Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis

The CNC-family transcription factor NRF3: A crucial therapeutic target for cancer treatment

The CNC-bZIP family member NRF3 (NFE2L3) has received limited attention since its discovery. However, recent research has gradually revealed its biological functions, such as involvement in the regulation of cell differentiation, lipid metabolism, and malignant cell proliferation. Under physiological conditions, NRF3 is anchored to the endoplasmic reticulum within the cytoplasm and is biologically inactive. Upon cellular exposure to microenvironmental stresses such as oxidative stress, NRF3 translocates to the nucleus, binds to DNA, and acts as a transcription factor by inducing or repressing the expression of various genes. In terms of tumor regulation, NRF3 exhibits a dual role. It can function as a tumor suppressor to prevent the malignant progression of tumor tissues, protecting the organism from harm. Conversely, current research indicates that NRF3 plays a tumor-promoting role in most tumor tissues. NRF3 enhances the proliferation, migration and invasion of tumor cells by regulating cell cycle-related proteins and enhancing proteasome assembly to degrade tumor suppressors. Studies correlating NRF3 expression with clinical tumor features have found that elevated NRF3 expression is often associated with poor prognoses in various cancers, with patients exhibiting higher NRF3 expression typically having lower survival rates. Several studies suggest that NRF3 could serve as a clinical diagnostic and prognostic marker for tumors. Finally, from the clinical perspective, exploring the feasibility of inhibiting NRF3 activity in tumor treatment provides new insights for the development of NRF3-targeted oncological therapies.

Comprehensive bioinformatics analysis was used to identify and verify differentially expressed genes in targeted therapy of colon cancer

Colorectal cancer (CRC) is one of the most common malignant tumors. CLCA1 and ZG16 are lowly expressed in CRC, and we wanted to investigate whether they could be prognostic biomarkers for the malignant progression of CRC. 12,195 DEGs and 12,071 DEGs were identified through the GSE39582 dataset and TCGA dataset, and then 50 coexisting genes were selected for further analysis using Venn diagrams. These 50 DEGs were then subjected to GO and KEGG functional enrichment analyses, along with genome-wide GSEA. the first 5 core genes were identified and visualized using Cytoscape through the PPI network. Then the expression of ZG16 and CLCA1 in normal and tumor tissues were analyzed using GSE39582 and TCGA datasets, and correlation analysis, and survival analysis were performed. The expression of ZG16 and CLCA1 in CRC cells was verified by qRT-PCR, and cell proliferation, migration, and invasion abilities were detected by CCK-8, scratch assay, clone formation assay, and Transwell assay. The expression levels of ZG16 and CLCA1 were significantly lower in CRC tissues than in normal tissues. Survival analysis showed that low expression of ZG16 and CLCA1 was associated with poor survival outcomes. Multifactorial analysis showed that low expression of ZG16 and CLCA1 was an independent risk factor affecting tumor prognosis. Cellular experiments showed that cell proliferation, migration, and invasion were inhibited after overexpression of ZG16 and CLCA1. Correlation analysis showed that ZG16 and CLCA1 expression levels were positively correlated and the correlation was statistically significant. GSEA enrichment analysis based on CLCA1-related genes and ZG16-related genes (FDR < 0.25, P < 0.05) revealed that the related genes of both genes were closely related to the GnRH signaling pathway. CLCA1 and ZG16, which are lowly expressed in CRC tissues, are associated with poor prognosis of CRC and may be one of the markers for diagnostic screening and prediction of prognostic outcome in CRC. Meanwhile, CLCA1 and ZG16 may also be new targets for tumor immunotherapy.

Ornithine decarboxylase antizyme 2 (OAZ2) in human colon adenocarcinoma: a potent prognostic factor associated with immunity

Despite few studies focusing on the OAZ2 gene in colorectal cancer, its potential role in colon adenocarcinoma (COAD) prognosis and immune modulation remains underexplored. This study examines the expression and mechanistic involvement of OAZ2 in COAD using data from The Cancer Genome Atlas (TCGA) and additional laboratory experiments. We employed uni- and multivariate Cox hazard regression analyses to evaluate its prognostic significance and gene set enrichment analysis (GSEA) to identify related signaling pathways. Our findings demonstrate significantly lower OAZ2 expression in COAD tissues compared to normal counterparts (P < 0.05) and establish its value as an independent prognostic indicator (P < 0.05). Laboratory experiments further revealed that the protein and mRNA levels of OAZ2 are significantly diminished in COAD compared to adjacent normal tissues, while its antagonist AZIN2 shows elevated expression, suggesting a competitive interaction that may regulate tumor behavior. Overexpression of OAZ2 in RKO colorectal cancer cells significantly reduced their proliferation rate and impaired migration, confirming the functional impact of OAZ2 dysregulation in COAD. Gene Set Enrichment Analysis (GSEA) highlighted the involvement of OAZ2 in cardiac muscle contraction and oxidative phosphorylation pathways. Additionally, OAZ2's association with immune features such as tumor mutational burden (TMB), microsatellite instability (MSI), and immune infiltration underscores its integral role in the tumor microenvironment. These comprehensive findings position OAZ2 as a promising biomarker for COAD prognosis and a potential target for therapeutic intervention, with evidence supporting its regulatory effects on cell dynamics and tumor aggressiveness.

Identification of a metabolic-immune signature associated with prognosis in colon cancer and exploration of potential predictive efficacy of immunotherapy response

The role of metabolic reprogramming of the tumor immune microenvironment in cancer development and immune escape has increasingly attracted attention. However, the predictive value of differences in metabolism-immune microenvironment on the prognosis of colon cancer (CC) and the response to immunotherapy have not been elucidated. The aim of this study was to investigate changes in metabolism and immune profile of CC and to identify a reliable signature for predicting prognosis and therapeutic response. The metabolism and immune-related differential genes in CC were screened out by differential gene expression analysis. A metabolism and immune related prognostic signature was established by the least absolute shrinkage and selection operator (LASSO) Cox algorithm. The training cohort with 417 patients from The Cancer Genome Atlas (TCGA) database and the validation cohort of 232 patients from GSE17538 were used to confirm the robustness of the prognostic signature. Immunohistochemical staining scores were used to assess gene expression levels in our clinical samples. Gene ontology (GO) analysis, gene set enrichment analysis (GSEA), single nucleotide variation (SNV) analysis, immune infiltration and immune factors analysis were used to explore the characteristics of patients with different subtypes. Multiple cancer immunotherapy datasets were used to assess the response of patients with different subtypes to immune checkpoint inhibitors. We established the Metabolism and Immune-Related Prognostic Score (MIRPS) based on six genes (CD36, PCOLCE2, SCG2, CALB2, STC2, CLDN23) to predict the prognosis of CC patients. We found a correlation between MIRPS and the malignant phenotype, microsatellite subtype, mutation load, and immune escape in CC. Tumors with high MIRPS presented a higher tumor mutation load and a more prominent immunosuppressive microenvironment. This subset of patients may potentially respond well to immune checkpoint inhibitor therapy. MIRPS may be used as a novel prognostic tool for CC and have potential value for immunotherapy response prediction.

Hyaluronan-Mediated Motility Receptor (HMMR) Overexpression Is Correlated with Poor Survival in Patients with B-ALL

Acute lymphoblastic leukemia (ALL) is a malignant neoplasm with the highest incidence in the pediatric population. Although the 5-year overall survival is greater than 85%, in emerging countries such as Mexico, the mortality rate is high. In Mexico, B-ALL is the most common type of childhood cancer; different characteristics suggest the presence of the disease; however, the prognosis is dependent on clinical and laboratory features, and no adverse prognostic molecular marker for B-ALL has yet been identified. The present research aimed to identify the prognostic value of HMMR expression in pediatric patients with B-ALL. The differential expression profile of B-ALL cells was determined via in silico analysis, and HMMR expression was subsequently measured via qRT-PCR and immunocytochemistry. The results were statistically analyzed via the ROUT test, Kolmogorov-Smirnov Z test, and Mann-Whitney U test. ROC curves and the Youden index were constructed, and Kaplan-Meier curves were plotted. We found that HMMR expression was increased in B-ALL patients (p < 0.0001). We observed that high expression was related to poor prognosis (p < 0.05). We observed that high expression was related to poor prognosis (p < 0.05). The increase in HMMR expression could be a potential early molecular prognostic marker and/or a new target in childhood B-ALL patients.

Multiplexed Biomarker Detection Using DNA Payloads: Design, Assembly, and Analysis

Most biomarker assays are typically designed to detect a single molecule type (DNA, RNA, proteins, etc.) in a single assay. This means that monitoring a diverse biomarker panel could quickly become a complex endeavor, requiring different techniques, labs, and expertise. In this chapter, we describe a method for multiplexed biomarker detection from a single sample using variable-length DNA payload chains as the output signal. Through the course of the assay, payloads are systematically disassembled in the presence of specific biomarkers. The resulting distinctly sized fragments then yield characteristic gel electrophoresis band patterns, which can be detected and quantified using image analysis algorithms. We detail the entire process for constructing DNA payloads and conducting a biomarker detection assay, including the sequence design, laboratory assembly, running the assay, and final image analysis.

Prediction of bladder cancer prognosis and immune microenvironment assessment using machine learning and deep learning models

Bladder cancer (BCa) is a heterogeneous malignancy characterized by distinct immune subtypes, primarily due to differences in tumor-infiltrating immune cells and their functional characteristics. Therefore, understanding the tumor immune microenvironment (TIME) landscape in BCa is crucial for prognostic prediction and guiding precision therapy. In this study, we integrated 10 machine learning algorithms to develop an immune-related machine learning signature (IRMLS) and subsequently created a deep learning model to detect the IRMLS subtype based on pathological images. The IRMLS proved to be an independent prognostic factor for overall survival (OS) and demonstrated robust and stable performance (p < 0.01). The high-risk group exhibited an immune-inflamed phenotype, associated with poorer prognosis and higher levels of immune cell infiltration. We further investigated the cancer immune cycle and mutation landscape within the IRMLS model, confirming that the high-risk group is more sensitive to immune checkpoint immunotherapy (ICI) and adjuvant chemotherapy with cisplatin (p = 2.8e-10), docetaxel (p = 8.8e-13), etoposide (p = 1.8e-07), and paclitaxel (p = 6.2e-13). In conclusion, we identified and validated a machine learning-based molecular characteristic, IRMLS, which reflects various aspects of the BCa biological process and offers new insights into personalized precision therapy for BCa patients.

Integrated bioinformatics analysis for exploring hub genes and related mechanisms affecting the progression of gastric cancer

Objective Gastric cancer (GC) is a high-risk tumor disease worldwide. The goal of the current study was to explore new diagnostic and prognostic indicators for gastric cancer. Methods Database GSE19826 and GSE103236 were gained from the Gene Expression Omnibus (GEO) to screen for differentially expressed genes (DEGs), which were then grouped together as co-DEGs. GO and KEGG pathway analysis were used to investigate the function of these genes. The protein-protein interaction (PPI) network of DEGs was constructed by STRING. Results GSE19826 selected 493 DEGs in GC and gastric normal tissues, including 139 up-regulated genes and 354 down-regulated genes. A total of 478 DEGs were selected by GSE103236, including 276 up-regulated genes and 202 downregulated genes. 32 co-DEGs were overlapped from two databasesand involved in digestion, regulation of response to wounding, wound healing, potassium ion imports across plasma membrane, regulation of wound healing, anatomical structure homeostasis, and tissue homeostasis. KEGG analysis revealed that co-DEGs were mainly involved in ECM-receptor interaction, tight junction, protein digestion and absorption, gastric acid secretion and cell adhesion molecules. Twelve hub genes were screened by Cytoscape, including cholecystokinin B receptor (CCKBR), Collagen type I alpha 1 (COL1A1), COL1A2, COL2A1, COL6A3, COL11A1, matrix metallopeptidase 1 (MMP1), MMP3, MMP7, MMP10, tissue inhibitor of matrix metalloprotease 1 (TIMP1) and secreted phosphoprotein 1 (SPP1). Conclusions Twelve key genes affecting the progression of gastric cancer were obtained by bioinformatics, which may be potential biomarkers for the diagnosis and prognosis of GC.

PCPE-2 (procollagen C-proteinase enhancer-2): The non-identical twin of PCPE-1

PCPE-2 was discovered at the beginning of this century, and was soon identified as a close homolog of PCPE-1 (procollagen C-proteinase enhancer 1). After the demonstration that it could also stimulate the proteolytic maturation of fibrillar procollagens by BMP-1/tolloid-like proteinases (BTPs), PCPE-2 did not attract much attention as it was thought to fulfill the same functions as PCPE-1 which was already well-described. However, the tissue distribution of PCPE-2 shows both common points and significant differences with PCPE-1, suggesting that their activities are not fully overlapping. Also, the recently established connections between PCPE-2 (gene name PCOLCE2) and several important diseases such as atherosclerosis, inflammatory diseases and cancer have highlighted the need for a thorough reappraisal of the in vivo roles of this regulatory protein. In this context, the recent finding that, while retaining the ability to bind fibrillar procollagens and to activate their C-terminal maturation, PCPE-2 can also bind BTPs and inhibit their activity has substantially extended its potential functions. In this review, we describe the current knowledge about PCPE-2 with a focus on collagen fibrillogenesis, lipid metabolism and inflammation, and discuss how we could further advance our understanding of PCPE-2-dependent biological processes.

Integrative Multi-Omics Analysis Reveals Critical Molecular Networks Linking Intestinal-System Diseases to Colorectal Cancer Progression

Background/Objectives: Colorectal cancer (CRC) frequently co-occurs with intestinal system diseases (ISDs), yet their molecular interplay remains poorly understood. We employed a comprehensive bioinformatics approach to elucidate shared genetic signatures and pathways between CRC and ISDs. Methods: We systematically analyzed 12 microarray and RNA-seq datasets encompassing 989 samples across seven ISDs and CRC. Differentially expressed genes (DEGs) were identified using Limma and DESeq2. Functional enrichment analysis was performed using clusterProfiler. Protein-protein interaction networks were constructed via STRING and visualized with Cytoscape to identify hub genes. Clinical significance of shared genes was further assessed through survival analysis and validated by immunohistochemistry staining of 30 paired CRC-normal tissue samples. Results: Integrating bioinformatics and machine learning approaches, we uncovered 160 shared DEGs (87 upregulated, 73 downregulated), which predominantly enriched cell metabolism, immune homeostasis, gut-brain communication, and inflammation pathways. Network analysis revealed nine key hub proteins linking CRC and ISDs, with seven upregulated (CD44, MYC, IL17A, CXCL1, FCGR3A, SPP1, and IL1A) and two downregulated (CXCL12 and CCL5). Survival analysis demonstrated the prognostic potential of these shared genes, while immunohistochemistry confirmed their differential expression in CRC tissues. Conclusions: Our findings unveil potential biomarkers and therapeutic targets, providing insights into ISD-influenced CRC progression and offering a robust foundation for improved diagnostic and treatment strategies in ISD-associated CRC.

The diagnostic and prognostic value of C1orf174 in colorectal cancer

Introduction

Colorectal cancer (CRC) is among the lethal cancers, indicating the need for the identification of novel biomarkers for the detection of patients in earlier stages. RNA and microRNA sequencing were analyzed using bioinformatics and machine learning algorithms to identify differentially expressed genes (DEGs), followed by validation in CRC patients.

Methods

The genome-wide RNA sequencing of 631 samples, comprising 398 patients and 233 normal cases was extracted from the Cancer Genome Atlas (TCGA). The DEGs were identified using DESeq package in R. Survival analysis was evaluated using Kaplan-Meier analysis to identify prognostic biomarkers. Predictive biomarkers were determined by machine learning algorithms such as Deep learning, Decision Tree, and Support Vector Machine. The biological pathways, protein-protein interaction (PPI), the co-expression of DEGs, and the correlation between DEGs and clinical data were evaluated. Additionally, the diagnostic markers were assessed with a combioROC package. Finally, the candidate tope score gene was validated by Real-time PCR in CRC patients.

Results

The survival analysis revealed five novel prognostic genes, including KCNK13, C1orf174, CLEC18A, SRRM5, and GPR89A. Thirty-nine upregulated, 40 downregulated genes, and 20 miRNAs were detected by SVM with high accuracy and AUC. The upregulation of KRT20 and FAM118A genes and the downregulation of LRAT and PROZ genes had the highest coefficient in the advanced stage. Furthermore, our findings showed that three miRNAs (mir-19b-1, mir-326, and mir-330) upregulated in the advanced stage. C1orf174, as a novel gene, was validated using RT-PCR in CRC patients. The combineROC curve analysis indicated that the combination of C1orf174-AKAP4-DIRC1-SKIL-Scan29A4 can be considered as diagnostic markers with sensitivity, specificity, and AUC values of 0.90, 0.94, and 0.92, respectively.

Conclusion

Machine learning algorithms can be used to Identify key dysregulated genes/miRNAs involved in the pathogenesis of diseases, leading to the detection of patients in earlier stages. Our data also demonstrated the prognostic value of C1orf174 in colorectal cancer.

PCPE2: Expression of multifunctional extracellular glycoprotein associated with diverse cellular functions

Procollagen C-endopeptidase enhancer 2, known as PCPE2 or PCOC2 (gene name, PCOLCE2) is a glycoprotein that resides in the extracellular matrix, and is similar in domain organization to PCPE1/PCPE, PCOC1 (PCOLCE1/PCOLCE). Due to the many similarities between the two related proteins, PCPE2 has been assumed to have biological functions similar to PCPE. PCPE is a well-established enhancer of procollagen processing activating the enzyme, BMP-1. However, reports show that PCPE2 has a strikingly different tissue expression profile compared to PCPE. With that in mind and given the paucity of published studies on PCPE2, this review examines the current literature citing PCPE2 and its association with specific cell types and signaling pathways. Additionally, this review will present a brief history of PCPE2's discovery, highlighting structural and functional similarities and differences compared to PCPE. Considering the widespread use of RNA sequencing techniques to examine associations between cell-specific gene expression and disease states, we will show that PCPE2 is repeatedly found as a differentially regulated gene (DEG) significantly associated with a number of cellular processes, well beyond the scope of procollagen fibril processing.

Solute carrier family 4 member 4 (SLC4A4) is associated with cell proliferation, migration and immune cell infiltration in colon cancer

BACKGROUND

Solute Carrier Family 4 Member 4 (SLC4A4) is a membrane protein-coding gene for a Na+/HCO3- cotransporter and plays a crucial role in regulating pH, bicarbonate secretion and homeostasis. However, the prognostic and immunological role of SLC4A4 in colon cancer remains unknown.

METHOD

In this study, expression profiles of SLC4A4 were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, to which a variety of bioinformatic analyses were performed. Sangerbox, Xiantao, ESTIMATE and TIMER online tools were used to delve into the relationship between SLC4A4 expression and immune cell infiltration. The role of SLC4A4 in the proliferation and migration of colon cancer cells was verified by CCK8, EdU and wound healing assays. The related molecules and pathways that SLC4A4 may affect were validated by bioinformatic prediction and western blotting analysis.

RESULTS

The expression levels of SLC4A4 were significantly lower in colon cancer tissues than in normal tissues and its low expression was positively correlated with poor prognosis. TIMER and ESTIMATE showed that SLC4A4 broadly influenced immune cell infiltration. Experiments in vitro demonstrated that SLC4A4 inhibited partial epithelial-mesenchymal transition (EMT) phenotypes.

CONCLUSIONS

To conclude, our study revealed that SLC4A4 is lowly expressed in colon cancer tissues, and SLC4A4 may inhibit the progression of colon cancer via regulating partial EMT phenotypes and immune cell infiltration, which may provide new perspectives for the development of more precise and personalized immune anti-tumor therapies.

Transcription factor YY1-activated GNG5 facilitates glioblastoma cell growth, invasion, stemness and glycolysis through Wnt/β-catenin pathway

G protein subunit Gamma 5 (GNG5) has been found to be involved in regulating glioma progression. However, its function and mechanism in glioblastoma (GBM) progression need to be further elucidated. GBM cell proliferation, apoptosis, invasion and stemness were assessed by cell counting kit 8 assay, EdU assay, flow cytometry, transwell assay and sphere formation assay. The mRNA and protein levels of GNG5 and Yin Yang 1 (YY1) were determined by quantitative real-time PCR and western blot (WB). Detection of the glucose consumption, lactate production and ATP/ADP ratios were used to assess cell glycolysis. Besides, Wnt/β-catenin pathway-related protein levels were examined by WB. Mice xenograft model was also constructed to explore GNG5 roles in vivo. GNG5 was highly expressed in GBM, and its silencing inhibited GBM cell proliferation, invasion, stemness and glycolysis, while promoted apoptosis. Transcription factor YY1 could bind to the GNG5 promoter region and induce its expression. GNG5 overexpression reversed the inhibitory effects of YY1 silencing on GBM cell growth, invasion, stemness and glycolysis. YY1/GNG5 axis could activate the Wnt/β-catenin pathway, and Wnt/β-catenin pathway agonists SKL2001 could revert the effects of GNG5 silencing on GBM cell progression. Furthermore, GNG5 facilitated GBM tumor growth by mediating the Wnt/β-catenin pathway. YY1-mediated GNG5 promoted GBM progression through the Wnt/β-catenin pathway.

LncRNA HOXB-AS4 promotes proliferation and migration of colorectal cancer via the miR-140-5p/hdac7 axis

Long noncoding RNAs (lncRNA) have a critical role in colorectal cancer (CRC) development and progression. However, the role of the lncRNA HOXB-AS4 in CRC remains unclear. In this study, we found that HOXB-AS4 was markedly upregulated in tumor tissues compared to precancerous tissues. Loss-of-function assays in HT29 and SW480 cells confirmed that knockdown of HOXB-AS4 inhibited proliferation, migration, and promoted apoptosis. In addition, HOXB-AS4 was shown to regulate histone deacetylase 7 (HDAC7) expression by acting as a molecular sponge to bind to and adsorb miR-140-5p. These findings were confirmed by the dual-luciferase reporter assay. Functional recovery experiments further demonstrated the crucial role of the HOXB-AS4/miR-140-5p/HDAC7 axis in modulating the malignant phenotype of CRC cells. Collectively, our data suggested that HOXB-AS4 regulated the malignant tumor aggression of HT29 and SW480 cells through the miR-140-5p/HDAC7 axis and PI3K/AKT signaling pathway. Our study provides novel insights into the mechanism of action of HOXB-AS4 in CRC and highlights its potential use as a targeted therapy.

Gliomedin drives gastric cancer cell proliferation and migration, correlating with a poor prognosis

Gastric cancer (GC) is a prevalent global malignancy, often diagnosed at advanced stage due to a lack of early symptoms and reliable markers. Previous research has identified gliomedin (GLDN) as a potential predictive marker for poor prognosis in cancer patients. However, the specific relationship between GLDN expression and GC prognosis has been unclear. Using the Tumor-Immune System Interaction Database (TISIDB), we examined GLDN expression in GC tissues and found a positive correlation with advanced clinical stages. Kaplan-Meier Plotter analysis further demonstrated that elevated GLDN levels were closely associated with poor prognosis in GC patients. To explore the functional significance of GLDN in GC, we conducted experiments involving GLDN overexpression and knockdown in GC cell lines, as well as subcutaneous tumor formation in nude mice. Our findings provided compelling evidence that GLDN promotes GC cell proliferation, viability, and migration, significantly enhancing tumor growth in vivo. Mechanistically, RNA-sequencing (RNA-seq) combined with bioinformatics analysis revealed that GLDN influences genes enriched in the p53 signaling pathway. Our data suggest that GLDN likely regulates cell proliferation through the p53-p21-CyclinD/CDK4 signaling axis. In conclusion, our study underscores GLDN's critical role in regulating GC cell proliferation and migration, and proposes its potential as a prognostic marker for GC patients.

LncRNA HAND2-AS1 Inhibited Colon Cancer Progression By Regulating miR-3118/ZG16 Axis

LncRNA HAND2-AS1 is a novel cancer regulator, but the role and mechanisms of HAND2-AS1 involved with colon cancer (CC) progression remains unknown. The purpose of this research was to figure out how HAND2-AS1 regulates the progression of CC. Using qRT-PCR, we studied expression levels of miR-3118, HAND2-AS1, and ZG16 in CC tissues and cells. Protein levels of apoptosis-related proteins (Bax and Bcl-2) and ZG16 were quantified by western blotting. In vitro function analysis referred to western blotting, wound healing assay and CCK-8. The binding association among miR-3118, HAND2-AS1, and ZG16 was investigated using luciferase reporter and RIP assays. The functional role of HAND2-AS1 was analyzed using xenograft tumor models in vivo. In tissues and cells of CC, HAND2-AS1 was downregulated. We observed that HAND2-AS1 overexpression declined CC cell proliferation and migration while facilitating apoptosis. We further verified that when HAND2-AS1 is overexpressed it reduced CC tumor development in vivo. In CC cells and tissues, miR-3118 competed with HAND2-AS1 and was elevated. Further it was noted that the HAND2-AS1 when overexpressed, lessened the survival of CC cells, however overexpression of miR-3118 restored these changes. ZG16 was shown to be a target of miR-3118, it was found that ZG16 was downregulated in CC tissue and cells. We observed, high expression of ZG16 partially restored the enhanced malignant phenotype caused by miR-3118 overexpression. HAND2-AS1 inhibited CC progression by upregulating ZG16 expression through sponging miR-3118. Hence, HAND2-AS1/miR-3118/ZG16 axis could be a possible new target for CC treatment.

SLC4A4 is a novel driver of enzalutamide resistance in prostate cancer

The androgen receptor signaling inhibitor (ARSI) enzalutamide (Enz) has shown critical efficacy in the treatment of advanced prostate cancer (PCa). However, the development of drug resistance is a significant factor contributing to mortality in PCa patients. We aimed to explore the key mechanisms of Enz-resistance. Through analysis of GEO databases, we identified SLC4A4 as a novel driver in Enz resistance. Long-term Enz treatment leads to the up-regulation of SLC4A4, which in turn mediates P53 lactylation via the NF-κB/STAT3/SLC4A4 axis, ultimately leading to the development of Enz resistance and progression of PCa. SLC4A4 knockdown overcomes Enz resistance both in vitro and in vivo. Hence, our results suggest that targeting SLC4A4 could be a promising therapeutic strategy for Enz resistance. STATEMENT OF SIGNIFICANCE: SLC4A4 is a novel driver of enzalutamide resistance.

New insight into the CNC-bZIP member, NFE2L3, in human diseases

Nuclear factor erythroid 2 (NF-E2)-related factor 3 (NFE2L3), a member of the CNC-bZIP subfamily and widely found in a variety of tissues, is an endoplasmic reticulum (ER) membrane-anchored transcription factor that can be released from the ER and moved into the nucleus to bind the promoter region to regulate a series of target genes involved in antioxidant, inflammatory responses, and cell cycle regulation in response to extracellular or intracellular stress. Recent research, particularly in the past 5 years, has shed light on NFE2L3's participation in diverse biological processes, including cell differentiation, inflammatory responses, lipid homeostasis, immune responses, and tumor growth. Notably, NFE2L3 has been identified as a key player in the development and prognosis of multiple cancers including colorectal cancer, thyroid cancer, breast cancer, hepatocellular carcinoma, gastric cancer, renal cancer, bladder cancer, esophageal squamous cell carcinoma, T cell lymphoblastic lymphoma, pancreatic cancer, and squamous cell carcinoma. Furthermore, research has linked NFE2L3 to other cancers such as lung adenocarcinoma, malignant pleural mesothelioma, ovarian cancer, glioblastoma multiforme, and laryngeal carcinoma, indicating its potential as a target for innovative cancer treatment approaches. Therefore, to gain a better understanding of the role of NFE2L3 in disease, this review offers insights into the discovery, structure, function, and recent advancements in the study of NFE2L3 to lay the groundwork for the development of NFE2L3-targeted cancer therapies.

Hypoxia-related THBD+ macrophages as a prognostic factor in glioma: Construction of a powerful risk model

Glioma is a prevalent malignant tumour characterized by hypoxia as a pivotal factor in its progression. This study aims to investigate the impact of the most severely hypoxic cell subpopulation in glioma. Our findings reveal that the THBD+ macrophage subpopulation is closely associated with hypoxia in glioma, exhibiting significantly higher infiltration in tumours compared to non-tumour tissues. Moreover, a high proportion of THBD+ cells correlates with poor prognosis in glioblastoma (GBM) patients. Notably, THBD+ macrophages exhibit hypoxic characteristics and epithelial-mesenchymal transition features. Silencing THBD expression leads to a notable reduction in the proliferation and metastasis of glioma cells. Furthermore, we developed a THBD+ macrophage-related risk signature (THBDMRS) through machine learning techniques. THBDMRS emerges as an independent prognostic factor for GBM patients with a substantial prognostic impact. By comparing THBDMRS with 119 established prognostic features, we demonstrate the superior prognostic performance of THBDMRS. Additionally, THBDMRS is associated with glioma metastasis and extracellular matrix remodelling. In conclusion, hypoxia-related THBD+ macrophages play a pivotal role in glioma pathogenesis, and THBDMRS emerges as a potent and promising prognostic tool for GBM, contributing to enhanced patient survival outcomes.

Integrative analysis of transcriptome and proteome profiles in primary and recurrent glioblastoma

PURPOSE

Glioblastoma (GBM) is the most common and aggressive primary brain tumor characterized by poor prognosis and high recurrence. The underlying molecular mechanism that drives tumor progression and recurrence is unclear. This study is intended to look for molecular and biological changes that play a key role in GBM recurrence.

EXPERIMENTAL DESIGN

An integrative transcriptomic and proteomic analysis was performed on three primary GBM and three recurrent GBM tissues. Omics analyses were conducted using label-free quantitative proteomics and whole transcriptome sequencing.

RESULTS

A significant difference was found between primary GBM and recurrent GBM at the transcriptional level. Similar to other omics studies of cancer, a weak overlap was observed between transcriptome and proteome, and Procollagen C-Endopeptidase Enhancer 2 (PCOLCE2) was observed to be upregulated at mRNA and protein levels. Analysis of public cancer database revealed that high expression of PCOLCE2 is associated with poor prognosis of patients with GBM and that it may be a potential prognostic indicator. Functional and environmental enrichment analyses revealed significantly altered signaling pathways related to energy metabolism, including mitochondrial ATP synthesis-coupled electron transport and oxidative phosphorylation.

CONCLUSIONS AND CLINICAL RELEVANCE

This study provides new insights into the recurrence process of GBM through combined transcriptomic and proteomic analyses, complementing the existing GBM transcriptomic and proteomic data and suggesting that integrated multi-omics analyses may reveal new disease features of GBM.

Weighted Gene Co-expression Network Analysis and Machine Learning Validation for Identifying Major Genes Related to Sjogren's Syndrome

Sjogren's syndrome (SS) is an autoimmune disorder characterized by dry mouth and dry eyes. Its pathogenic mechanism is currently unclear. This study aims to integrate weighted gene co-expression network analysis (WGCNA) and machine learning to identify key genes associated with SS. We downloaded 3 publicly available datasets from the GEO database comprising the gene expression data of 231 SS and 78 control cases, including GSE84844, GSE48378 and GSE51092, and carried out WGCNA to elucidate differences in the abundant genes. Candidate biomarkers for SS were then identified using a LASSO regression model. Totally 6 machine-learning models were subsequently utilized for validating the biological significance of major genes according to their expression. Finally, immune cell infiltration of the SS tissue was assessed using the CIBERSORT algorithm. A weighted gene co-expression network was built to divide genes into 10 modules. Among them, blue and red modules were most closely associated with SS, and showed significant enrichment in type I interferon signaling, cellular response to type I interferon and response to virus, etc. Combined machine learning identified 5 hub genes, including OAS1, EIF2AK2, IFITM3, TOP2A and STAT1. Immune cell infiltration analysis showed that SS was associated with CD8+ T cell, CD4+ T cell, gamma delta T cell, NK cell and dendritic cell activation. WGCNA was combined with machine learning to uncover genes that may be involved in SS pathogenesis, which can be utilized for developing SS biomarkers and appropriate therapeutic targets.

Identification of extracellular matrix-related biomarkers in colon adenocarcinoma by bioinformatics and experimental validation

Backgrounds

Extracellular matrix (ECM) is an important component of tumor microenvironment, and its abnormal expression promotes tumor formation, progression and metastasis.

Methods

Weighted gene co-expression network analysis (WGCNA) was used to identify ECM-related hub genes based on The Cancer Genome Atlas (TCGA) colon adenocarcinoma (COAD) data. COAD clinical samples were used to verify the expression of potential biomarkers in tumor tissues, and siRNA was used to explore the role of potential biomarkers in cell proliferation and epithelial-mesenchymal transition (EMT).

Results

Three potential biomarkers (LEP, NGF and PCOLCE2) related to prognosis of COAD patients were identified and used to construct ERGPI. Immunohistochemical analysis of clinical samples showed that the three potential biomarkers were highly expressed in tumor tissues of COAD patients. Knockdown of LEP, NGF or PCOLCE2 inhibited COAD cell proliferation and EMT. Dictamnine inhibited tumor cell growth by binding to these three potential biomarkers based on molecular docking and transplanted tumor model.

Conclusion

The three biomarkers can provide new ideas for the diagnosis and targeted therapy of COAD patients.

Identification roles of NFE2L3 in digestive system cancers

BACKGROUND

Morbidity and mortality rates of Digestive System Cancers (DSC) continue to pose human lives and health. Nuclear factor erythroid 2-like protein 3 (NFE2L3) is aberrantly expressed in DSC. This study aimed to explore the clinical value and underlying mechanisms of NFE2L3 as a novel biomarker in DSC.

METHODS

We utilized data from databases and clinical gastric cancer specimens to validate the aberrant expression level of NFE2L3 and further assessed the clinical value of NFE2L3. To investigate the potential molecular mechanism of NFE2L3, we analyzed the correlation of NFE2L3 with immune molecular mechanisms, constructed PPI network, performed GO analysis and KEGG analysis, and finally explored the biological function of NFE2L3 in gastric cancer cells.

RESULTS

NFE2L3 expression is up-regulated in DSC and has both prognostic and diagnostic value. NFE2L3 correlates with various immune mechanisms, PPI network suggests proteins interacting with NFE2L3, GSEA analysis suggests potential molecular mechanisms for NFE2L3 to play a role in cancer promotion, and in vitro cellular experiments also confirmed the effect of NFE2L3 on the biological function of gastric cancer cells.

CONCLUSION

Our study confirms the aberrant expression and molecular mechanisms of NFE2L3 in DSC, indicating that NFE2L3 could serve as a novel biomarker for diagnosis and prognosis of DSC.

The State-of-the-Art Mechanisms and Antitumor Effects of Somatostatin in Colorectal Cancer: A Review

Somatostatin, a somatotropin release inhibiting factor (SST, SRIF), is a widely distributed multifunctional cyclic peptide and acts through a transmembrane G protein-coupled receptor (SST1-SST5). Over the past decades, research has begun to reveal the molecular mechanisms underlying the anticancer activity of this hormonal peptide. Among gastrointestinal tract (GIT) tumors, direct and indirect antitumor effects of SST have been documented best in gastroenteropancreatic neuroendocrine tumors (GEP-NETs) and less well in non-endocrine cancers, including sporadic colorectal cancer (CRC). In the latter, the signaling pathways involved in the antitumor function of SST are primarily MAPK/ERK/AKT and Wnt/β-catenin. Direct (involving the MAPK pathway) and indirect (VEGF production) antiangiogenic effects of SST in CRC have also been described. The anti-inflammatory role of SST in CRC is emphasized, but detailed molecular mechanisms are still being explored. The role of SST in tumor genome/tumor microenvironment (TME)/host's gut microbiome interactions is only partially known. The results of SST analogues (SSAs)' treatment of sporadic CRC in monotherapy in vivo are not spectacular. The current review aims to present the state-of-the-art mechanisms and antitumor activity of endogenous SST and its synthetic analogues in CRC, with particular emphasis on sporadic CRC.

Characterization and verification of CAF-relevant prognostic gene signature to aid therapy in bladder cancer

Cancer-associated fibroblasts (CAFs) are significantly involved in determining the patient's prognosis and response to bladder cancer (BLCA) therapy. CAFs can induce epithelial-mesenchymal transformation (EMT) as well as complex interaction with immune cells. Hence, it is imperative to identify potential markers for enhancing our understanding of CAFs in BLCA progression and immune regulation. A variety of algorithms and analyses were employed in the study, leading to the development of a novel prognostic feature for CAFs-Stromal-EMT (CSE)-prognostic feature. This feature was constructed based on the genes MFAP5, PCOLCE2, and JUN. Furthermore, we revealed that patients with higher CSE risk scores responded to immunotherapy better compared to those with lower. Finally, we verified two CSE-related genes using in vitro experiments. Our results suggested that the CSE-prognostic feature could predict the prognosis and evaluate the response of patients to immune and chemotherapies. This would aid clinicians in designing treatment strategies for patients with BLCA.

Macrophage Inflammatory Proteins (MIPs) Contribute to Malignant Potential of Colorectal Polyps and Modulate Likelihood of Cancerization Associated with Standard Risk Factors

Better understanding of molecular changes leading to neoplastic transformation is prerequisite to optimize risk assessment and chemopreventive and surveillance strategies. Data on macrophage inflammatory proteins (MIPs) in colorectal carcinogenesis are scanty and their clinical relevance remains unknown. Therefore, transcript and protein expression of CCL3, CCL4, CXCL2, and CCL19 were determined in 173 and 62 patients, respectively, using RT-qPCR and immunohistochemistry with reference to polyps' characteristics. The likelihood of malignancy was modeled using probit regression. With the increasing malignancy potential of hyperplastic-tubular-tubulo-villous-villous polyps, the expression of CCL3, CCL4, and CCL19 in lesions decreased. CCL19 expression decreased also in normal mucosa while that of CXCL2 increased. Likewise, lesion CCL3 and lesion and normal mucosa CCL19 decreased and normal CXCL2 increased along the hyperplasia-low-high dysplasia grade. The bigger the lesion, the lower CCL3 and higher CXCL2 in normal mucosa. Singular polyps had higher CCL3, CCL4, and CCL19 levels in normal mucosa. CCL3, CCL4 and CXCL2 modulated the likelihood of malignancy associated with traditional risk factors. There was no correlation between the protein and mRNA expression of CCL3 and CCL19. In summary, the polyp-adjacent mucosa contributes to gaining potential for malignancy by polyps. MIPs may help in specifying cancerization probability estimated based on standard risk factors.

Identification of PCPE-2 as the endogenous specific inhibitor of human BMP-1/tolloid-like proteinases

BMP-1/tolloid-like proteinases (BTPs) are major players in tissue morphogenesis, growth and repair. They act by promoting the deposition of structural extracellular matrix proteins and by controlling the activity of matricellular proteins and TGF-β superfamily growth factors. They have also been implicated in several pathological conditions such as fibrosis, cancer, metabolic disorders and bone diseases. Despite this broad range of pathophysiological functions, the putative existence of a specific endogenous inhibitor capable of controlling their activities could never be confirmed. Here, we show that procollagen C-proteinase enhancer-2 (PCPE-2), a protein previously reported to bind fibrillar collagens and to promote their BTP-dependent maturation, is primarily a potent and specific inhibitor of BTPs which can counteract their proteolytic activities through direct binding. PCPE-2 therefore differs from the cognate PCPE-1 protein and extends the possibilities to fine-tune BTP activities, both in physiological conditions and in therapeutic settings.

Multi-omics analysis of the oncogenic role of optic atrophy 1 in human cancer

OBJECTIVE

To investigate the prognostic significance of optic atrophy 1 (OPA1) in pan-cancer and analyze the relationship between OPA1 and immune infiltration in cancer.

RESULTS

OPA1 exhibited high expression levels or mutations in various types of tumor cells, and its expression levels were significantly correlated with the survival rate of tumor patients. In different tumor tissues, there was a notable positive correlation between OPA1 expression levels and the infiltration of cancer-associated fibroblasts in the immune microenvironment. Additionally, OPA1 and its related genes were found to be involved in several crucial biological processes, including protein phosphorylation, protein import into the nucleus, and protein binding.

CONCLUSION

OPA1 is highly expressed or mutated in numerous tumors and is strongly associated with protein phosphorylation, patient prognosis, and immune cell infiltration. OPA1 holds promise as a novel prognostic marker with potential clinical utility across various tumor types.

METHODS

We examined OPA1 expression in pan-cancer at both the gene and protein levels using various databases, including Tumor Immune Estimation Resource 2.0 (TIMER 2.0), Gene Expression Profiling Interactive Analysis (GEPIA2), UALCAN, and The Human Protein Atlas (HPA). We utilized the Kaplan-Meier plotter and GEPIA datasets to analyze the relationship between OPA1 expression levels and patient prognosis. Through the cBioPortal database, we detected OPA1 mutations in tumors and examined their relationship with patient prognosis. We employed the TIMER 2.0 database to explore the correlation between OPA1 expression levels in tumor tissue and the infiltration of cancer-associated fibroblasts in the immune microenvironment. Furthermore, we conducted a gene search associated with OPA1 and performed enrichment analysis to identify the main signaling pathways and biological processes linked to them.

Natural killer cell-related prognosis signature predicts immune response in colon cancer patients

Background: Natural killer (NK) cells are crucial components of the innate immune system that fight tumors and viral infections. Patients with colorectal cancer (CRC) have a poor prognosis, and immunotherapeutic tools play a key role in the treatment of CRC. Methods: Public data on CRC patients was collected from the TCGA and the GEO databases. Tissue data of CRC patients were collected from Guangxi Medical University Affiliated Cancer Hospital. An NK-related prognostic model was developed by the least absolute shrinkage and selection operator (LASSO) and Cox regression method. Validation data were collected from different clinical subgroups and an external independent validation cohort to verify the model's accuracy. In addition, multiple external independent immunotherapy datasets were collected to further examine the value of NK-related risk scores (NKRS) in the prediction of immunotherapy response. Potential biological functions of key genes were examined by methods of cell proliferation, apoptosis and Western blotting. Results: A novel prognostic model for CRC patients based on NK-related genes was developed and NKRS was generated. There was a significantly poorer prognosis among the high-NKRS group. Based on immune response prediction, patients with low NKRS may be more suitable for immunotherapy and they are more sensitive to immunotherapy. The proliferation rate of CRC cells was significantly reduced and apoptosis of CRC cells was increased after SLC2A3 was knocked down. SLC2A3 was also found to be associated with the TGF-β signaling pathway. Conclusion: NKRS has potential applications for predicting prognostic status and response to immunotherapy in CRC patients. SLC2A3 has potential as a therapeutic target for CRC.

Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis

Genomics and bioinformatics methods were used to screen genes and molecular markers correlated with colorectal cancer incidence and progression, and their biological functions were analyzed. Differentially expressed genes were obtained using the GEO2R program following colorectal cancer chip data GSE44076 retrieval from the Gene Expression Omnibus gene expression comprehensive database. An online database (David) that combines annotation, visualization, and gene discovery was utilized for investigating genes. Pathway and protein analyses were performed via resources from the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Visual analysis of the KEGG pathway was carried out according to ClueGO and CluePedia to establish the PPI network of gene interaction between pathways; the genes with the highest connectivity were screened by the molecular complex detection analysis method as Hub genes in this study; gene expression was verified by GEPIA online analysis tool, and Kaplan-Meier survival curve was drawn for prognosis analysis. By analyzing GSE44076 microarray data, 86 genes were selected, and colorectal cancer tissues' upregulation was observed in 27 genes and downregulation in 59 ones. GO assessment revealed that the differentially expressed genes were basically correlated with retinol dehydrogenase activity, carbon dehydrogenase activity, collagen-containing extracellular matrix, anchored component of memory, and cellular hormone metabolic process. Moreover, the KEGG assessment revealed that the differential genes contained various signal pathways such as retinol metabolism, chemical carotenogenesis, and nitrogen metabolism. Through further analysis of the PPI protein network, 4 clusters were obtained, and 16 Hub genes were screened out by combining the degree of each gene. Through the analysis of each gene on the prognosis of colon cancer through the GEPIA online analysis website, it was found that the expression levels of AQP8, CXCL8, and ZG16 genes were remarkably associated with colon cancer prognosis (P < 0.05). Genomics and bioinformatics methods can effectively analyze the genes and molecular markers correlated with colorectal cancer incidence and progression, help to systematically clarify the molecular mechanism of 16 key genes in colorectal cancer development and progression, and provide a theoretically valid insight for the screening of diagnostic markers of colorectal cancer and the selection of accurate targets for drug therapy.

Immune-related gene-based model predicts the survival of colorectal carcinoma and reflected various biological statuses

Bakcground: Prognosis of colorectal cancer (CRC) varies due to complex genetic-microenviromental interactions, and multiple gene-based prognostic models have been highlighted. Material and Method: In this work, the immune-related genes' expression-based model was developed and the scores of each sample were calculated. The correlation between the model and clinical information, immune infiltration, drug response and biological pathways were analyzed. Results: The high-score samples have a significantly longer survival (overall survival and progression-free survival) period than those with a low score, which was validated across seven datasets containing 1,325 samples (GSE17536 (N = 115), GSE17537 (N = 55), GSE33113 (N = 90), GSE37892 (N = 130), GSE38832 (N = 74), GSE39582 (N = 481), and TCGA (N = 380)). The score is significantly associated with clinical indicators, including age and stage, and further associated with PD-1/PD-L1 gene expression. Furthermore, high-score samples have significantly higher APC and a lower MUC5B mutation rate. The high-score samples show more immune infiltration (including CD4+ and CD8+ T cells, M1/M2 macrophages, and NK cells). Enriched pathway analyses showed that cancer-related pathways, including immune-related pathways, were significantly activated in high-score samples and that some drugs have significantly lower IC50 values than those with low score. Conclusion: The model developed based on immune-related genes is robust and reflected various statuses of CRC and may be a potential clinical indicator.

Identification of GUCA2A and COL3A1 as prognostic biomarkers in colorectal cancer by integrating analysis of RNA-Seq data and qRT-PCR validation

By 2030, it is anticipated that there will be 2.2 million new instances of colorectal cancer worldwide, along with 1.1 million yearly deaths. Therefore, it is critical to develop novel biomarkers that could help in CRC early detection. We performed an integrated analysis of four RNA-Seq data sets and TCGA datasets in this study to find novel biomarkers for diagnostic, prediction, and as potential therapeutic for this malignancy, as well as to determine the molecular mechanisms of CRC carcinogenesis. Four RNA-Seq datasets of colorectal cancer were downloaded from the Sequence Read Archive (SRA) database. The metaSeq package was used to integrate differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the string platform, and hub genes were identified using the cytoscape software. The gene ontology and KEGG pathway enrichment analysis were performed using enrichR package. Gene diagnostic sensitivity and its association to clinicopathological characteristics were demonstrated by statistical approaches. By using qRT-PCR, GUCA2A and COL3A1 were examined in colon cancer and rectal cancer. We identified 5037 differentially expressed genes, including (4752 upregulated, 285 downregulated) across the studies between CRC and normal tissues. Gene ontology and KEGG pathway analyses showed that the highest proportion of up-regulated DEGs was involved in RNA binding and RNA transport. Integral component of plasma membrane and mineral absorption pathways were identified as containing down-regulated DEGs. Similar expression patterns for GUCA2A and COL3A1 were seen in qRT-PCR and integrated RNA-Seq analysis. Additionally, this study demonstrated that GUCA2A and COL3A1 may play a significant role in the development of CRC.

Unraveling TIMP1: a multifaceted biomarker in colorectal cancer

Background: The pathogenic genes of colorectal cancer (CRC) have not yet been fully elucidated, and there is currently a lack of effective therapeutic targets. This study used bioinformatics methods to explore and experimentally validate the most valuable biomarkers for colorectal cancer and further investigate their potential as targets. Methods: We analyzed differentially expressed genes (DEGs) based on the Gene Expression Omnibus (GEO) dataset and screened out hub genes. ROC curve and univariate Cox analysis of The Cancer Genome Atlas (TCGA) dataset revealed the most diagnostically and prognostically valuable genes. Immunohistochemistry (IHC) experiments were then conducted to validate the expression level of these selected genes in colorectal cancer. Gene set enrichment analysis (GSEA) was performed to evaluate the enriched signaling pathways associated with the gene. Using the CIBERSORT algorithm in R software, we analyzed the immune infiltrating cell abundance in both high and low gene expression groups and examined the gene's correlation with immune cells and immune checkpoints. Additionally, we performed drug sensitivity analysis utilizing the DepMap database, and explored the correlation between gene expression levels and ferroptosis based on the The Cancer Genome Atlas dataset. Results: The study identified a total of 159 DEGs, including 7 hub genes: SPP1, MMP1, CXCL8, CXCL1, TIMP1, MMP3, and CXCL10. Further analysis revealed TIMP1 as the most valuable diagnostic and prognostic biomarker for colorectal cancer, with IHC experiments verifying its high expression. Additionally, GSEA results showed that the high TIMP1 expression group was involved in many cancer signaling pathways. Analysis of the TCGA database revealed a positive correlation between TIMP1 expression and infiltration of macrophages (M0, M1, M2) and neutrophils, as well as the expression of immune checkpoint genes, including CTLA-4 and HAVCR2. Drug sensitivity analysis, conducted using the DepMap database, revealed that colorectal cancer cell lines exhibiting elevated levels of TIMP1 expression were more responsive to certain drugs, such as CC-90003, Pitavastatin, Atuveciclib, and CT7001, compared to those with low levels of TIMP1. Furthermore, TIMP1 expression was positively correlated with that of ferroptosis-related genes, such as GPX4 and HSPA5. Conclusion: TIMP1 can be used as a biomarker for colorectal cancer and is associated with the immunological microenvironment, drug sensitivity, and ferroptosis inhibition in this disease.

Clinical importance and PI3K/Akt pathway-dependent anti-proliferative role of PALMD and DPT in breast cancer

This study aimed to identify novel differentially expressed genes in breast cancer and to explore the clinical value and the anti-tumor or oncogenic effects of the identified genes using bioinformatics analysis and in vitro experiments. The differentially expressed genes in breast cancer patients were identified using Gene Expression Omnibus (GEO) database with the cut-off criteria p < 0.05 and |logFC| > 1. The expression levels of palmdelphin (PALMD) and dermatopontin (DPT) in normal tissues and breast cancer tissues were evaluated based on GEPIA and UALCAN databases. PALMD and DPT expression levels in clinical subgroups of patients with breast cancer were analyzed to assess the association of PALMD and DPT expression with clinical characteristics. The prognostic and diagnostic values of PALMD and DPT in breast cancer were evaluated from Kaplan-Meier (K-M) survival curves and receiver operating characteristic (ROC) curves. Pearson's correlation coefficient was performed using LinkedOmics. KEGG pathway enrichment analysis was performed using DAVID. The protein levels were evaluated using western blot analysis. Cell proliferation was assessed using MTT and EdU assays. Two important genes, PALMD and DPT, were identified in breast cancer. The expression levels of PALMD and DPT were significantly lower in breast cancer tissues. The expression levels of PALMD were closely related to age, histological type, and T stage of breast cancer patients. The expression levels of DPT were closely related to age, histological type, T stage, N stage, estrogen receptor status, and progesterone receptor status of breast cancer patients. The K-M survival curves showed that PALMD or DPT was not an independent prognostic factor for breast cancer. The ROC curves showed that both PALMD and DPT had good diagnostic potential for breast cancer. KEGG pathway enrichment results showed that PI3K/Akt pathway was an important overlapping signaling for PALMD and DPT. Further studies proved that overexpression of PALMD and DPT inhibited proliferation in MCF-7 and MDA-MB-231 cells by suppressing the PI3K/Akt pathway. PALMD and DPT knockdown promoted proliferation in MCF-7 and MDA-MB-231 cells by activating the PI3K/Akt pathway. These results collectively suggested that PALMD and DPT might serve as potential diagnostic biomarkers and therapeutic targets for breast cancer.

The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach

Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to construct a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants-the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1-as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes-ASPHD1 and ZBTB12-and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.

TGM2, HMGA2, FXYD3, and LGALS4 genes as biomarkers in acquired oxaliplatin resistance of human colorectal cancer: A systems biology approach

Acquired resistance to oxaliplatin is considered as the primary reason for failure in colorectal cancer (CRC) therapy. Identifying the underlying resistance mechanisms may improve CRC treatment. The present study aims to identify the key genes involved in acquired oxaliplatin-resistant in CRC by confirming the oxaliplatin resistance index (OX-RI). To this aim, two public microarray datasets regarding oxaliplatin-resistant CRC cells with different OX-RI, GSE42387, and GSE76092 were downloaded from GEO database to identify differentially expressed genes (DEGs). The results indicated that the OX-RI affects the gene expression pattern significantly. Then, 54 common DEGs in both datasets including 18 up- and 36 down-regulated genes were identified. Protein-protein interaction (PPI) analysis revealed 13 up- (MAGEA6, TGM2, MAGEA4, SCHIP1, ECI2, CD33, AKAP12, MAGEA12, CALD1, WFDC2, VSNL1, HMGA2, and MAGEA2B) and 12 down-regulated (PDZK1IP1, FXYD3, ALDH2, CEACAM6, QPRT, GRB10, TM4SF4, LGALS4, ALDH3A1, USH1C, KCNE3, and CA12) hub genes. In the next step, two novel up-regulated hub genes including ECI2 and SCHIP1 were identified to be related to oxaliplatin resistance. Functional enrichment and pathway analysis indicated that metabolic pathways, proliferation, and epithelial-mesenchymal transition may play dominant roles in CRC progression and oxaliplatin resistance. In the next procedure, two in vitro oxaliplatin-resistant sub-lines including HCT116/OX-R4.3 and HCT116/OX-R10 cells with OX-IR 3.93 and 10.06 were established, respectively. The results indicated the up-regulation of TGM2 and HMGA2 in HCT116/OX-R10 cells with high OX-RI and down-regulation of FXYD3, LGALS4, and ECI2 in both cell types. Based on the results, TGM2, HMGA2, FXYD3, and LGALS4 genes are related to oxaliplatin-resistant CRC and may serve as novel therapeutic targets.

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.

TIMP1 represses sorafenib-triggered ferroptosis in colorectal cancer cells by activating the PI3K/Akt signaling pathway

BACKGROUND

Ferroptosis is involved in the drug resistance mechanisms of some tumors. The present study aimed to explore the role of tissue inhibitor of matrix metalloprotease 1 (TIMP1) in sorafenib-triggered ferroptosis in colorectal cancer (CRC).

METHODS

HCT-8 CRC cell lines were generated that were sorafenib-resistant or that under- or overexpressed TIMP1. The levels of reactive oxygen species (ROS), iron, and malondialdehyde (MDA) were compared across the different cell lines. The half-maximal inhibitory concentration of sorafenib against the different lines was determined based on cell viability. Expression of ferroptosis-related genes and the corresponding proteins was determined by quantitative RT-PCR or western blotting.

RESULTS

TIMP1 overexpression induced sorafenib resistance in HCT-8 cells. TIMP1 knockdown repressed the activation of the PI3K/Akt pathway and reduced levels of glutathione peroxidase 4 (GPX4), enhancing sorafenib-induced ferroptosis. This led to accumulation of ROS, iron, and MDA. Giving sorafenib and the GPX4 inhibitor RSL3 to sorafenib-resistant HCT-8 cells induced ferroptosis, leading to elevated levels of iron and lipid peroxides, ultimately reducing cell viability. TIMP1 depletion in CRC cells enhances sorafenib-triggered ferroptosis by reducing PI3K/Akt axis signal transduction.

CONCLUSION

The combination of sorafenib and GPX4 inhibitors such as RSL3 may be a promising therapy against CRC.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Development of novel DNAJB6-KIAA1522-p-mTOR three-protein prognostic prediction models for CRC

BACKGROUND

To evaluate the prognostic value of DNAJB6, KIAA1522, and p-mTOR expression for colorectal cancer (CRC) and to develop effective prognostic models for CRC patients.

METHODS

The expression of DNAJB6, KIAA1522, and p-mTOR (Ser2448) was detected using immunohistochemistry in 329 CRC specimens. The prognostic values of the three proteins in the training cohort were assessed using Kaplan-Meier curves and univariate and multivariate Cox proportional hazards models. Prediction nomogram models integrating the three proteins and TNM stage were constructed. Subsequently, calibration curves, receiver operating characteristic (ROC) curves, the concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the performance of the nomograms in the training and validation cohorts.

RESULTS

The three proteins DNAJB6, KIAA1522, and p-mTOR were significantly overexpressed in CRC tissues (each P < 0.01), and their expression was an independent prognostic factor for overall survival (OS) and disease-free survival (DFS) (each P < 0.05). The area under the ROC curves (AUC) and C-index values were approximately 0.7. Additionally, the calibration curves showed that the predicted values and the actual values fit well. Furthermore, DCA curves indicated that the clinical value of the nomogram models was higher than that of TNM stage. Overall, the novel prediction models have good discriminability, sensitivity, specificity and clinical utility.

CONCLUSION

The nomograms containing DNAJB6, KIAA1522, and p-mTOR may be promising models for predicting postoperative survival in CRC.

Transcriptomic data analysis coupled with copy number aberrations reveals a blood-based 17-gene signature for diagnosis and prognosis of patients with colorectal cancer

Background: Colorectal cancer (CRC) is the third most common cancer and third leading cause of cancer-associated deaths worldwide. Diagnosing CRC patients reliably at an early and curable stage is of utmost importance to reduce the risk of mortality. Methods: We identified global differentially expressed genes with copy number alterations in patients with CRC. We then identified genes that are also expressed in blood, which resulted in a blood-based gene signature. We validated the gene signature's diagnostic and prognostic potential using independent datasets of gene expression profiling from over 800 CRC patients with detailed clinical data. Functional enrichment, gene interaction networks and pathway analyses were also performed. Results: The analysis revealed a 17-gene signature that is expressed in blood and demonstrated that it has diagnostic potential. The 17-gene SVM classifier displayed 99 percent accuracy in predicting the patients with CRC. Moreover, we developed a prognostic model and defined a risk-score using 17-gene and validated that high risk score is strongly associated with poor disease outcome. The 17-gene signature predicted disease outcome independent of other clinical factors in the multivariate analysis (HR = 2.7, 95% CI = 1.3-5.3, p = 0.005). In addition, our gene network and pathway analyses revealed alterations in oxidative stress, STAT3, ERK/MAPK, interleukin and cytokine signaling pathways as well as potentially important hub genes, including BCL2, MS4A1, SLC7A11, AURKA, IL6R, TP53, NUPR1, DICER1, DUSP5, SMAD3, and CCND1. Conclusion: Our results revealed alterations in various genes and cancer-related pathways that may be essential for CRC transformation. Moreover, our study highlights diagnostic and prognostic value of our gene signature as well as its potential use as a blood biomarker as a non-invasive diagnostic method. Integrated analysis transcriptomic data coupled with copy number aberrations may provide a reliable method to identify key biological programs associated with CRC and lead to improved diagnosis and therapeutic options.

Role of Traditional Chinese Medicine Syndrome Type, Gut Microbiome, and Host Immunity in Predicting Early and Advanced Stage Colorectal Cancer

OBJECTIVE

To investigate the role of Traditional Chinese Medicine (TCM) syndrome type, gut microbiome distribution, and host immunity function in predicting the early and advanced clinical stages of colorectal cancer (CRC).

METHODS

A cross-sectional case-control study was performed which included 48 early stage and 48 advanced patients with CRC enrolled from March 2018 to December 2020. 16S rRNA gene sequencing was performed to analyze the gut microbiomes of the patients, while T and B lymphocyte subsets in peripheral blood were assessed using flow cytometry. TCM syndrome type was measured using the spleen deficiency syndrome (SDS) scale.

RESULTS

The abundance levels of Prevotella, Escherichia-Shigella, and Faecalibacterium in the gut microbiota were significantly increased in the advanced group, while Bacteroides was significantly decreased. Phascolarctobacterium was detectable only in the early metaphase group, whereas Alistipes was detectable only in the advanced group. The lymphocyte (P = .006), T helper cell (TH) (P = .002), cytotoxic T cell (TC) (P = .003), double positive T cell (DPT) (P = .02), and total T counts (P = .001) were significantly higher in the early metaphase group than in the advanced metaphase group. Compared with patients with early stage CRC, the advanced group had a higher SDS score. After adjusting for clinical stage, Spearman's correlation analysis showed interactions among gut microbiome abundance, T cell level, and SDS score. Multivariate logistic analysis showed that after controlling for the SDS score, abundance of Alistipes and Faecalibacterium, and double negative T cell (DNT) level, DPT was significantly associated with a lower risk of advanced-stage disease (hazard ratio, 0.918; P = .022).

CONCLUSION

Our study suggested associations between clinical stage, SDS, gut microbiota, and T lymphocytes, which provided insights for a potential prediction model for the disease progression of CRC.

Prognostic value of SLC4A4 and its correlation with the microsatellite instability in colorectal cancer

Objective

To explore new biomarkers related to microsatellite instability in order to better predict prognosis and guide medication.

Methods

The "limma" R package was used to identify differentially expressed genes in GSE24514, and then weighted correlation network analysis was used to select key genes. Different cell types in the tumor microenvironment were identified and analyzed by single-cell sequencing, with a Lasso regression model used to screen prognostic variables. Furthermore, the correlation between microsatellite instability and potential prognostic variables was explored, as well as the expression characteristics and clinical characteristics of the prognostic variables in the TCGA, UALCAN, and HPA databases. PCR assay was used to investigate the expression of SLC4A4 in colorectal cancer cell lines. Finally, we further verified the expression of SLC4A4 by immunohistochemistry.

Results

First, 844 differentially expressed genes in GSE24514 were identified. Subsequently, weighted co-expression network analysis (WGCNA) of GSE24514 obtained all the genes significantly associated with microsatellite instability (MSI), a total of 1452. Analysis of GSE166555 single cell sequencing data set yielded 1564 differentially expressed genes. The gene sets obtained from the above three analysis processes were intersected, and 174 genes were finally obtained. The Lasso regression model revealed two potential prognostic genes, TIMP1 and SLC4A4, of which, there was a stronger correlation between microsatellite instability and SLC4A4. The mRNA and protein expression of SLC4A4 was significantly decreased in tumors, and patients with low SLC4A4 expression had a poor prognosis. In addition, SLC4A4 was specifically expressed in epithelial cells. In the microenvironment of colorectal cancer, malignant cells have a strong interaction with different stromal cells. PCR showed that SLC4A4 was significantly down-regulated in colorectal cancer cell lines Caco-2, HCT116 and HT29 compared with normal control NCM460 cell lines. Immunohistochemistry also showed low expression of SLC4A4 in colorectal cancer.

Conclusion

SLC4A4, as a tumor suppressor gene, is significantly downregulated and positively correlated with microsatellite instability, thus it may be combined with microsatellite instability to guide colorectal cancer treatment.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

A pan-cancer analysis of the oncogenic role of procollagen C-endopeptidase enhancer (PCOLCE) in human

There is no evidence showing that the expression of procollagen C-endopeptidase enhancer (PCOLCE) is associated with human tumors, and pan-cancer analysis is not available. Based on public databases such as the cancer genome atlas, we investigated the potential role of PCOLCE expression in 33 different human tumors. PCOLCE expression in 11 tumors was significantly correlated with tumor prognosis and was a prognostic predictor for pancreatic adenocarcinoma, thymoma and CES. We also found that PCOLCE expression correlated with the immune microenvironment of tumors and the level of cancer-associated fibroblast infiltration. PCOLCE is a potential predictor of small molecule targeted drugs and immune checkpoint inhibitors. Finally, we found by enrichment analysis that PCOLCE localizes to extracellular structures and the extracellular matrix and exerts substantial effects on tumors through the PI3K-Akt and AGE-RAGE signaling pathways. We have a preliminary and relatively comprehensive understanding of the role of PCOLCE in various tumors.

HS3ST3A1 and CAPN8 Serve as Immune-Related Biomarkers for Predicting the Prognosis in Thyroid Cancer

Background

Thyroid cancer (TC) tends to be a common malignancy worldwide and results in various outcomes due to its different subtypes. The tumor microenvironment (TME) was demonstrated to play crucial roles in various malignancies, including thyroid cancer. This study combined the ESTIMATE and CIBERSORT algorithms, identified four TME-related genes, and evaluated their correlation with clinical characteristics. These findings revealed the malignant performance of TME in TC, and the TME-related DEGs might serve as prognostic biomarkers, which can be utilized for the prediction of immunotherapy effects in patients with TC.

Methods

The clinical and gene expression profiles of TC patients were collected from the TCGA dataset. The ESTIMATE algorithm was utilized to estimate stromal and immune scores and predict the level of stromal and immune cell infiltration. The differential expressed genes related to TME were filtered by the "limma" package in R, and the PPI network was constructed by a string website. KEGG pathway and GO analyses were performed to investigate the biological progression and molecular functions of TME-related DEGs. Then, univariate Cox regression analysis was employed to screen four genes correlated with clinical characteristics. GSEA was conducted to assess their roles in the TME of TC. To further investigate the association between TME-related genes and tumor-infiltrating immune cells (TIICs), the CIBERSORT algorithm was performed. Finally, the malignancy behaviors of the two genes were verified by RT-qPCR, IHC, MTT, colony formation, and transwell assays.

Results

Four TME-related DEGs, LRRN4CL, HS3ST3A1, PCOLCE2, and CAPN8, were identified and were significantly predictive of poor overall survival. KEGG and GO pathway analysis established that the TME-related DEGs were involved in immune responses and pathways in cancer. Furthermore, the malignancy behaviors of HS3ST3A1 and CAPN8 were verified by cellular functional experiments. These results revealed that the TME-related genes HS3ST3A1 and CAPN8 were able to serve as predictors of prognosis in patients with TC.

Conclusion

HS3ST3A1 and CAPN8 may serve as valuable prognostic biomarkers and TME indicators, which can be utilized for the prediction of immunotherapy effects and provide novel treatment strategies for patients with TC.

Neuromedin U secreted by colorectal cancer cells promotes a tumour-supporting microenvironment

BACKGROUND

Neuromedin U (NMU) was identified as one of the hub genes closely related to colorectal cancer (CRC) progression and was recently shown to be a motility inducer in CRC cells. Its autocrine signalling through specific receptors increases cancer cell migration and invasiveness. Because of insufficient knowledge concerning NMU accessibility and action in the tumour microenvironment, its role in CRC remains poorly understood and its potential as a therapeutic target is still difficult to define.

METHODS

NMU expression in CRC tissue was detected by IHC. Data from The Cancer Genome Atlas were used to analyse gene expression in CRC. mRNA and protein expression was detected by real-time PCR, immunoblotting or immunofluorescence staining and analysed using confocal microscopy or flow cytometry. Proteome Profiler was used to detect changes in the profiles of cytokines released by cells constituting tumour microenvironment after NMU treatment. NMU receptor activity was monitored by detecting ERK1/2 activation. Transwell cell migration, wound healing assay and microtube formation assay were used to evaluate the effects of NMU on the migration of cancer cells, human macrophages and endothelial cells.

RESULTS

Our current study showed increased NMU levels in human CRC when compared to normal adjacent tissue. We detected a correlation between high NMUR1 expression and shorter overall survival of patients with CRC. We identified NMUR1 expression on macrophages, endothelial cells, platelets, and NMUR1 presence in platelet microparticles. We confirmed ERK1/2 activation by treatment of macrophages and endothelial cells with NMU, which induced pro-metastatic phenotypes of analysed cells and changed their secretome. Finally, we showed that NMU-stimulated macrophages increased the migratory potential of CRC cells.

CONCLUSIONS

We propose that NMU is involved in the modulation and promotion of the pro-metastatic tumour microenvironment in CRC through the activation of cancer cells and other tumour niche cells, macrophages and endothelial cells. Video abstract.

Identification of necroptosis-related subtypes, development of a novel signature, and characterization of immune infiltration in colorectal cancer

Introduction

Necroptosis, a type of programmed cell death, has recently been extensively studied as an important pathway regulating tumor development, metastasis, and immunity. However, the expression patterns of necroptosis-related genes (NRGs) in colorectal cancer (CRC) and their potential roles in the tumor microenvironment (TME) have not been elucidated.

Methods

We explored the expression patterns of NRGs in 1247 colorectal cancer samples from genetics and transcriptional perspective. Based on a consensus clustering algorithm, we identified NRG molecular subtypes and gene subtypes, respectively. Furthermore, we constructed a necroptosis-related signature for predicting overall survival time and verified the predictive ability of the model. Using the ESTIMATE, CIBERSORT, and ssGSEA algorithms, we assessed the association between the above subtypes, scores and immune infiltration.

Results

Most NRGs were differentially expressed between CRC tissues and normal tissues. We found that distinct subtypes exhibited different NRGs expression, patients' prognosis, immune checkpoint gene expression, and immune infiltration characteristics. The scores calculated from the necroptosis-related signature can be used to classify patients into high-risk and low-risk groups, with the high-risk group corresponding to reduced immune cell infiltration and immune function, and a greater risk of immune dysfunction and immune escape.

Discussion

Our comprehensive analysis of NRGs in CRC demonstrated their potential role in clinicopathological features, prognosis, and immune infiltration in the TME. These findings help us deepen our understanding of NRGs and the tumor microenvironment landscape, and lay a foundation for effectively assessing patient outcomes and promoting more effective immunotherapy.

Integrated Analysis of Gene Expression and Metabolite Data Reveals Candidate Molecular Markers in Colorectal Carcinoma

Background: This study investigated potential gene targets and metabolite markers associated with colorectal carcinoma (CRC). Materials & Methods: Gene expression data (GSE110224) related with CRC were obtained from Gene Expression Omnibus, including 17 tumor tissues and 17 normal colon ones. The gene differential analysis, functional analysis, protein-protein interaction (PPI) analysis, and metabolite network construction were performed to identify key genes related to CRC. Moreover, an external dataset was used to validate genes of interest in CRC, and corresponding survival analysis was also conducted. Results: The authors extracted 197 differentially expressed genes (75 upregulated and 122 downregulated genes). Moreover, upregulated genes were closely associated with rheumatoid arthritis and amoebiasis pathways. The downregulated genes were mainly related to bile secretion and proximal tubule bicarbonate reclamation pathway. Combined with PPI network and metabolite prediction, the overlapped nine genes (CXCL1, CXCL8, CXCL10, HDS1782, IL18, PCK1, PTGS2, SERPINB2, TMP1) were found to be critical in CRC. Similar gene expression profiles of nine critical genes were validated by an external dataset, except for SERPINB2. In addition, the expressions of TIMP1, IL1B, and PTGS2 were closely related with prognosis. Finally, the metabolite network analysis revealed that there were close associations between prostaglandin E2 and three pathways (rheumatoid arthritis, amoebiasis, and leishmaniasis). Conclusion: CXCL1/CXCL8/IL1B/PTGS2-prostaglandin E2 axes were the potential signatures involved in CRC progression, which could provide new insights to understand the molecular mechanisms of CRC.