Upregulated NLGN1 predicts poor survival in colorectal cancer

Transcriptome Analysis of Transiently Reversible Cell Vacuolization Caused by Excessive Serum Concentration in Scophthalmus maximus

As an important research tool, cell lines play a vital role in life science research, medical research, and drug development. During the culture of the Scophthalmus maximus head kidney (TK) cell line, we found a phenomenon of cell vacuolization caused by excessive serum concentration. Moreover, the vacuolization of the cells gradually disappeared after passage by trypsin digestion. In clarifying the formation mechanism of this reversible cellular vacuolation, transcriptomics was utilized to explore the mechanism of cell vacuolization caused by excessive serum concentration. Transcriptome analysis indicated that excessive serum concentration could cause the up-regulated expression of PORCN and other genes to promote cell proliferation. Compared with cells whose vacuolization disappeared after trypsin digestion and passage, the expression of mitosis-related genes (BUB1, ttk, Mad2, Cdc20, CDK1, CCNB1), nuclear stability-related genes LMNB1 and tissue stress and repair-related genes HMMR in vacuolated cells caused by excessive serum concentration was significantly up-regulated. There is a regulatory system related to adaptation and stress repair in the cells, which can maintain cell stability to a certain extent. This study provides a theoretical basis for the stable culture of fish cell lines and the solution to the problem of cell vacuolation.

Molecular subtype identification and prognosis stratification by a immunogenic cell death-related gene expression signature in colorectal cancer

OBJECTIVES

This study intended to develop a new immunogenic cell death (ICD)-related prognostic signature for colorectal cancer (CRC) patients.

RESEARCH DESIGN AND METHODS

The Non-Negative Matrix Factorization (NMF) algorithm was adopted to cluster tumor samples based on ICD gene expression to obtain ICD-related subtypes. Survival analysis and immune microenvironment analysis were conducted among different subtypes. Regression analysis was used to construct the model. Based on riskscore median, cancer patients were classified into high and low risk groups, and independent prognostic ability of the model was analyzed. The CIBERSORT algorithm was adopted to determine the immune infiltration level of both groups.

RESULTS

We analyzed the differential genes between cluster 4 and cluster 1-3 and obtained 12 genes with the best prognostic features finally (NLGN1, SLC30A3, C3orf20, ADAD2, ATOH1, ATP6V1B1, KCNQ2, MUCL3, RGCC, CLEC17A, COL6A5, and INSL4). In addition, patients with lower risk had higher levels of infiltration of most immune cells, lower Tumor Immune Dysfunction and Exclusion (TIDE) level and higher immunophenscore (IPS) level than those with higher risk.

CONCLUSIONS

This study constructed and validated the ICD feature signature predicting CRC prognosis and provide a reference criteria for guiding the prognosis and immunotherapy of CRC cancer patients.

Identification of glutamine metabolism-related gene signature to predict colorectal cancer prognosis

Backgrounds: Colorectal cancer (CRC) is a highly malignant gastrointestinal malignancy with a poor prognosis, which imposes a significant burden on patients and healthcare providers globally. Previous studies have established that genes related to glutamine metabolism play a crucial role in the development of CRC. However, no studies have yet explored the prognostic significance of these genes in CRC. Methods: CRC patient data were downloaded from The Cancer Genome Atlas (TCGA), while glutamine metabolism-related genes were obtained from the Molecular Signatures Database (MSigDB) database. Univariate COX regression analysis and LASSO Cox regression were utilized to identify 15 glutamine metabolism-related genes associated with CRC prognosis. The risk scores were calculated and stratified into high-risk and low-risk groups based on the median risk score. The model's efficacy was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. Cox regression analysis was employed to determine the risk score as an independent prognostic factor for CRC. Differential immune cell infiltration between the high-risk and low-risk groups was assessed using the ssGSEA method. The clinical applicability of the model was validated by constructing nomograms based on age, gender, clinical staging, and risk scores. Immunohistochemistry (IHC) was used to detect the expression levels of core genes. Results: We identified 15 genes related to glutamine metabolism in CRC: NLGN1, RIMKLB, UCN, CALB1, SYT4, WNT3A, NRCAM, LRFN4, PHGDH, GRM1, CBLN1, NRG1, GLYATL1, CBLN2, and VWC2. Compared to the high-risk group, the low-risk group demonstrated longer overall survival (OS) for CRC. Clinical correlation analysis revealed a positive correlation between the risk score and the clinical stage and TNM stage of CRC. Immune correlation analysis indicated a predominance of Th2 cells in the low-risk group. The nomogram exhibited excellent discriminatory ability for OS in CRC. Immunohistochemistry revealed that the core gene CBLN1 was expressed at a lower level in CRC, while GLYATL1 was expressed at a higher level. Conclusions: In summary, we have successfully identified and comprehensively analyzed a gene signature associated with glutamine metabolism in CRC for the first time. This gene signature consistently and reliably predicts the prognosis of CRC patients, indicating its potential as a metabolic target for individuals with CRC.

Early results of the integrative epigenomic-transcriptomic landscape of colorectal adenoma and cancer

BACKGROUND

Aberrant methylation is common during the initiation and progression of colorectal cancer (CRC), and detecting these changes that occur during early adenoma (ADE) formation and CRC progression has clinical value.

AIM

To identify potential DNA methylation markers specific to ADE and CRC.

METHODS

Here, we performed SeqCap targeted bisulfite sequencing and RNA-seq analysis of colorectal ADE and CRC samples to profile the epigenomic-transcriptomic landscape.

RESULTS

Comparing 22 CRC and 25 ADE samples, global methylation was higher in the former, but both showed similar methylation patterns regarding differentially methylated gene positions, chromatin signatures, and repeated elements. High-grade CRC tended to exhibit elevated methylation levels in gene promoter regions compared to those in low-grade CRC. Combined with RNA-seq gene expression data, we identified 14 methylation-regulated differentially expressed genes, of which only AGTR1 and NECAB1 methylation had prognostic significance.

CONCLUSION

Our results suggest that genome-wide alterations in DNA methylation occur during the early stages of CRC and demonstrate the methylation signatures associated with colorectal ADEs and CRC, suggesting prognostic biomarkers for CRC.

Unfolded protein response signature unveils novel insights into breast cancer prognosis and tumor microenvironment

BACKGROUND

The critical role of the unfolded protein response (UPR) in tumorigenesis is widely acknowledged, yet the precise molecular mechanisms underlying its contribution to breast cancer (BC) have not been fully elucidated. The present study aimed to comprehensively explore the expression characteristics and prognostic significance of UPR-related genes in breast cancer METHODS: The transcriptome and clinical data of breast cancer were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. Differential expression analysis was conducted on UPR-related genes, and the resulting genes were employed for consensus clustering analysis. A breast cancer prognosis risk model was constructed using univariate, least absolute shrinkage and selection operator (LASSO), and multivariable Cox regression analyses. Difference in survival outcomes between groups were analyzed Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curve was used to assess predictive performance. The relationship between the risk model and clinical-pathological characteristics, immune infiltration, immunotherapy response, and drug sensitivity was assessed.

RESULTS

Differential expression analysis identified 10 UPR-related genes that were differentially expressed in breast cancer. Using the expression matrix of these genes, two molecular subtypes of breast cancer were characterized, which displayed significant differences in prognostic and immune infiltration characteristics. Drawing from the gene expression profiles that distinguish between the molecular subtypes, a prognostic risk scoring model comprising eight genes was developed. This model stratified BC patients from both the training and validation cohorts into high-risk and low-risk groups. Patients in the low-risk group had better prognoses, while those with advanced clinical stage and T stage exhibited higher risk scores. The high- and low-risk groups exhibited notable disparities in immune cell infiltration and the expression of multiple immune checkpoint-related genes. Additionally, the low-risk group demonstrated elevated immunophenoscore, Merck18, CD274, and CAF scores compared to the high-risk group, along with a lesser sensitivity to chemotherapy drugs. These results suggest that patients within the low-risk group may potentially benefit more from immunotherapy and chemotherapy interventions.

CONCLUSIONS

This study developed a novel UPR-derived risk signature, which could robustly predict the survival outcome, immune microenvironment, and chemotherapy response of patients with breast cancer.

PriPath: identifying dysregulated pathways from differential gene expression via grouping, scoring, and modeling with an embedded feature selection approach

BACKGROUND

Cell homeostasis relies on the concerted actions of genes, and dysregulated genes can lead to diseases. In living organisms, genes or their products do not act alone but within networks. Subsets of these networks can be viewed as modules that provide specific functionality to an organism. The Kyoto encyclopedia of genes and genomes (KEGG) systematically analyzes gene functions, proteins, and molecules and combines them into pathways. Measurements of gene expression (e.g., RNA-seq data) can be mapped to KEGG pathways to determine which modules are affected or dysregulated in the disease. However, genes acting in multiple pathways and other inherent issues complicate such analyses. Many current approaches may only employ gene expression data and need to pay more attention to some of the existing knowledge stored in KEGG pathways for detecting dysregulated pathways. New methods that consider more precompiled information are required for a more holistic association between gene expression and diseases.

RESULTS

PriPath is a novel approach that transfers the generic process of grouping and scoring, followed by modeling to analyze gene expression with KEGG pathways. In PriPath, KEGG pathways are utilized as the grouping function as part of a machine learning algorithm for selecting the most significant KEGG pathways. A machine learning model is trained to differentiate between diseases and controls using those groups. We have tested PriPath on 13 gene expression datasets of various cancers and other diseases. Our proposed approach successfully assigned biologically and clinically relevant KEGG terms to the samples based on the differentially expressed genes. We have comparatively evaluated the performance of PriPath against other tools, which are similar in their merit. For each dataset, we manually confirmed the top results of PriPath in the literature and found that most predictions can be supported by previous experimental research.

CONCLUSIONS

PriPath can thus aid in determining dysregulated pathways, which applies to medical diagnostics. In the future, we aim to advance this approach so that it can perform patient stratification based on gene expression and identify druggable targets. Thereby, we cover two aspects of precision medicine.

Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer

Background: Cancer-associated fibroblasts (CAFs) play an important role in the tumorigenesis, immunosuppression and metastasis of colorectal cancer (CRC), and can predict poor prognosis in patients with CRC. The present study aimed to construct a CAFs-related prognostic signature for CRC. Methods: The clinical information and corresponding RNA data of CRC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATES) and xCell methods were applied to evaluate the tumor microenvironment infiltration from bulk gene expression data. Weighted gene co-expression network analysis (WGCNA) was used to construct co-expression modules. The key module was identified by calculating the module-trait correlations. The univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were combined to develop a CAFs-related signature for the prognostic model. Moreover, pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases were employed to evaluate the protein expressions. Results: ESTIMATES and xCell analysis showed that high CAFs infiltration was associated with adverse prognoses. A twenty-gene CAFs-related prognostic signature (CAFPS) was established in the training cohort. Kaplan-Meier survival analyses reveled that CRC patients with higher CAFs risk scores were associated with poor prognosis in each cohort. Univariate and multivariate Cox regression analyses verified that CAFPS was as an independent prognostic factor in predicting overall survival, and a nomogram was built for clinical utility in predicting CRC prognosis. Patients with higher CAFs risk scores tended to not respond to immunotherapy, but were more sensitive to five conventional chemotherapeutic drugs. Conclusion: In summary, the CAFPS could serve as a robust prognostic indicator in CRC patients, which might help to optimize risk stratification and provide a new insight into individual treatments for CRC.

An Overview of the Molecular Cues and Their Intracellular Signaling Shared by Cancer and the Nervous System: From Neurotransmitters to Synaptic Proteins, Anatomy of an All-Inclusive Cooperation

We propose an overview of the molecular cues and their intracellular signaling involved in the crosstalk between cancer and the nervous system. While "cancer neuroscience" as a field is still in its infancy, the relation between cancer and the nervous system has been known for a long time, and a huge body of experimental data provides evidence that tumor-nervous system connections are widespread. They encompass different mechanisms at different tumor progression steps, are multifaceted, and display some intriguing analogies with the nervous system's physiological processes. Overall, we can say that many of the paradigmatic "hallmarks of cancer" depicted by Weinberg and Hanahan are affected by the nervous system in a variety of manners.

Establish of an Initial Platinum-Resistance Predictor in High-Grade Serous Ovarian Cancer Patients Regardless of Homologous Recombination Deficiency Status

Backgrounds

Ovarian cancer (OC) is still the leading aggressive and lethal disease of gynecological cancers, and platinum-based regimes are the standard treatments. However, nearly 20%–30% of patients with OC are initial platinum resistant (IPR), and there is a lack of valid tools to predict whether they will be primary platinum resistant or not prior to chemotherapy.

Methods

Transcriptome data from The Cancer Genome Atlas (TCGA) was downloaded as the training data, and transcriptome data of GSE15622, GSE102073, GSE19829, and GSE26712 were retrieved from Gene Expression Omnibus (GEO) as the validation cohorts. Differentially expressed genes (DEGs) were selected between platinum-sensitive and platinum-resistant patients from the training cohort, and multiple machine-learning algorithms [including random forest, XGboost, and least absolute shrinkage and selection operator (LASSO) regression] were utilized to determine the candidate genes from DEGs. Then, we applied logistic regression to establish the IPR signature based on the expression. Finally, comprehensive clinical, genomic, and survival feature were analyzed to understand the application value of the established IPR signature.

Results

A total of 532 DEGs were identified between platinum-resistant and platinum-sensitive samples, and 11 of them were shared by these three-machine learning algorithms and utilized to construct an IPR prediction signature. The area under receiver operating characteristic curve (AUC) was 0.841 and 0.796 in the training and validation cohorts, respectively. Notably, the prediction capacity of this signature was stable and robust regardless of the patients’ homologous recombination deficiency (HRD) and mutation burden status. Meanwhile, the genomic feature was concordant between samples with high- or low-IPR signature, except a significantly higher prevalence of gain at Chr19q.12 (regions including CCNE1) in the high-IPR signature samples. The efficacy of prediction of platinum resistance of IPR signature successfully transferred to the precise survival prediction, with the AUC of 0.71, 0.72, and 0.66 to predict 1-, 3-, and 5-year survival, respectively. At last, we found a significantly different tumor-infiltrated lymphocytes feature, including lower abundance of CD4+ naive T cells in the samples with high-IPR signature. A relatively lower tumor immune dysfunction and exclusion (TIDE) value and more sensitivity to multiple therapies including Gefitinib may suggest the potency to transfer from platinum-based therapy to immunotherapy or target therapies in patients with high-IPR signature.

Conclusion

Our study established an IPR signature based on the expression of 11 genes that could stably and robustly distinguish OC patients with IPR and/or poor outcomes, which may guide therapeutic regimes tailoring.