, indicating that it may be a critical regulator of tumor viability and proliferation in LGG. Targeting the Hippo pathway could be an innovative new strategy for treating LGG.

Regulatory network and targeted interventions for CCDC family in tumor pathogenesis

CCDC (coiled-coil domain-containing) is a coiled helix domain that exists in natural proteins. There are about 180 CCDC family genes, encoding proteins that are involved in intercellular transmembrane signal transduction and genetic signal transcription, among other functions. Alterations in expression, mutation, and DNA promoter methylation of CCDC family genes have been shown to be associated with the pathogenesis of many diseases, including primary ciliary dyskinesia, infertility, and tumors. In recent studies, CCDC family genes have been found to be involved in regulation of growth, invasion, metastasis, chemosensitivity, and other biological behaviors of malignant tumor cells in various cancer types, including nasopharyngeal carcinoma, lung cancer, colorectal cancer, and thyroid cancer. In this review, we summarize the involvement of CCDC family genes in tumor pathogenesis and the relevant upstream and downstream molecular mechanisms. In addition, we summarize the potential of CCDC family genes as tumor therapy targets. The findings discussed here help us to further understand the role and the therapeutic applications of CCDC family genes in tumors.

Construction and validation of a novel algorithm based on oncosis-related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis

Colorectal cancer (CRC) has high morbidity and mortality, particularly if diagnosed at an advanced stage. Although there have been several studies on CRC, few have investigated the relationship between oncosis and CRC. Thus, the purpose of the present study was to identify oncosis-related long noncoding RNAs (lncRNAs) and to establish a clinical prognostic model. Original data were acquired from The Cancer Genome Atlas database and PubMed. Differentially expressed oncosis-related lncRNAs (DEorlncRNAs) were identified and were subsequently formed into pairs. Next, a series of tests and analyses, including both univariate and multivariate analyses, as well as Lasso and Cox regression analyses, were performed to establish a receiver operating characteristic curve. A cut-off point was subsequently used to divide the samples into groups labelled as high- or low-risk. Thus, a model was established and evaluated in several dimensions. Six pairs of DEorlncRNAs associated with prognosis according to the algorithm were screened out and the CRC cases were divided into high- and low-risk groups. Significant differences between patients in the different risk groups were observed for several traits, including survival outcomes, clinical pathology characteristics, immune cell infiltration status and drug sensitivity. In addition, PCR and flow cytometry were performed to further verify the model. In summary, a new risk model algorithm based on six pairs of DEorlncRNAs in CRC, which does not require specific data regarding the level of gene expression, was established and validated. This algorithm may be used to predict patient prognosis, immune cell infiltration and drug sensitivity.

Anticarcinogenic Effects of Isothiocyanates on Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for about 90% of cases. Sorafenib, lenvatinib, and the combination of atezolizumab and bevacizumab are considered first-line treatments for advanced HCC. However, clinical application of these drugs has also caused some adverse reactions such as hypertension, elevated aspartate aminotransferases, and proteinuria. At present, natural products and their derivatives have drawn more and more attention due to less side effects as cancer treatments. Isothiocyanates (ITCs) are one type of hydrolysis products from glucosinolates (GLSs), secondary plant metabolites found exclusively in cruciferous vegetables. Accumulating evidence from encouraging in vitro and in vivo animal models has demonstrated that ITCs have multiple biological activities, especially their potentially health-promoting activities (antibacterial, antioxidant, and anticarcinogenic effects). In this review, we aim to comprehensively summarize the chemopreventive, anticancer, and chemosensitizative effects of ITCs on HCC, and explain the underlying molecular mechanisms.

Characterizing the Crosstalk of NCAPG with Tumor Microenvironment and Tumor Stemness in Stomach Adenocarcinoma

Background. Nonstructural maintenance of non-SMC condensin I complex subunit G (NCAPG) exerts critical effects on cancer progression. However, its biological roles in tumorigenesis and metastasis remain unclear. Thus, we aimed to assess the prognostic utility of NCAPG in stomach adenocarcinoma (STAD) and its potential as a tumor biomarker. Methods. Pan-cancer expression profile dataset from public databases and corresponding clinical information were extracted. Single-sample gene set enrichment analysis (ssGSEA) was performed for the evaluation of immune correlations pan-cancer. Subsequently, we focused on STAD and evaluated the methylation profiles, copy number variants (CNVs), and single nucleotide variants (SNVs). Immune features were analyzed between high and low NCAPG expression groups. Differential analysis was performed between high and low expression groups to identify differentially expressed genes (DEGs). Prognostic DEGs were screened by univariate analysis, and an NCAPG-based risk model was constructed based on the prognostic DEGs and LASSO analysis. Results. NCAPG expression in STAD was significantly and positively correlated with four immune checkpoints, namely, CTLA4, PDCD1, LAG3, and CD276, but was negatively correlated with the infiltration of most immune cells. High and low NCAPG expression groups had differential overall survival, tumor mutation burden, and differential enrichment of therapeutic-related pathways. An immune risk scoring model related to NCAPG expression and immune score was constructed which showed a favorable performance in predicting STAD prognosis as well as predicting the response to immunotherapy. In addition, we found a higher mRNA stemness index (mRNAsi) in the high-risk group and a positive correlation between NCAPG expression and mRNAsi. Conclusion. NCAPG was suggested to be involved in the regulation of tumor microenvironment in STAD. High NCAPG expression was related to high tumor stemness and good prognosis. The immune risk model had a potential to predict STAD prognosis and help directing therapeutic treatment.

Association of FLG mutation with tumor mutation load and clinical outcomes in patients with gastric cancer

Background: Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancers in the world with a poor prognosis due to genetic heterogeneity. The present study aimed to explore potential prognostic predictors and therapeutic targets that can be used for STAD treatment.

Methods: We collected relevant data of STAD patients from the Cancer Genome Atlas (TCGA), including somatic mutation, transcriptome, and survival data. We performed a series of analyses such as tumor mutational burden (TMB), immune infiltration, and copy number variation (CNV) analysis to evaluate the potential mechanism of filaggrin (FLG) mutation in gastric cancer. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were performed for annotation of differentially expressed genes (DEGs). The STRING online database was used to construct the protein–protein interaction (PPI) and ceRNA network and hub genes were identified. Univariate and multivariate Cox regression analyses were used to determine the effect of selected DEGs on tumor prognosis.

Results: The FLG-mutant group (FLG-MT) showed a higher mutation load and immunogenicity in gastric cancer. GO and KEGG analyses identified and ranked unique biologic processes and immune-related pathway maps that correlated with the FLG-mutant target. GSEA analysis showed that several tumorigenesis and metastasis-related pathways were indeed enriched in FLG-mutant tumor tissue. Both cell cycle–related pathways and the DNA damage and repair associated pathways were also enriched in the FLG-MT group. The FLG mutations resulted in increased gastric cancer sensitivity to 24 chemotherapeutic drugs. The ceRNA network was established using Cytoscape and the PPI network was established in the STRING database. The results of the prognostic information further demonstrated that the OS and DFS were significantly higher in FLG mutation carriers, and the FLG gene mutation might be a protective factor.

Conclusion: The multiple molecular mechanisms of the FLG gene in STAD are worthy of further investigation and may reveal novel therapeutic targets and biomarkers for STAD treatment.

Identification of a 3-Gene Model as Prognostic Biomarker in Patients With Gastric Cancer

Objective

Although the incidence of gastric cancer (GC) is decreasing, GC remains one of the leading cancers in the world. Surgical resection, radiotherapy, chemotherapy, and neoadjuvant therapy have advanced, but patients still face the risk of recurrence and poor prognosis. This study provides new insights for assessment of prognosis and postoperative recurrence of GC patients.

Methods

We collected paired cancer and adjacent tissues of 17 patients with early primary GC for bulk transcriptome sequencing. By comparing the transcriptome information of cancer and adjacent cancer, 321 differentially expressed genes (DEGs) were identified. These DEGs were further screened and analyzed with the GC cohort of TCGA to establish a 3-gene prognostic model (PLCL1, PLOD2 and ABCA6). At the same time, the predictive ability of this risk model is validated in multiple public data sets. Besides, the differences in immune cells proportion between the high- and low-risk groups were analyzed by the CIBERSORT algorithm with the Leukocyte signature matrix (LM22) gene signature to reveal the role of the immune microenvironment in the occurrence and development of GC.

Results

The model could divide GC samples from TCGA cohorts into two groups with significant differences in overall and disease-free survival. The excellent predictive ability of this model was also validated in multiple other public data sets. The proportion of these immune cells such as resting mast cells, T cells CD4+ memory activated and Macrophages M2 are significantly different between high and low risk group.

Conclusion

These three genes used to build the models were validated as biomarkers for predicting tumor recurrence and survival. They may have potential significance for the treatment and diagnosis of patients in the future, and may also promote the development of targeted drugs.

Identification of Prognostic and Tumor Microenvironment by Shelterin Complex-Related Signatures in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is a common malignant tumor of the oral cavity. Shelterin complex gene (SG) has an important role in regulating telomere structure and length. SG is considered promising as a novel prognostic marker for cancer and a potential target for tumor therapy. However, SGs have not been systematically studied in OSCC. We analyzed SGs based on public data from OSCC patients and showed that SGs are closely associated with the prognosis of OSCC patients. Two different subtypes of SGs were identified in the TCGA and GEO cohorts, and LASSO regression analysis was used to further construct an SGs-related prognostic model. Randomized cohorts and different clinical subgroups validated the model's accuracy. The assessment of clinical characteristics, tumor mutational burden (TMB), and tumor microenvironment (TME) between high- and low-risk scores groups showed lower TMB, more abundant immune cell infiltration, and better prognosis in the low-risk group. According to the IPS analysis, patients in the low-risk group were more responsive to immunotherapy. This study establishes a foundation for research on SG and confirms that risk scores can predict prognosis and guide clinical treatment in OSCC patients.

Identification of Prognostic Biomarkers of Glioblastoma Based on Multidatabase Integration and Its Correlation with Immune-Infiltration Cells

Background

Glioblastoma (GBM) is the most malignant of all known intracranial tumors; meanwhile, most patients have a poor prognosis. In order to improve the poor prognosis of GBM patients as much as possible, it is specifically significant to identify biomarkers related to the gene diagnosis and gene therapy.

Methods

In this study, a total of 343 GBM specimens and 259 nontumor specimens were collected from four Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database; then, we analyzed the differentially expressed genes (DEGs) from the above data. Through Venn diagram analysis, 54 common upregulated DEGs and 22 common downregulated DEGs were triumphantly recognized.

Results

On the basis of the degree of formation communication in protein-protein interaction network (PPIN), the 10 upregulated central genes were ranked, incorporating LOX, IGFBP3, CD44, TIMP1, FN1, VEGFA, POSTN, COL1A1, COL1A2, and COL3A1. By combining the expression levels and the clinical features of GBM, we found that four hub genes (TIMP1, FN1, POSTN, and LOX) were significantly upregulated and related to poor prognosis of GBM. Meanwhile, univariate and multivariate Cox regression analysis suggested that TIMP1 could be one of the independent prognostic factors for GBM patients. Furthermore, TIMP1 was particularly correlated with the immune marker of macrophage M1, macrophage M2, neutrophils, tumor-associated macrophage, and Tregs. We then analyzed the role of TIMP1 in GBM cancer cell lines by relevant experiments, which indicated that TIMP1 knockdown resulted in the decreased cell proliferation, migration, and invasion.

Conclusions

Taken together, these findings demonstrated that TIMP1 might be a new biomarker to determine prognosis and immune infiltration of GBM patients.

Identification of three immune subtypes characterized by distinct tumor immune microenvironment and therapeutic response in stomach adenocarcinoma

BACKGROUND

In primary stomach adenocarcinoma (STAD), the tumor immune microenvironment (TIME) is important for cancer occurrence and progression; however, its clinical significance remains unclear. This study investigated the association between patient survival, TIME, and therapeutic response to STAD.

METHODS

Gene expression profiles of STAD cases were collected from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus. Molecular subtypes were explored with consistent clustering methods according to 119 immune signatures and the infiltrating scores of 22 immune cells using the Multi-Omics Immuno-Oncology Biological Research algorithm. We determined IFNγ scores and immune cytolytic activity (CYT) scores on the basis of corresponding gene signatures via single-sample Gene Set Enrichment Analysis. Comparisons of survival, TIME, 10 immunity-related oncogenic pathways, immune checkpoint expression, and therapeutic response were conducted among the three subtypes. We further applied linear discriminant analysis to construct a characteristic index to classify the subtypes, and the Pearson correlation coefficient for the relationship between the index and immune checkpoint genes. Weighted Correlation Network Analysis (WGCNA) was used to mine the associated modules and specific genes.

RESULTS

We collected gene expression profiles from 352 STAD cases in the TCGA database, 300 in GSE62254, and 344 in GSE84437. Three STAD subtypes (IS1-IS3) were established according to the TIME signatures. The IS3 subtype had the highest immune score and the best prognosis, as well as markedly increased immune T-cell CYT, Th1/IFNγ scores, and immune checkpoint gene expression, compared to the other two subtypes. It was highly similar to the PD-1 response group in the previous study samples of GSE91061. The established TIME classification index performed well in classifying subtypes and was directly proportional to immune checkpoint-related gene expression levels. WGCNA explored 6 modules and 14 genes, namely DYSF, MAN1C1, HTRA3, EMCN, RFLNB, KANK3, MAGEH1, CD93, PCAT19, FUT11, BMP1, FOSB, DCHS1, and TCF3, which were associated with the established TIME classification index and STAD patient prognosis.

CONCLUSION

TIME phenotypes of STAD patients could be divided into three different molecular subtypes, which displayed different prognoses, immune features, and therapeutic responses. Our results shed new light on predicting patient outcomes and the discovery of new anti-STAD therapeutic strategies according to the TIME.

Identification of Prognosis Biomarkers for High-Grade Serous Ovarian Cancer Based on Stemness

In this paper, high-grade serous ovarian cancer (HGSOC) is studied, which is the most common histological subtype of ovarian cancer. We use a new analytical procedure to combine the bulk RNA-Seq sample for ovarian cancer, mRNA expression-based stemness index (mRNAsi), and single-cell data for ovarian cancer. Through integrating bulk RNA-Seq sample of cancer samples from TCGA, UCSC Xena and single-cell RNA-Seq (scRNA-Seq) data of HGSOC from GEO, and performing a series of computational analyses on them, we identify stemness markers and survival-related markers, explore stem cell populations in ovarian cancer, and provide potential treatment recommendation. As a result, 171 key genes for capturing stem cell characteristics are screened and one vital cancer stem cell subpopulation is identified. Through further analysis of these key genes and cancer stem cell subpopulation, more critical genes can be obtained as LCP2, FCGR3A, COL1A1, COL1A2, MT-CYB, CCT5, and PAPPA, are closely associated with ovarian cancer. So these genes have the potential to be used as prognostic biomarkers for ovarian cancer.

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

Background

Prostate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study.

Methods

High-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa.

Results

A total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa.

Conclusion

This new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.

Per- and polyfluoroalkyl substances, epigenetic age and DNA methylation: a cross-sectional study of firefighters

Background: Per- and polyfluoroalkyl substances (PFASs) are persistent chemicals that firefighters encounter. Epigenetic modifications, including DNA methylation, could serve as PFASs toxicity biomarkers. Methods: With a sample size of 197 firefighters, we quantified the serum concentrations of nine PFASs, blood leukocyte DNA methylation and epigenetic age indicators via the EPIC array. We examined the associations between PFASs with epigenetic age, site- and region-specific DNA methylation, adjusting for confounders. Results: Perfluorohexane sulfonate, perfluorooctanoate (PFOA) and the sum of branched isomers of perfluorooctane sulfonate (Sm-PFOS) were associated with accelerated epigenetic age. Branched PFOA, linear PFOS, perfluorononanoate, perfluorodecanoate and perfluoroundecanoate were associated with differentially methylated loci and regions. Conclusion: PFASs concentrations are associated with accelerated epigenetic age and locus-specific DNA methylation. The implications for PFASs toxicity merit further investigation.

Integrated Analysis of the Expression Characteristics, Prognostic Value, and Immune Characteristics of PPARG in Breast Cancer

Background: Breast cancer (BRCA) is the most frequent malignancy. Identification of potential biomarkers could help to better understand and combat the disease at early stages.

Methods: We selected the overlapping genes of differential expressed genes and genes in BRCA-highly correlated modules by Weighted Gene Co-Expression Network Analysis (WGCNA) in TCGA and GEO data and performed KEGG and GO enrichment. PPARG was achieved from Protein-Protein Interaction (PPI) network analysis and prognostic analysis. TIMER, UALCAN, GEO, TCGA, and western blot analysis were used to validate the expression of PPARG in BRCA. PPARG was further analyzed by DNA methylation, immune parameters, and tumor mutation burden.

Results: Among 381 overlapping genes, the lipid metabolic process was identified as highly enriched pathways in BRCA by TCGA and GEO data. When the prognostic analysis of 10 core genes by PPI network was performed, results revealed that high expression of PPARG was significantly correlated to a better prognosis. PPARG was lesser expression in BRCA according to TIMER, UALCAN, GEO, TCGA, and western blot in both mRNA level and protein level. PPARG had several high DNA methylation level sites and the methylation level is negatively correlated to expression. PPARG is also correlated to TNM stages, tumor microenvironment, and tumor burden.

Conclusions: Findings of our study identified the PPARG as a potential biomarker by confirming its low expression in BRCA and its correlation to prognosis. Moreover, its correlation to DNA methylation and tumor microenvironment may guide new therapeutic strategies for BRCA patients.

Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer

Colorectal cancer (CRC) is one of the most common and lethal malignant lesions. Determining how the identified risk factors drive the formation and development of CRC could be an essential means for effective therapeutic development. Aiming this, we investigated how the altered gene expression resulting from exposure to putative CRC risk factors contribute to prognostic biomarker identification. Differentially expressed genes (DEGs) were first identified for CRC and other eight risk factors. Gene set enrichment analysis (GSEA) through the molecular pathway and gene ontology (GO), as well as protein-protein interaction (PPI) network, were then conducted to predict the functions of these DEGs. Our identified genes were explored through the dbGaP and OMIM databases to compare with the already identified and known prognostic CRC biomarkers. The survival time of CRC patients was also examined using a Cox Proportional Hazard regression-based prognostic model by integrating transcriptome data from The Cancer Genome Atlas (TCGA). In this study, PPI analysis identified 4 sub-networks and 8 hub genes that may be potential therapeutic targets, including CXCL8, ICAM1, SOD2, CXCL2, CCL20, OIP5, BUB1, ASPM and IL1RN. We also identified seven signature genes (PRR5.ARHGAP8, CA7, NEDD4L, GFR2, ARHGAP8, SMTN, OIP5) in independent analysis and among which PRR5. ARHGAP8 was found in both multivariate analyses and in analyses that combined gene expression and clinical information. This approach provides both mechanistic information and, when combined with predictive clinical information, good evidence that the identified genes are significant biomarkers of processes involved in CRC progression and survival.