Gene Expression Profiling Of Cancer Research Articles

T cell receptor clonotype in tumor microenvironment contributes to intratumoral signaling network in patients with colorectal cancer.

Single-cell RNA sequencing (scRNA-seq) has contributed to understanding cellular heterogeneity and immune profiling in cancer. The aim of the study was to investigate gene expression and immune profiling in colorectal cancer (CRC) using scRNA-seq. We analyzed single-cell gene expression and T cell receptor (TCR) sequences in 30 pairs of CRC and matched normal tissue. Intratumoral lymphocytes were measured with digital image analysis. CRC had more T cells, epithelial cells, and myeloid cells than normal colorectal tissue. CRCs with microsatellite instability had more abundant T cells than those without microsatellite instability. Immune cell compositions of CRC and normal colorectal tissue were inversely correlated. CD4 + or CD8 + proliferating T cells, CD4 + effector memory T cells, CD8 + naïve T cells, and regulatory T cells of CRC showed higher TCR clonal expansion. Tumor epithelial cells interacted with immune cells more strongly than normal. T cells, myeloid cells, and fibroblasts from CRCs of expanded T cell clonotypes showed increased expression of genes related to TNF and NFKB signaling and T cell activation. CRCs of expanded T cell clonotypes also showed stronger cellular interactions among immune cells, fibroblasts, and endothelial cells. Pro-inflammatory CXCL and TNF signaling were activated in CRCs of expanded T cell clonotype. In conclusion, scRNA-seq analysis revealed different immune cell compositions, differential gene expression, and diverse TCR clonotype dynamics in CRC. TCR clonality expansion is associated with immune activation through T cell signaling and chemokine signaling. Patients with CRCs of expanded clonotype can be promising candidates for immunotherapy.

Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA)

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

The Progression and Prospects of the Gene Expression Profiling in Ovarian Epithelial Cancer

Abstract Ovarian cancer is one of the most common cancers with a high mortality rate among females worldwide. The understanding of the pathogenesis of the disease is highly important to provide personalized therapy to the patients. Ovarian cancer is as heterogeneous as colon and breast cancer which makes it difficult to treat. The development of gene signature is the only hope in providing targeted therapy to improve the survival of ovarian cancer patients. Malignant epithelial carcinomas are the most common cancers of the ovary with different histological and molecular subtypes and clinical behavior. The development of precursor lesions of ovarian carcinoma in the tubes and endometrium has provided a new dimension to the origin of ovarian cancers. The clinical utility of various gene signatures may not be logical unless validated. Validated gene signatures can aid the clinician in deciding the appropriate line of treatment.

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma

BackgroundZinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown.MethodsThrough the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration.ResultsIt was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments.ConclusionsAnoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

Loss of tumor-derived SMAD4 enhances primary tumor growth but not metastasis following BMP4 signalling

BackgroundBone morphogenetic protein 4 (BMP4) is a potent inhibitor of breast cancer metastasis. However, a tumor-promoting effect of BMP4 is reported in other tumor types, especially when SMAD4 is inactive.MethodsTo assess the requirement for SMAD4 in BMP4-mediated suppression of metastasis, we knocked down SMAD4 in two different breast tumors and enforced SMAD4 expression in a third line with endogenous SMAD4 deletion. In addition, we assessed the requirement for SMAD4 in tumor cell-specific BMP signalling by expression of a constitutively active BMP receptor. Delineation of genes regulated by BMP4 in the presence or absence of SMAD4 was assessed by RNA sequencing and a BMP4-induced gene, MYO1F was assessed for its role in metastasis. Genes regulated by BMP4 and/or SMAD4 were assessed in a publicly available database of gene expression profiles of breast cancer patients.ResultsIn the absence of SMAD4, BMP4 promotes primary tumor growth that is accompanied by increased expression of genes associated with DNA replication, cell cycle, and MYC signalling pathways. Despite increased primary tumor growth, BMP4 suppresses metastasis in the absence of tumor cell expression of SMAD4. Consistent with the anti-metastatic activity of BMP4, enforced signalling through the constitutively active receptor in SMAD4 positive tumors that lacked BMP4 expression still suppressed metastasis, but in the absence of SMAD4, the suppression of metastasis was largely prevented. Thus BMP4 is required for suppression of metastasis regardless of tumor SMAD4 status. The BMP4 upregulated gene, MYO1F, was shown to be a potent suppressor of breast cancer metastasis. Gene signature upregulated by BMP4 in the absence of SMAD4 was associated with poor prognosis in breast cancer patients, whereas gene signature upregulated by BMP4 in the presence of SMAD4 was associated with improved prognosis.ConclusionsBMP4 expression is required for suppression of metastasis regardless of the SMAD4 status of the tumor cells. Since BMP4 is a secreted protein, we conclude that it can act both in an autocrine manner in SMAD4-expressing tumor cells and in a paracrine manner on stromal cells to suppress metastasis. Deletion of SMAD4 from tumor cells does not prevent BMP4 from suppressing metastasis via a paracrine mechanism.

Cross-platform gene expression profiling of breast cancer: Exploring the relationship between breast cancer grades and gene expression pattern

Gene expression profiling is a powerful tool that has been extensively used to investigate the underlying biology and etiology of diseases, including cancer. Microarray gene expression analysis enables simultaneous measurement of thousands of mRNA levels. Sophisticated computational approaches have evolved in parallel with the rapid progress in bioassay technologies, enabling more effective analysis of the large and complex datasets that these technologies produce.In this study, we utilized systems biology approaches to examine gene expression profiles across different grades of breast cancer progression. We conducted a meta-analysis of publicly available microarray data to elucidate the molecular mechanisms underlying breast cancer grade classification.Our results suggest that while grade index is commonly used for evaluating cancer progression status in the clinic, the complexity of molecular mechanisms, histological characteristics, and other factors related to patient outcomes raises doubts about the utility of breast cancer grades as a foundation for formulating treatment protocols.Our study underscores the importance of advancing personalized strategies for breast cancer classification and management. More research is crucial to refine diagnostic tools and treatment modalities, aiming for greater precision and tailored care in patient outcomes.

CTBP2 contributes to cisplatin resistance in lung adenocarcinoma by inhibiting generation of reactive oxygen species.

Cisplatin (CP) is commonly used for the initial treatment of lung adenocarcinoma (LUAD). Resistance to CP has long been recognized as a significant obstacle to achieving improved therapeutic outcomes. Nevertheless, the intricate molecular mechanisms underlying the phenomenon remain incompletely understood. The present study utilized the University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) and Gene Expression Profiling Interactive Analysis (GEPIA) databases to conduct an analysis of the expression of C-terminal binding protein 2 (CTBP2) in LUAD. The correlation between CTBP2 expression and survival data was investigated by the Kaplan-Meier (K-M) plotter. Subsequently, the roles of CTBP2 in CP resistance were explored by analyzing cell viability, cell apoptosis, reactive oxygen species (ROS), and mitochondrial membrane potential (MMP) in CP-resistant cells (A549/DDP). Our data indicated that the CTBP2 expression in LUAD exhibited a significant increase compared to the non-malignant tissues. CTBP2 overexpression showed a correlation to poor survival. CTBP2 knockdown significantly enhanced cell sensitivity to CP in A549/DDP cells. The underlying mechanism is related to promoting ROS production and decreasing MMP after CP treatment. CTBP2 expression has been identified as a novel biomarker for resistance to CP, and its downregulation has been found to enhance sensitivity to CP. Therefore, CTBP2 can serve as a predictor related to CP resistance and a viable therapeutic target for CP resistance in LUAD.

Hexavalent chromium exposure activates the non-canonical nuclear factor kappa B pathway to promote immune checkpoint protein programmed death-ligand 1 expression and lung carcinogenesis

Lung cancer is the leading cause of cancer-related death worldwide; however, the mechanism of lung carcinogenesis has not been clearly defined. Chronic exposure to hexavalent chromium [Cr(VI)], a common environmental and occupational pollutant, causes lung cancer, representing an important lung cancer etiology factor. The mechanism of how chronic Cr(VI) exposure causes lung cancer remains largely unknown. By using cell culture and mouse models and bioinformatics analyses of human lung cancer gene expression profiles, this study investigated the mechanism of Cr(VI)-induced lung carcinogenesis. A new mouse model of Cr(VI)-induced lung carcinogenesis was developed as evidenced by the findings showing that a 16-week Cr(VI) exposure (CaCrO4, 100 μg per mouse once per week) via oropharyngeal aspiration induced lung adenocarcinomas in male and female A/J mice, whereas none of the sham-exposed control mice had lung tumors. Mechanistic studies revealed that chronic Cr(VI) exposure activated the non-canonical NFκB pathway through the long non-coding RNA (lncRNA) ABHD11-AS1/deubiquitinase USP15-mediated tumor necrosis factor receptor-associated factor 3 (TRAF3) down-regulation. The non-canonical NFκB pathway activation increased the interleukin 6 (IL-6)/Janus kinase (Jak)/signal transducer and activator of transcription 3 (Stat3) signaling. The activation of the IL-6/Jak signaling axis by Cr(VI) exposure not only promoted inflammation but also stabilized the immune checkpoint molecule programmed death-ligand 1 (PD-L1) protein in the lungs, reducing T lymphocyte infiltration to the lungs. Given the well-recognized critical role of PD-L1 in inhibiting anti-tumor immunity, these findings suggested that the lncRNA ABHD11-AS1-mediated non-canonical NFκB pathway activation and PD-L1 up-regulation may play important roles in Cr(VI)-induced lung carcinogenesis.

Abstract 1160: Investigating spatial gene expression profiling in colorectal cancer: Tumor and stroma comparison between primary lesions and matched liver metastases

Abstract The nanoString GeoMx® Digital Spatial Profiling (DSP) enables the investigation of spatial assessment of tumors through high-plex profiling at the RNA and protein levels. Recent genomic analyses have revealed the intertumor heterogeneity between primary and metastatic lesions in colorectal cancer (CRC) patients. In CRC with liver metastases, current treatment strategies are mainly based on the parameters of primary tumors, and metastatic heterogeneity is a challenge since molecular heterogeneity contributes to therapeutic resistance. Furthermore, spatial intratumor heterogeneity exists within a single tumor between cancer cells (tumor) and their microenvironment (stroma) in human cancers. However, whether there are distinct spatial gene expression patterns of tumor and stroma between primary lesions and liver metastases remains unclear in CRCs. We examined 24 formalin-fixed paraffin-embedded tissue samples, including primary lesions and matched liver metastases from 12 patients (6 with synchronous and 6 with metachronous metastases) using NanoString GeoMx® DSP. The best regions of interest (ROI) with the invasive front boundary of the tumor were selected by pathologists. The ROI was segmented into PanCK-positive (tumor) and PanCK-negative (stroma), followed by a collection of indexed oligonucleotides and sequencing on Illumina instrument. Differential expression and pathways enrichment analyses were performed using R BioConductor package standR, limma, and GSEABase. Statistical significances were based on |Log2 fold change| > 1 and Benjamini-Hochberg-corrected P < 0.05. Immune cell abundance was estimated using the SpatialDecon package. In 12 metastatic patients (mean age, 61 years ± 7 [standard deviation]), liver metastatic stroma was associated with 68 upregulated and 93 downregulated genes compared to the primary stroma, with enrichment of 'Immune response' terms in gene set enrichment analysis (GSEA). A higher proportion of CD4 T memory cells and increased expression of cytotoxic genes (IL7R [2.02-fold], CD3E [1.75-fold], KLRB1 [1.68-fold], and GZMK [1.54-fold]) were observed in liver stroma compared to primary stroma. Conversely, liver metastatic tumors were associated only with 23 upregulated and 6 downregulated genes compared to primary tumors, with 'humoral response' terms enriched in GSEA. Similar patterns were observed in stratified analyses with synchronous and metachronous metastases, although a more prominent ‘immune response’ was observed in synchronous metastatic stroma compared to the primary stroma. We revealed that higher immune cell proportion and cytotoxic activity were observed in liver metastatic stroma compared to CRC stroma, providing novel insights into a unique etiology and may yield clinical implications for developing targeted treatment modalities for liver metastatic CRC patients. Citation Format: Jongwon Lee, Yeseul Kim, Hyo Seon Ryu, Jongmin Sim, Chungyeul Kim, Jong Min Park, Ah-Reum Lim, Jung Sun Kim, Hwa Jung Sung, Xingyi Guo, Jungmin Choi, Jungyoon Choi. Investigating spatial gene expression profiling in colorectal cancer: Tumor and stroma comparison between primary lesions and matched liver metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1160.

Abstract 6775: 3D culture aggregation morphology depends on cell phenotype and gene expression profiles in lung cancer

Abstract Background: Three-dimensional (3D) cell culture allows for the reproduction of tissue structure and function in a state more akin to the in vivo environment compared to two-dimensional (2D) cell culture. It is established that 3D culture more faithfully replicates cell morphology, differentiation, proliferation, gene and protein expression, and responsiveness to stimulation, as well as drug metabolism. Focusing on the observation that lung cancer cell lines exhibit different clustering morphologies in 3D cell culture, we conducted a comparative study on the relationship between these clustering morphologies and the characteristics of the lung cancer cell lines. Methods: We cultured 30 lung cancer cell lines using 3D culture plate, and classified the types of cell clustering morphologies formed. We evaluated the molecular and cellular biology characteristics of all cell lines. Additionally, comprehensive gene expression analysis using RNA sequencing was performed on the 30 3D-cultured cell samples, and the characteristics of gene expression between different clustering types were compared. Results: The 3D culture aggregation morphology of 30 lung cancer cell lines were classified into three types (monolayer sheet, NOS, spheroid). Monolayer sheet-type cells exhibited higher proliferation, migration, and invasion capabilities compared to spheroid-type cells. In the transcriptomic bioinformatics analysis of the acquired gene expression data, GSEA analysis using Hallmark gene sets showed that the enrichment of epithelial-mesenchymal transition (EMT) and cancer metastasis-related genes in the monolayer type cells significantly compared to the spheroid type cells. Conversely, cell adhesion and intercellular binding-related gene sets were highly enriched in the spheroid type cells. Conclusion: 3D culture aggregation morphology represents phenotypic properties and transcriptomic profiles in non-small cell lung cancer cells. Citation Format: Naohiro Hayashi, Shimpei Tsudaka, Ken Suzawa, Tomoaki Higashihara, Yin Min Thu, Atsushi Matsuoka, Daisuke Mizuno, Ryo Yoshichika, Fumiaki Mukohara, Mao Yoshikawa, Kazuhiko Shien, Hiromasa Yamamoto, Shinichi Toyooka. 3D culture aggregation morphology depends on cell phenotype and gene expression profiles in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6775.

Identification and prognostic analysis of candidate biomarkers for lung metastasis in colorectal cancer.

Colorectal cancer (CRC) is one of the most prevalent types of malignant tumors. It's vital to explore new biomarkers and potential therapeutic targets in CRC lung metastasis through adopting integrated bioinformatics tools. Multiple cohort datasets and databases were integrated to clarify and verify potential key candidate biomarkers and signal transduction pathways in CRC lung metastasis. DAVID, STRING, UALCAN, GEPIA, TIMER, cBioPortal, THE HUMAN PROTEIN ATLAS, GSEA 4.3.2, FUNRICH 3.1.3, and R 4.2.3 were utilized in this study. The enriched biological processes and pathways modulated by the differentially expressed genes (DEGs) were determined with Gene Ontology, Kyoto Encyclopedia of Genes and Genomes. The search tool Retrieval of Interacting Genes and Cytoscape were used to construct a protein-protein interaction network among DEGs. Four hundred fifty-nine colorectal primary cancer and lung metastatic gene expression profiles were screened from 3 gene expression profiles (GSE41258, GSE68468, and GSE41568). Forty-one upregulated genes and 8 downregulated genes were identified from these 3 gene expression profiles and verified by the transcriptional levels of hub genes in other GEO datasets and The Cancer Genome Atlas database. Two pathways (immune responses and chemokine receptors bind chemokines), 13 key DEGs, 6 hub genes (MMP3, SFTPD, ABCA3, CLU, APOE, and SPP1), and 2 biomarkers (APOE, SPP1) with significantly prognostic values were screened. Forty-nine DEGs were identified as potential candidate diagnostic biomarkers for patients with CRC lung metastasis in present study. Enrichment analysis indicated that immune responses and chemokine receptors bind chemokines may play a leading role in lung metastasis of CRC, and further studies are needed to validate these findings.

CeRNA Network and WGCNA Analyses of Differentially Expressed Genes in Cervical Cancer Tissues for Association with Survival of Patients.

The study aims to search and identify differentially expressed genes (DEGs) in cervical cancer tissues as novel biomarkers to predict cervical cancer prognosis. The Cancer Genome Atlas (TCGA) data on gene expression profiles in cervical cancer were downloaded and analyzed using R software to identify DEGs in cervical cancer tissues. miRNAs targeted by differentially expressed long non-coding RNAs (DElncRNAs) and mRNAs targeted by microRNAs (miRNAs) were identified using the online miRcode, miRTarBase, TargetScan, and miRDB tools. The ceRNA network and lncRNA expression modules in cervical cancer tissues were constructed using weighted gene co-expression network analysis (WGCNA) and analyzed bioinformatically. The Kaplan-Meier analysis was performed to confirm these DEGs as prognostic markers. Immunohistochemical (IHC) analysis was used to verify expression of the hub genes in 10 paired cervical cancer and normal tissues. A total of 1914 DEmRNAs, 210 DElncRNAs, and 67 DEmiRNAs were identified in cervical cancer samples. There were 39 lncRNAs, 19 miRNAs, and 87 mRNAs involved in the ceRNA network and 25 DElncRNAs, three DEmiRNAs, and four mRNAs involved in the ceRNA sub-network. CACNA1C-AS1 was associated with the yellow and blue modules in the ceRNA sub-network, and LIFR-AS1 was associated with the blue module. The DEmRNAs were involved in cancer-related pathways, and three hub genes (i.e., E2F1, CCNB1, and CCNE1) were highly expressed in cervical squamous cell carcinoma and adenocarcinoma tissues and associated with the prognosis of patients. The ceRNA network and WGCNA analyses are useful to identify novel DEGs that can serve as prognostic markers in cervical cancer. The DEGs will be validated in future studies.

GS-TCGA: Gene Set-Based Analysis of The Cancer Genome Atlas.

Most tools for analyzing large gene expression datasets, including The Cancer Genome Atlas (TCGA), have focused on analyzing the expression of individual genes or inference of the abundance of specific cell types from whole transcriptome information. While these methods provide useful insights, they can overlook crucial process-based information that may enhance our understanding of cancer biology. In this study, we describe three novel tools incorporated into an online resource; gene set-based analysis of The Cancer Genome Atlas (GS-TCGA). GS-TCGA is designed to enable user-friendly exploration of TCGA data using gene set-based analysis, leveraging gene sets from the Molecular Signatures Database. GS-TCGA includes three unique tools: GS-Surv determines the association between the expression of gene sets and survival in human cancers. Co-correlative gene set enrichment analysis (CC-GSEA) utilizes interpatient heterogeneity in cancer gene expression to infer functions of specific genes based on GSEA of coregulated genes in TCGA. GS-Corr utilizes interpatient heterogeneity in cancer gene expression profiles to identify genes coregulated with the expression of specific gene sets in TCGA. Users are also able to upload custom gene sets for analysis with each tool. These tools empower researchers to perform survival analysis linked to gene set expression, explore the functional implications of gene coexpression, and identify potential gene regulatory mechanisms.

Validation of serum cystatin SN detection for diagnosis and poor prognosis of esophageal squamous cell carcinoma.

The identification of effective tumor markers is of paramount importance for the early diagnosis, treatment, and prognosis of esophageal squamous cell carcinoma (ESCC). The present study endeavors to identify efficacious serological markers that can differentiate patients with early-stage ESCC from those with benign esophageal lesions and healthy controls (HC). Cystatin-SN (CST1), an active cysteine protease inhibitor belonging to the Cystatin (CST) superfamily, is implicated in the pathogenesis of inflammation and tumorigenesis. The objective of this investigation is to assess the diagnostic, therapeutic, and prognostic potential of serum CST1 in ESCC. In our prior RNA sequencing and screening endeavors, we have identified ten genes that are up-regulated in relation to esophageal cancer. Subsequently, we have verified the gene CST1 from the transcriptome data of the The Cancer Genome Atlas Program (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) database. Following this, we conducted an enzyme-linked immunosorbent assay (ELISA) to ascertain the expression levels of CST1 in serum samples from clinical cohorts. The study revealed a significant elevation in serum CST1 levels among patients with early-stage esophageal squamous cell carcinoma (ESCC) (7.41 ± 4.32 ng/ml) compared to those with esophageal benign lesions (4.67 ± 2.43 ng/ml) (p < 0.0001) and healthy controls (4.87 ± 2.77 ng/ml) (p < 0.0001). The diagnostic sensitivity of CST1 for ESCC was 75.68% (specificity 70.83%, AUC 0.775). Combination of CST1 and SCC-Ag exhibited the AUC up to 0.819. Additionally, serum CST1 levels exhibited a significant decrease at 1-2 weeks post-surgery (4.49 ± 3.31 ng/ml) compared to pre-surgery levels (7.68 ± 3.71 ng/ml) (p<0.0001). Survival analysis demonstrated a strong association between high (844/415-1543 d) or low (1490/645-1710 d) serum CST1 levels at diagnosis and overall survival time (p < 0.001). Furthermore, multivariate regression analysis confirmed CST1 (p=0.024, HR=2.023, 95%CI 1.099-3.725) as an independent prognostic factor. Serum CST1 has the potential to function as a diagnostic indicator for distinguishing early-stage esophageal squamous cell carcinoma (ESCC) from individuals with benign esophageal lesions and healthy individuals. Additionally, it could serve as a prognostic predictor and therapeutic efficacy indicator for patients with ESCC.

Molecular subtype identification and prognosis stratification based on lysosome-related genes in breast cancer

BackgroundLysosomes are known to have a significant impact on the development and recurrence of breast cancer. However, the association between lysosome-related genes (LRGs) and breast cancer remains unclear. This study aims to explore the potential role of LRGs in predicting the prognosis and treatment response of breast cancer. MethodsBreast cancer gene expression profile data and clinical information were downloaded from TCGA and GEO databases, and prognosis-related LRGs were screened for consensus clustering analysis. Lasso Cox regression analysis was used to construct risk features derived from LRGs, and immune cell infiltration, immune therapy response, drug sensitivity, and clinical pathological feature differences were evaluated for different molecular subtypes and risk groups. A nomogram based on risk features derived from LRGs was constructed and evaluated. ResultsOur study identified 176 differentially expressed LRGs that are associated with breast cancer prognosis. Based on these genes, we divided breast cancer into two molecular subtypes with significant prognostic differences. We also found significant differences in immune cell infiltration between these subtypes. Furthermore, we constructed a prognostic risk model consisting of 7 LRGs, which effectively divides breast cancer patients into high-risk and low-risk groups. Patients in the low-risk group have better prognostic characteristics, respond better to immunotherapy, and have lower sensitivity to chemotherapy drugs, indicating that the low-risk group is more likely to benefit from immunotherapy and chemotherapy. Additionally, the risk score based on LRGs is significantly correlated with immune cell infiltration, including CD8 T cells and macrophages. This risk score model, along with age, chemotherapy, clinical stage, and N stage, is an independent prognostic factor for breast cancer. Finally, the nomogram composed of these factors has excellent performance in predicting overall survival of breast cancer. ConclusionsIn conclusion, this study has constructed a novel LRG-derived breast cancer risk feature, which performs well in prognostic prediction when combined with clinical pathological features.

A bioinformatic analysis found low expression and clinical significance of ATF4 in breast cancer

BackgroundActivating Transcription Factor 4 (ATF4) expression exhibits differential patterns across different types of tumors. Besides, the pathogenesis of breast cancer is complex, and the exact relationship between ATF4 and ATF4 remains uncertain. MethodsThe analysis of ATF4 expression was conducted by utilizing The Cancer Genome Atlas (TCGA) pan-cancer data, while the gene expression profile of breast cancer was checked by the comprehensive database-Gene Expression Omnibus database. In order to gain a more comprehensive understanding of the specific cell types that exhibit ATF4 expression within the microenvironment of breast cancer, we conducted a single-cell analysis of ATF4 using two distinct datasets of human breast cancer (GSE114717 and GSE11088, respectively). The spatial distribution of ATF4 within a tissue was demonstrated based on datasets obtained from the Human Protein Atlas (HPA) and SpatialDB. The clinical prognostic significance of ATF4 was assessed by analyzing clinical survival data obtained from TCGA, GSE4830, and GSE25055 datasets. We used the R package clusterProfiler to carry out an enrichment analysis of ATF4. We assessed how ATF4 impacts the growth and movement of breast cancer cell lines. We manipulated ATF4 levels using plasmid transfection techniques. ResultsThe expression of ATF4 was found to be suboptimal and demonstrated a significant correlation with enhanced disease-specific survival (p = 0.012) and overall survival (p = 0.032) in breast cancer as well as other malignancies. We conducted an analysis to investigate the interaction between the infiltration level of immune cells and the expression of ATF4, using samples obtained from TCGA with known immune cell infiltration scores. Furthermore, a notable positive correlation exists between the elevated expression of ATF4 and immune-related genomes, specifically those associated with chemokine as well as immunity. Subsequent examination revealed a notable augmentation in the cytodifferentiation of T cells into regulatory T (Treg) cells within tissues exhibiting elevated levels of ATF4 expression. ATF4 exhibits notable upregulation in the MDA-MB-231 cell, thereby exerting a substantial impact on cell proliferation and migration upon its knockdown. Conversely, the overexpression of ATF4 in the MCF7 Luminal A breast cancer cell line can also modulate cellular function. ConclusionsOur study suggests that ATF4 helps T cells differentiate into Treg cells in breast cancer. ATF4 can represent a clinically useful biomarker to predict the overall survival rate, especially in patients with different subtypes of breast cancer. Provide certain guidance value for the development of targeted drugs or inhibitors targeting ATF4.

Single-cell RNA sequencing of human prostate basal epithelial cells reveals zone-specific cellular populations and gene expression signatures.

Despite evidence of genetic signatures in normal tissue correlating with disease risk, prospectively identifying genetic drivers and cell types that underlie subsequent pathologies has historically been challenging. The human prostate is an ideal model to investigate this phenomenon because it is anatomically segregated into three glandular zones (central, peripheral, and transition) that develop differential pathologies: prostate cancer in the peripheral zone (PZ) and benign prostatic hyperplasia (BPH) in the transition zone (TZ), with the central zone (CZ) rarely developing disease. More specifically, prostatic basal cells have been implicated in differentiation and proliferation during prostate development and regeneration; however, the contribution of zonal variation and the critical role of basal cells in prostatic disease etiology are not well understood. Using single-cell RNA sequencing of primary prostate epithelial cultures, we elucidated organ-specific, zone-specific, and cluster-specific gene expression differences in basal cells isolated from human prostate and seminal vesicle (SV). Aggregated analysis identified ten distinct basal clusters by Uniform Manifold Approximation and Projection. Organ specificity compared gene expression in SV with the prostate. As expected, SV cells were distinct from prostate cells by clustering, gene expression, and pathway analysis. For prostate zone specificity, we identified two CZ-specific clusters, while the TZ and PZ populations clustered together. Despite these similarities, differential gene expression was identified between PZ and TZ samples that correlated with gene expression profiles in prostate cancer and BPH, respectively. Zone-specific profiles and cell type-specific markers were validated using immunostaining and bioinformatic analyses of publicly available RNA-seq datasets. Understanding the baseline differences at the organ, zonal, and cellular level provides important insight into the potential drivers of prostatic disease and guides the investigation of novel preventive or curative treatments. Importantly, this study identifies multiple prostate basal cell populations and cell type-specific gene signatures within prostate basal epithelial cells that have potential critical roles in driving prostatic diseases. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Individually-tailored perioperative psychological intervention improves pro-metastatic gene expression profiles in breast cancer

Individually-tailored perioperative psychological intervention improves pro-metastatic gene expression profiles in breast cancer

ANXA3, associated with YAP1 regulation, participates in the proliferation and chemoresistance of cervical cancer cells.

Cervical cancer, as one of the most common cancers in women, remains a major health threat worldwide. Annexin A3 (ANXA3), a component of the annexin family, is upregulated in numerous cancers, with no explicit role in cervical cancer. This study aims to investigate the function of ANXA3 in cervical cancer. Differential expression genes between the cervical cancer tissues of patients and the controls were analyzed in The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA) database. Using transfection approaches to either upregulate or downregulate ANXA3, its role in cell proliferation and chemosensitivity of human cervical cancer cell lines (HeLa and C33A) was evaluated. Furthermore, the binding activity between YAP1 and ANXA3 was also explored. Genomics analysis indicated that differential genes were mostly associated with cell cycle progression and DNA replication. ANXA3 was highly expressed in the cervical cancer tissues and closely linked to malignancy degree. Knockdown of ANXA3 in cervical cancer cells inhibited cell cycle progression. A similar result was observed in the reduction of cyclin D, CDK4, cyclin E, and CDK2 in cervical cancer cells with ANXA3 silencing. Cervical cancer cells obtained high sensitivity to cisplatin (DDP) when ANXA3 was downregulated. Conversely, these capabilities were the opposite in cervical cancer cells overexpressing ANXA3. Furthermore, the expression levels of ANXA3 and YAP1 were positively correlated. YAP1 upregulation was positively connected with malignant behaviors, which were reversed by ANXA3 downregulation. In light of our findings, targeting ANXA3 expressed in cervical cancer might contribute to more potential therapeutic strategies.

Unveiling Potential Targeted Therapeutic Opportunities for Co-Overexpressed Targeting Protein for Xklp2 and Aurora-A Kinase in Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most prevalent and leading causes of cancer deaths globally, with limited diagnostic and clinically significant therapeutic targets. Identifying the genes and processes involved in developing and progressing LUAD is crucial for developing effective targeted therapeutics and improving patient outcomes. Therefore, the study aimed to explore the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) and gene expression profile datasets involving GSE10072, GSE31210, and GSE32863 from the Gene Expression Omnibus (GEO) databases. The differential gene expression and the downstream analysis determined clinically significant biomarkers using a network-based approach. These therapeutic targets predominantly enriched the dysregulation of mitotic cell cycle regulation and revealed the co-overexpression of Aurora-A Kinase (AURKA) and Targeting Protein for Xklp2 (TPX2) with high survival risk in LUAD patients. The hydrophobic residues of the AURKA-TPX2 interaction were considered as the target site to block the autophosphorylation of AURKA during the mitotic cell cycle. The tyrosine kinase inhibitor (TKI) dacomitinib demonstrated the strong binding potential to hinder TPX2, shielding the AURKA destabilization. This in silico study lays the foundation for repurposing targeted therapeutic options to impede the Protein-Protein Interactions (PPIs) in LUAD progression and aid in future translational investigations.