Systematic Pan-Cancer Analysis of KLRB1 with Prognostic Value and Immunological Activity across Human Tumors

Introduction. KLRB1 is a gene encoding CD161 expressed in NK cells and some T cell subsets. At present, KLRB1 is believed to affect tumorigenesis and development by regulating the cytotoxicity of NK cells in several cancers. However, there is a lack of systematic reviews of KLRB1 in a variety of malignancies. Objectives. Hence, our research is aimed at providing a relatively comprehensive understanding of the role of KLRB1 in different types of cancer, paving the way for further research on the molecular mechanism and immunotherapy potential of KLRB1. Methods. In this study, we used relevant public databases, including TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus), CCLE (Cancer Cell Line Encyclopedia), GTEx (Genotype Tissue-Expression), and HPA (Human Protein Atlas), to perform a pan-cancer analysis of KLRB1 across 33 types of cancer. We explored the potential molecular mechanism of KLRB1 in clinical prognosis and tumor immunity from the aspects of gene expression, survival status, clinical phenotype, immune infiltration, immunotherapy response, and chemotherapeutic drug sensitivity. Results. KLRB1 was downregulated in 13 cancers while upregulated in kidney cancer. Patients with high expression of KLRB1 have a better prognosis in most types of cancer. Moreover, the KLRB1 expression level is related to TMB and MSI and related to various immune signatures of tumor. The expression of KLRB1 can affect tumor immune cell infiltration. KLRB1 expression level can also affect the sensitivity of chemotherapy drugs. Conclusions. KLRB1 may be a prognostic and immunological biomarker across tumors. At the same time, KLRB1 expression can reflect the sensitivity of cancer patients to chemotherapy drugs. KLRB1 may become a new target for immunotherapy.

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Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

BMC Medical Genomics ◽

10.1186/s12920-021-00923-0 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Guoliang Jia ◽

Zheyu Song ◽

Zhonghang Xu ◽

Youmao Tao ◽

Yuanyu Wu ◽

...

Keyword(s):

Gene Expression ◽

Cutaneous Melanoma ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Validation Dataset ◽

Survival Prognosis ◽

Gene Markers ◽

Clinical Prognosis ◽

Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

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Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

Cancer Informatics ◽

10.1177/11769351211002494 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110024

Author(s):

Jason D Wells ◽

Jacqueline R Griffin ◽

Todd W Miller

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Human Tumors ◽

Molecular Characteristics ◽

Survival Times ◽

Chemotherapy Drugs ◽

Testing Dataset ◽

Cancer Models ◽

Cancer Types ◽

Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

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The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms22041820 ◽

2021 ◽

Vol 22 (4) ◽

pp. 1820

Author(s):

Anna Makuch-Kocka ◽

Janusz Kocki ◽

Anna Brzozowska ◽

Jacek Bogucki ◽

Przemysław Kołodziej ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Clinical Data ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Lymphatic Vessels ◽

The Cancer Genome Atlas ◽

Expression Level ◽

Cancer Tissue ◽

Expression Levels

The BIRC (baculoviral IAP repeat-containing; BIRC) family genes encode for Inhibitor of Apoptosis (IAP) proteins. The dysregulation of the expression levels of the genes in question in cancer tissue as compared to normal tissue suggests that the apoptosis process in cancer cells was disturbed, which may be associated with the development and chemoresistance of triple negative breast cancer (TNBC). In our study, we determined the expression level of eight genes from the BIRC family using the Real-Time PCR method in patients with TNBC and compared the obtained results with clinical data. Additionally, using bioinformatics tools (Ualcan and The Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner v4.5)), we compared our data with the data in the Cancer Genome Atlas (TCGA) database. We observed diverse expression pattern among the studied genes in breast cancer tissue. Comparing the expression level of the studied genes with the clinical data, we found that in patients diagnosed with breast cancer under the age of 50, the expression levels of all studied genes were higher compared to patients diagnosed after the age of 50. We observed that in patients with invasion of neoplastic cells into lymphatic vessels and fat tissue, the expression levels of BIRC family genes were lower compared to patients in whom these features were not noted. Statistically significant differences in gene expression were also noted in patients classified into three groups depending on the basis of the Scarff-Bloom and Richardson (SBR) Grading System.

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Upregulated Expression of CYBRD1 Predicts Poor Prognosis of Patients with Ovarian Cancer

Journal of Oncology ◽

10.1155/2021/7548406 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Rui Chen ◽

Jianhong Cao ◽

Wei Jiang ◽

Shunli Wang ◽

Jingxin Cheng

Keyword(s):

Nk Cells ◽

Signaling Pathway ◽

Poor Prognosis ◽

Cox Proportional Hazards ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Erk Signaling ◽

Th2 Cells ◽

Primary Therapy ◽

Clinical Prognosis

Cytochrome b reductase 1 (CYBRD1) promotes the development of ovarian serous cystadenocarcinoma (OV). We assessed the function of CYBRD1 in OV underlying The Cancer Genome Atlas (TCGA) database. The correlation between clinicopathological characteristics and CYBRD1 expression was estimated. The Cox proportional hazards regression model and the Kaplan–Meier method were applied to identify clinical features related to overall survival and disease-specific survival. Gene set enrichment analysis (GSEA) was applied to identify the relationship between CYBRD1 expression and immune infiltration. CYBRD1 expression in OV was significantly associated with poor outcomes of primary therapy and FIGO stage. Patients with high levels of CYBRD1 expression were prone to the development of a poorly differentiated tumor and experience of an unfavorable outcome. CYBRD1 expression had significant association with shorter OS and acts as an independent predictor of poor outcome. Moreover, enhanced CYBRD1 expression was positively associated with Tem, NK cells, and mast cells but negatively associated with CD56 bright NK cells and Th2 cells. CYBRD1 expression may serve as a diagnostic and prognostic indicator of OV patients. The mechanisms of poor prognosis of CYBRD1-mediated OV may include increased iron uptake, regulation of immune microenvironment, ferroptosis related pathway, and ERK signaling pathway, among which ferroptosis and ERK signaling pathway may be important pathways of CYBRD1-mediated OV. Furthermore, we verified that CYBRD1 was upregulated in OV and significant correlated with lymph nodes metastasis, advanced stage, poor-differentiated tumor, and poor clinical prognosis in East Hospital cohort. The results of this study may provide guidance for the development of optimal treatment strategies for OV.

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Identification of an extracellular vesicle-related gene signature in the prediction of pancreatic cancer clinical prognosis

Bioscience Reports ◽

10.1042/bsr20201087 ◽

2020 ◽

Vol 40 (12) ◽

Author(s):

Dafeng Xu ◽

Yu Wang ◽

Kailun Zhou ◽

Jincai Wu ◽

Zhensheng Zhang ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Immune Cells ◽

Prognostic Value ◽

Tumor Tissue ◽

Clinical Stage ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Clinical Prognosis

Abstract Although extracellular vesicles (EVs) in body fluid have been considered to be ideal biomarkers for cancer diagnosis and prognosis, it is still difficult to distinguish EVs derived from tumor tissue and normal tissue. Therefore, the prognostic value of tumor-specific EVs was evaluated through related molecules in pancreatic tumor tissue. NA sequencing data of pancreatic adenocarcinoma (PAAD) were acquired from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). EV-related genes in pancreatic cancer were obtained from exoRBase. Protein–protein interaction (PPI) network analysis was used to identify modules related to clinical stage. CIBERSORT was used to assess the abundance of immune and non-immune cells in the tumor microenvironment. A total of 12 PPI modules were identified, and the 3-PPI-MOD was identified based on the randomForest package. The genes of this model are involved in DNA damage and repair and cell membrane-related pathways. The independent external verification cohorts showed that the 3-PPI-MOD can significantly classify patient prognosis. Moreover, compared with the model constructed by pure gene expression, the 3-PPI-MOD showed better prognostic value. The expression of genes in the 3-PPI-MOD had a significant positive correlation with immune cells. Genes related to the hypoxia pathway were significantly enriched in the high-risk tumors predicted by the 3-PPI-MOD. External databases were used to verify the gene expression in the 3-PPI-MOD. The 3-PPI-MOD had satisfactory predictive performance and could be used as a prognostic predictive biomarker for pancreatic cancer.

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Integrative Analysis Reveals Comprehensive Altered Metabolic Genes Linking with Tumor Epigenetics Modification in Pan-Cancer

BioMed Research International ◽

10.1155/2019/6706354 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Yahui Shi ◽

Jinfen Wei ◽

Zixi Chen ◽

Yuchen Yuan ◽

Xingsong Li ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Histone Acetylation ◽

Epigenetic Modification ◽

Metabolic Reprogramming ◽

The Cancer Genome Atlas ◽

Altered Expression ◽

Metabolic Genes ◽

Cancer Types ◽

Pan Cancer

Background. Cancer cells undergo various rewiring of metabolism and dysfunction of epigenetic modification to support their biosynthetic needs. Although the major features of metabolic reprogramming have been elucidated, the global metabolic genes linking epigenetics were overlooked in pan-cancer. Objectives. Identifying the critical metabolic signatures with differential expressions which contributes to the epigenetic alternations across cancer types is an urgent issue for providing the potential targets for cancer therapy. Method. The differential gene expression and DNA methylation were analyzed by using the 5726 samples data from the Cancer Genome Atlas (TCGA). Results. Firstly, we analyzed the differential expression of metabolic genes and found that cancer underwent overall metabolism reprogramming, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Secondly, the regulatory network of histone acetylation and DNA methylation according to altered expression of metabolism genes was summarized in our results. Then, the survival analysis showed that high expression of DNMT3B had a poorer overall survival in 5 cancer types. Integrative altered methylation and expression revealed specific genes influenced by DNMT3B through DNA methylation across cancers. These genes do not overlap across various cancer types and are involved in different function annotations depending on the tissues, which indicated DNMT3B might influence DNA methylation in tissue specificity. Conclusions. Our research clarifies some key metabolic genes, ACLY, SLC2A1, KAT2A, and DNMT3B, which are most disordered and indirectly contribute to the dysfunction of histone acetylation and DNA methylation in cancer. We also found some potential genes in different cancer types influenced by DNMT3B. Our study highlights possible epigenetic disorders resulting from the deregulation of metabolic genes in pan-cancer and provides potential therapy in the clinical treatment of human cancer.

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Development and Validation of a Novel 13-Genes Prognostic Model for Colon Cancer

10.21203/rs.3.rs-378434/v1 ◽

2021 ◽

Author(s):

Duo Yun ◽

Zhirong Yang

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Overall Survival ◽

Cancer Patients ◽

Prognostic Value ◽

Risk Model ◽

Malignant Tumors ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Pathway Enrichment

Abstract Colon cancer is one of the most common malignant tumors in the world. The purpose of this study is to explore the prognostic value of genes in colon cancer. After analyzing gene expression profiles, differential expressed genes between 39 normal tissues and 398 tumor tissues were identified from The Cancer Genome Atlas database. We use Cox and lasso regression to find genes related to prognosis. Through analysis, 13 genes were found to predict the overall survival of colon cancer patients. In addition, the external comparing of gene expression and the single prognostic gene survival analysis were made. Finally, pathway enrichment and mutation status of each gene were also analyzed. After a series of bioinformatics analysis, we select 13 survival-related signature and established a prognostic risk model based on these genes. The prognostic risk model was developed to comprehensively predict the overall survival of colon cancer patients. The prognostic value of the 13-genes (CLDN23,HAND1,IL23A,KLHL35,SIX2,UPK2,HOXC11,KRT6B,SRCIN1,TNNI3,TYRO3,MIR6835,LINC02474) related risk score for each colon cancer patent was calculated to predict the survival. Furthermore, five genes (SIX2 MIR6835 LINC02474 CLDN23 HOXC11) were significantly associated with overall survival (OS). The KEGG pathway enrichment results suggested that most of the pathways are related to the occurrence, metabolism, proliferation and invasion of the tumor cells. It was found that the expression of 13-genes signature can be used as prognostic indicator for colon cancer patients. The 13-genes signature predictive model may help clinicians provide a prognosis and personalized treatment for colon cancer patients.

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Characteristics of Subtypes Within Microsatellite Instability-high Gastric Cancer Based on a Gene Signature Related to Immune Microenvironment Components

10.21203/rs.3.rs-31506/v1 ◽

2020 ◽

Author(s):

Xiaolong Wu ◽

Xiangyu Gao ◽

Xiaofang Xing ◽

Xianzi Wen ◽

Ziyu Li ◽

...

Keyword(s):

Gastric Cancer ◽

Microsatellite Instability ◽

Cancer Patients ◽

Immune Checkpoint ◽

Differentially Expressed Gene ◽

The Cancer Genome Atlas ◽

Immune Microenvironment ◽

Clinical Prognosis ◽

Time Signature ◽

Gastric Cancer Patients

Abstract Background: Gastric cancer patients with microsatellite instability-high (MSI-H) status have a better clinical prognosis and higher response rate to immune checkpoint inhibitors. However, recent studies have suggested that some molecular pathways in MSI-H tumors could affect tumor immune microenvironment (TIME) components, thereby leading to immunotherapy resistance. We aimed to establish subtypes based on the TIME components of MSI-H gastric cancer and analyze the characteristics of each subtype. Methods: Cohorts from the Cancer Genome Atlas, the Asian Cancer Research Group, and Peking University Cancer Hospital were used for this study. CIBERSORT software was used to analyze the TIME components. A set of genes based on the TIME component characteristics, which we named the MSI-TIME signature, was defined using k-means cluster and differentially expressed gene analysis. Results: By using the MSI-TIME signature in the aforementioned cohorts for cluster analysis, the TIME subtypes within MSI-H gastric cancer (MSI-S1, MSI-S2) were established; the differences between the subgroups were reflected in multiple aspects. The MSI-S1 subtype was characterized by a high density of CD8+ T cells, high expression levels of immune checkpoint molecules including PD-L1, PD-L2, CTLA-4, and a high T-cell inflammation level. Patients with the MSI-S1 subtype could also benefit from adjuvant chemotherapy. In contrast, the WNT/β-catenin pathway was enriched in the MSI-S2 subtype. Conclusion: We found that patients with MSI-H gastric cancer showed very different TIME characteristics and could be divided into two subtypes accordingly. These results might benefit MSI-H gastric cancer patients developing individualized treatment strategies in the future.

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Tumor-Experienced Human NK Cells Express High Levels of PD-L1 and Inhibit CD8+ T Cell Proliferation

Frontiers in Immunology ◽

10.3389/fimmu.2021.745939 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jessica M. Sierra ◽

Florencia Secchiari ◽

Sol Y. Nuñez ◽

Ximena L. Raffo Iraolagoitia ◽

Andrea Ziblat ◽

...

Keyword(s):

Cell Proliferation ◽

T Cell ◽

Nk Cells ◽

Cancer Patients ◽

Nk Cell ◽

The Cancer Genome Atlas ◽

T Cell Proliferation ◽

Dependent Manner ◽

Effector Functions

Natural Killer (NK) cells play a key role in cancer immunosurveillance. However, NK cells from cancer patients display an altered phenotype and impaired effector functions. In addition, evidence of a regulatory role for NK cells is emerging in diverse models of viral infection, transplantation, and autoimmunity. Here, we analyzed clear cell renal cell carcinoma (ccRCC) datasets from The Cancer Genome Atlas (TCGA) and observed that a higher expression of NK cell signature genes is associated with reduced survival. Analysis of fresh tumor samples from ccRCC patients unraveled the presence of a high frequency of tumor-infiltrating PD-L1+ NK cells, suggesting that these NK cells might exhibit immunoregulatory functions. In vitro, PD-L1 expression was induced on NK cells from healthy donors (HD) upon direct tumor cell recognition through NKG2D and was further up-regulated by monocyte-derived IL-18. Moreover, in vitro generated PD-L1hi NK cells displayed an activated phenotype and enhanced effector functions compared to PD-L1- NK cells, but simultaneously, they directly inhibited CD8+ T cell proliferation in a PD-L1-dependent manner. Our results suggest that tumors might drive the development of PD-L1-expressing NK cells that acquire immunoregulatory functions in humans. Hence, rational manipulation of these regulatory cells emerges as a possibility that may lead to improved anti-tumor immunity in cancer patients.

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Identification of Distinct Molecular Patterns and a Four-Gene Signature in Colon Cancer Based on Invasion-Related Genes

Frontiers in Genetics ◽

10.3389/fgene.2021.685371 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yunfei Dong ◽

Tao Shang ◽

HaiXin Ji ◽

Xiukou Zhou ◽

Zhi Chen

Keyword(s):

Gene Expression ◽

Colon Cancer ◽

Cancer Patients ◽

Risk Group ◽

Univariate Analysis ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Lasso Regression ◽

Independent Predictive Factor

BackgroundThe pathological stage of colon cancer cannot accurately predict recurrence, and to date, no gene expression characteristics have been demonstrated to be reliable for prognostic stratification in clinical practice, perhaps because colon cancer is a heterogeneous disease. The purpose was to establish a comprehensive molecular classification and prognostic marker for colon cancer based on invasion-related expression profiling.MethodsFrom the Gene Expression Omnibus (GEO) database, we collected two microarray datasets of colon cancer samples, and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) further underwent univariate analysis, least absolute shrinkage, selection operator (LASSO) regression analysis, and multivariate Cox survival analysis to screen prognosis-associated feature genes, which were further verified with test datasets.ResultsTwo molecular subtypes (C1 and C2) were identified based on invasion-related genes in the colon cancer samples in TCGA training dataset, and C2 had a good prognosis. Moreover, C1 was more sensitive to immunotherapy. A total of 1,514 invasion-related genes, specifically 124 downregulated genes and 1,390 upregulated genes in C1 and C2, were identified as DEGs. A four-gene prognostic signature was identified and validated, and colon cancer patients were stratified into a high-risk group and a low-risk group. Multivariate regression analyses and a nomogram indicated that the four-gene signature developed in this study was an independent predictive factor and had a relatively good predictive capability when adjusting for other clinical factors.ConclusionThis research provided novel insights into the mechanisms underlying invasion and offered a novel biomarker of a poor prognosis in colon cancer patients.

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