SCG2 is a Prognostic Biomarker Associated With Immune Infiltration and Macrophage Polarization in Colorectal Cancer

SCG2 and CPE may be novel markers for the identification of pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms

Background

Distinguishing pancreatic neuroendocrine tumors (pNETs) from solid pseudopapillary neoplasms (SPNs) is challenging, primarily due to their overlapping pathological characteristics. To address this, our study aims to identify and validate novel biomarkers that effectively differentiate between these two conditions. We focus on the exploration of new immunohistochemical markers to enhance this distinction.

Methods

In this study, we analyzed genetic variations in pNETs and SPNs using the GSE43795 dataset from the Gene Expression Omnibus (GEO) database. Our approach was to identify genes with higher expression in pNETs compared to SPNs and normal pancreatic tissues. We conducted enrichment analyses to understand the functions of these genes. Furthermore, protein-protein interaction (PPI) network analysis was utilized to identify key genes associated with pNETs. Our sample consisted of 163 pancreatic tumor specimens, comprising 78 pNETs and 85 SPNs. We also collected clinicopathological data and used immunohistochemistry to measure the expression levels of these key genes.

Results

The enrichment analysis revealed that genes overexpressed in pNETs were mainly involved in signal release, vesicle transport, and ion pathway activation, playing significant roles in endocrine processes like insulin secretion, dopamine synapses, and circadian rhythm regulation. The PPI analysis identified secretogranin II (SCG2), carboxypeptidase E (CPE), and chromogranin A (CgA, CHGA) as key markers for differentiating pNETs from SPNs. Immunohistochemical validation of these markers demonstrated high sensitivity (SCG2: 98.7%, CPE: 97.4%) and specificity (100%), indicating their superior discriminative power compared to traditional markers like CgA, β-catenin, lymphoid enhancer-binding factor 1 (LEF1), and vimentin.

Conclusions

Our study indicates that SCG2 and CPE are effective, novel immunohistochemical biomarkers for differentiating pNETs from SPNs.

Single-cell transcriptomics reveals stage- and side-specificity of gene modules in colorectal cancer

BACKGROUND

An understanding of mechanisms underlying colorectal cancer (CRC) development and progression is yet to be fully elucidated. This study aims to employ network theoretic approaches to analyse single cell transcriptomic data from CRC to better characterize its progression and sided-ness.

METHODS

We utilized a recently published single-cell RNA sequencing data (GEO-GSE178341) and parsed the cell X gene data by stage and side (right and left colon). Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified gene modules with varying preservation levels (weak or strong) of network topology between early (pT1) and late stages (pT234), and between right and left colons. Spearman's rank correlation (ρ) was used to assess the similarity or dissimilarity in gene connectivity.

RESULTS

Equalizing cell counts across different stages, we detected 13 modules for the early stage, two of which were non-preserved in late stages. Both non-preserved modules displayed distinct gene connectivity patterns between the early and late stages, characterized by low ρ values. One module predominately dealt with myeloid cells, with genes mostly enriched for cytokine-cytokine receptor interaction potentiallystimulating myeloid cells to participate in angiogenesis. The second module, representing a subset of epithelial cells, was mainly enriched for carbohydrate digestion and absorption, influencing the gut microenvironment through the breakdown of carbohydrates. In the comparison of left vs. right colons, two of 12 modules identified in the right colon were non-preserved in the left colon. One captured a small fraction of epithelial cells and was enriched for transcriptional misregulation in cancer, potentially impacting communication between epithelial cells and the tumor microenvironment. The other predominantly contained B cells with a crucial role in maintaining human gastrointestinal health and was enriched for B-cell receptor signalling pathway.

CONCLUSIONS

We identified modules with topological and functional differences specific to cell types between the early and late stages, and between the right and left colons. This study enhances the understanding of roles played by different cell types at different stages and sides, providing valuable insights for future studies focused on the diagnosis and treatment of CRC.

Identification of novel T cell proliferation patterns, potential biomarkers and therapeutic drugs in colorectal cancer

Background: T cells are crucial components of antitumor immunity. A list of genes associated with T cell proliferation was recently identified; however, the impact of T cell proliferation-related genes (TRGs) on the prognosis and therapeutic responses of patients with colorectal cancer (CRC) remains unclear. Methods: 33 TRG expression information and clinical information of patients with CRC gathered from multiple datasets were subjected to bioinformatic analysis. Consensus clustering was used to determine the molecular subtypes associated with T cell proliferation. Utilizing the Lasso-Cox regression, a predictive signature was created and verified in external cohorts. A tumor immune environment analysis was conducted, and potential biomarkers and therapeutic drugs were identified and confirmed via in vitro and in vivo studies. Results: CRC patients were separated into two TRG clusters, and differentially expressed genes (DEGs) were identified. Patient information was divided into three different gene clusters, and the determined molecular subtypes were linked to patient survival, immune cells, and immune functions. Prognosis-associated DEGs in the three gene clusters were used to evaluate the risk score, and a predictive signature was developed. The ability of the risk score to predict patient survival and treatment response has been successfully validated using multiple datasets. To discover more possible biomarkers for CRC, the weighted gene co-expression network analysis algorithm was utilized to screen key TRG variations between groups with high- and low-risk. CDK1, BATF, IL1RN, and ITM2A were screened out as key TRGs, and the expression of key TRGs was confirmed using real-time reverse transcription polymerase chain reaction. According to the key TRGs, 7,8-benzoflavone was identified as the most significant drug molecule, and MTT, colony formation, wound healing, transwell assays, and in vivo experiments indicated that 7,8-benzoflavone significantly suppressed the proliferation and migration of CRC cells. Conclusion: T cell proliferation-based molecular subtypes and predictive signatures can be utilized to anticipate patient results, immunological landscape, and treatment response in CRC. Novel biomarker candidates and potential therapeutic drugs for CRC were identified and verified using in vitro and in vivo tests.

[High expression of secretogranin II increases oxaliplatin resistance of colorectal cancer cells]

OBJECTIVE

To investigate the expression of secretogranin II (SCG2) in colorectal cancer (CRC) tissues and its impact on oxaliplatin resistance of CRC cells.

METHODS

We performed immunohistochemistry to detect the expression level of SCG2 on a tissue microarray containing 96 CRC and 84 adjacent tissues and analyzed the association of SCG2 expression with the clinical features of the CRC patients. SCG2 expression was also measured in DLD1 cells treated with oxaliplatin using immunoblotting and RT-qPCR analyses. The effects of SCG2 expression on oxaliplatin sensitivity and cell viability were evaluated in a DLD1 cell model of SCG2 knockout established using CRISPR-cas9 technique, and the expressions of apoptosis-related proteins were detected using Western blotting and RT-qPCR. We further examined SCG2 expression levels in an oxaliplatin-resistant DLD1 cell line and its parental DLD1 cells.

RESULTS

SCG2 expression was significantly increased in CRC tissues as compared with the adjacent tissues (1.932±0.816 vs 1), and the tumor tissues in advanced stages showed higher SCG2 expression levels. In DLD1 cells, treatment with oxaliplatin significantly increased SCG2 expression, and SCG2 knockout obviously increased oxaliplatin sensitivity of the cells and enhanced the expressions of apoptosis-related proteins. Compared with the parental cells, oxaliplatin-resistant DLD1 cells showed a significant increase of SCG2 expression by 3.901±0.471 folds.

CONCLUSION

SCG2 may serve as a risk gene in CRC, and its high expression increases oxaliplatin resistance of CRC cells.

Potential therapeutic target secretogranin II might cooperate with hypoxia-inducible factor 1α in sunitinib-resistant renal cell carcinoma

Multitargeted receptor tyrosine kinase inhibitors, including vascular endothelial growth factor (VEGF) inhibitors, such as sunitinib, have been used as the primary targeted agents for patients with recurrent or distant metastasis of advanced renal cell carcinoma (RCC). However, endogenous or acquired sunitinib resistance has become a significant therapeutic problem. Therefore, we focused on mechanisms of sunitinib resistance in RCC. First, we undertook RNA sequencing analysis using previously established sunitinib-resistant RCC (SUR-Caki1, SUR-ACHN, and SUR-A498) cells. The results showed increased expression of secretogranin II (SCG2, chromogranin C) in SUR-RCC cells compared to parental cells. The Cancer Genome Atlas database showed that SCG2 expression was increased in RCC compared to normal renal cells. In addition, the survival rate of the SCG2 high-expression group was significantly lower than that of the RCC low-expression group. Thus, we investigated the involvement of SCG2 in sunitinib-resistant RCC. In vitro analysis showed that migratory and invasive abilities were suppressed by SCG2 knockdown SUR cells. As SCG2 was previously reported to be associated with angiogenesis, we undertook a tube formation assay. The results showed that suppression of SCG2 inhibited angiogenesis. Furthermore, coimmunoprecipitation assays revealed a direct interaction between SCG2 and hypoxia-inducible factor 1α (HIF1α). Expression levels of VEGF-A and VEGF-C downstream of HIF1α were found to be decreased in SCG2 knockdown SUR cells. In conclusion, SCG2 could be associated with sunitinib resistance through VEGF regulation in RCC cells. These findings could lead to a better understanding of the VHL/HIF/VEGF pathway and the development of new therapeutic strategies for sunitinib-resistant RCC.

Cross-Talk between N6-Methyladenosine and Their Related RNAs Defined a Signature and Confirmed m6A Regulators for Diagnosis of Endometriosis

An RNA modification known as N6-methyladenosine (m6A) interacts with a range of coding and non-coding RNAs. The majority of the research has focused on identifying m6A regulators that are differentially expressed in endometriosis, but it has ignored their mechanisms that are derived from the alterations of modifications among RNAs, affecting the disease progression primarily. Here, we aimed to investigate the potential roles of m6A regulators in the diagnostic potency, immune microenvironment, and clinicopathological features of endometriosis through interacting genes. A GEO cohort was incorporated into this study. Variance expression profiling was executed via the "limma" R package. Pearson analysis was performed to investigate the correlations among 767 interacting lncRNAs, 374 interacting mRNAs, and 23 m6A regulators. K-means clustering analysis, based on patterns of mRNA modifications, was applied to perform clinical feature analysis. Infiltrating immune cells and stromal cells were calculated using the Cibersort method. An m6A-related risk model was created and supported by an independent risk assay. LASSO regression analysis and Cox analyses were implemented to determine the diagnostic genes. The diagnostic targets of endometriosis were verified using PCR and the WB method. Results: A thorough investigation of the m6A modification patterns in the GEO database was carried out, based on mRNAs and lncRNAs related to these m6A regulators. Two molecular subtypes were identified using unsupervised clustering analysis, resulting in further complex infiltration levels of immune microenvironment cells in diversified endometriosis pathology types. We identified two m6A regulators, namely METTL3 and YTHDF2, as diagnostic targets of endometriosis following the usage of overlapping genes to construct a diagnostic m6A signature of endometriosis through multivariate logistic regression, and we validated it using independent GSE86534 and GSE105764 cohorts. Finally, we found that m6A alterations might be one of the important reasons for the progression of endometriosis, especially with significant downregulation of the expressions of METTL3 and YTHDF2. Finally, m6A modification patterns have significant effects on the diversity and complexity of the progression and immune microenvironment, and might be key diagnostic markers for endometriosis.

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs associated with them were identified. We performed immune microenvironment and functional enrichment analyses. The analysis was followed by applying univariate/multivariate Cox regression algorithms to finally identify three lncRNAs associated with the immune pathway for the construction of prognostic prediction models (CXCL10, RXRG, and SCG2). This stepwise downscaling method, which finally screens out prognostic factors and key genes and then uses them to build a risk model, has excellent predictive power. According to analyses of the model's reliability, it was able to differentiate the prognostic value and continued existence of Skin cutaneous melanoma patient populations more effectively. This study is an analysis of the immune pathway that leads lncRNAs in Skin cutaneous melanoma in an effort to open up new treatment avenues for Skin cutaneous melanoma.

An epithelial-mesenchymal transition-related mRNA signature associated with the prognosis, immune infiltration and therapeutic response of colon adenocarcinoma

Background: Epithelial-mesenchymal transition (EMT) is closely associated with cancer cell metastasis. Colon adenocarcinoma (COAD) is one of the most common malignancies in the world, and its metastasis leading to poor prognosis remains a challenge for clinicians. The purpose of this study was to explore the prognostic value of EMT-related genes (EMTRGs) by bioinformatics analysis and to develop a new EMTRGs prognostic signature for COAD. Methods: The TCGA-COAD dataset was downloaded from the TCGA portal as the training cohort, and the GSE17538 and GSE29621 datasets were obtained from the GEO database as the validation cohort. The best EMTRGs prognostic signature was constructed by differential expression analysis, Cox, and LASSO regression analysis. Gene set enrichment analysis (GSEA) is used to reveal pathways that are enriched in high-risk and low-risk groups. Differences in tumor immune cell levels were analyzed using microenvironmental cell population counter and single sample gene set enrichment analysis. Subclass mapping analysis and Genomics of Drug Sensitivity in Cancer were applied for prediction of immunotherapy response and chemotherapy response, respectively. Results: A total of 77 differentially expressed EMTRGs were identified in the TCGA-COAD cohort, and they were significantly associated with functions and pathways related to cancer cell metastasis, proliferation, and apoptosis. We constructed EMTRGs prognostic signature with COMP, MYL9, PCOLCE2, SCG2, and TIMP1 as new COAD prognostic biomarkers. The high-risk group had a poorer prognosis with enhanced immune cell infiltration. The GSEA demonstrated that the high-risk group was involved in "ECM Receptor Interaction," "WNT Signaling Pathway" and "Colorectal Cancer." Furthermore, patients with high risk scores may respond to anti-CTLA4 therapy and may be more resistant to targeted therapy agents BI 2536 and ABT-888. Conclusion: Together, we developed a new EMTRGs prognostic signature that can be an independent prognostic factor for COAD. This study has guiding implications for individualized counseling and treatment of COAD patients.

A signature based on five immune-related genes to predict the survival and immune characteristics of neuroblastoma

BACKGROUND

MYCN amplification (MNA) has been proved to be related to poor prognosis in neuroblastoma (NBL), but the MYCN-related immune signatures and genes remain unclear.

METHODS

Enrichment analysis was used to identify the significant enrichment pathways of differentially expressed immune-related genes (DEIRGs). Weight gene coexpression network analysis (WGCNA) was applied to reveal the correlation between these DEIRGs and MYCN status. Univariate and multivariate Cox analyses were used to construct risk model. The relevant fractions of immune cells were evaluated by CIBERSORT and single-sample gene set enrichment analysis (ssGSEA).

RESULTS

Five genes, including CHGA, PTGER1, SHC3, PLXNC1, and TRIM55 were enrolled into the risk model. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve showed that our model performed well in predicting the outcomes of NBL (3-years AUC = 0.720, 5-year AUC = 0.775, 10-years AUC = 0.782), which has been validated in the GSE49711 dataset and the E-MTAB-8248 dataset. By comparing with the tumor immune dysfunction and exclusion (TIDE) and tumor inflammation signature (TIS), we further proved that our model is reliable. Univariate and multivariate Cox regression analyses indicated that the risk score, age, and MYCN can serve as independent prognostic factors in the E-MATB-8248. Functional enrichment analysis showed the DEIRGs were enriched in leukocyte adhesion-related signaling pathways. Gene set enrichment analysis (GSEA) revealed the significantly enriched pathways of the five MYCN-related DEIRGs. The risk score was negatively correlated with the immune checkpoint CD274 (PD-L1) but no significant difference with the TMB. We also confirmed the prognostic value of our model in predicting immunotherapeutics.

CONCLUSION

We constructed and verified a signature based on DEIRG that related to MNA and predicted the survival of NBL based on relevant immune signatures. These findings could provide help for predicting prognosis and developing immunotherapy in NBL.

A Novel Inflammatory Response-Related Gene Signature Predicts Immune Status and Prognosis of Breast Cancer

PURPOSE

Breast cancer is the most common type of cancer and the leading cause of cancer-related death in women worldwide. In this study, we aimed to construct an inflammatory response-related gene model for predicting the immune status and prognosis of breast cancer patients.

METHODS

We obtained the inflammatory response-related genes from the Molecular Signatures Database. Furthermore, we used univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression to construct an inflammatory response-related gene signature (IRGS) model based on dataset obtained from The Cancer Genome Atlas (TCGA). Patients were consequently categorized into high-risk and low-risk groups. Kaplan-Meier analysis was used to compare the overall survival (OS) of high-risk and low-risk groups. Following that, we validated the model using a dataset (GSE96058) acquired from Gene Expression Omnibus (GEO) database. Univariate and multivariate Cox analyses were used to determine the independent prognostic value of the IRGS in the TCGA and GSE96058 cohorts. A nomogram was constructed to predict the OS in the TCGA cohort. Further, we used Gene Set Enrichment Analysis (GSEA), CIBERSORT, and single-sample Gene Set Enrichment Analysis (ssGSEA) to evaluate the associations of IRGS with immune-associated pathways and immune infiltration. Finally, the relationship between the expression of the signature genes and drug sensitivity was conducted using Pearson correlation analysis.

RESULTS

We established an IRGS to stratify breast cancer patients into the low-risk and high-risk groups. In both the training and validation sets, patients in the high-risk group had significantly shorter OS than those in the low-risk group. The risk score was significantly correlated with the clinical characteristics and could be used as a tool to predict the prognosis of breast cancer. Moreover, we found that the IRGS risk score was an independent predictor of OS in breast cancer patients, and a nomogram model based on IRGS risk score and other clinical factors could effectively predict the prognosis of breast cancer patients. Furthermore, the IRGS risk score was correlated with immune characteristics and was inversely associated with the abundance of immune cell infiltration. Patients with a low IRGS risk score had higher expression levels of immune checkpoint genes, suggesting that IRGS can be used as a potential indicator for immunotherapy. Finally, we found that the expression levels of prognostic genes were significantly correlated with tumor cell sensitivity to chemotherapeutic drugs.

CONCLUSION

Overall, these findings suggest that the IRGS can be used to predict the prognosis and immune status of breast cancer patients and provide new therapeutic targets for the treatment of these patients.

Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts

Despite immune checkpoint blockade (ICB) therapy contributed to significant advances in cancer therapy, only a small percentage of patients with colorectal cancer (CRC) respond to it. Identification of these patients will facilitate ICB application in CRC. In this study, we integrated multiple CRC cohorts (2,078 samples) to construct tumor microenvironment (TME) subtypes using TME indices calculated by CIBERSORT and ESTIMATE algorithms. Furthermore, a surrogate quantitative indicator, a tumor microenvironment immune gene (TMEIG) score system, was established using the key immune genes between TME clusters 1 and 2. The subsequent analysis demonstrated that TME subtypes and the TMEIG score system correlated with clinical outcomes of patients in multiple CRC cohorts and exhibited distinct immune statuses. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that patients with low TMEIG scores were more likely to benefit from ICB therapy. A study on two ICB cohorts (GSE78220 and IMvigor210) also validated that patients with low TMEIG scores exhibited higher ICB response rates and better prognoses after ICB treatment. The biomarker evaluation module on the TIDE website revealed that the TMEIG score was a robust predictive biomarker. Moreover, differential expression analysis, immunohistochemistry, qPCR experiments, and gene set prioritization module on the TIDE website demonstrated that the five genes that constitute the TMEIG score system (SERPINE1, FABP4, SCG2, CALB2, and HOXC6) were closely associated with tumorigenesis, immune cells, and ICB response indices. Finally, TMEIG scores could accurately predict the prognosis and ICB response of patients with CRC. SERPINE1, FABP4, SCG2, CALB2, and HOXC6 might be potential targets related to ICB treatment. Furthermore, our study provided new insights into precision ICB therapy in CRC.

(Nano)platforms in bladder cancer therapy: Challenges and opportunities

Urological cancers are among the most common malignancies around the world. In particular, bladder cancer severely threatens human health due to its aggressive and heterogeneous nature. Various therapeutic modalities have been considered for the treatment of bladder cancer although its prognosis remains unfavorable. It is perceived that treatment of bladder cancer depends on an interdisciplinary approach combining biology and engineering. The nanotechnological approaches have been introduced in the treatment of various cancers, especially bladder cancer. The current review aims to emphasize and highlight possible applications of nanomedicine in eradication of bladder tumor. Nanoparticles can improve efficacy of drugs in bladder cancer therapy through elevating their bioavailability. The potential of genetic tools such as siRNA and miRNA in gene expression regulation can be boosted using nanostructures by facilitating their internalization and accumulation at tumor sites and cells. Nanoparticles can provide photodynamic and photothermal therapy for ROS overgeneration and hyperthermia, respectively, in the suppression of bladder cancer. Furthermore, remodeling of tumor microenvironment and infiltration of immune cells for the purpose of immunotherapy are achieved through cargo-loaded nanocarriers. Nanocarriers are mainly internalized in bladder tumor cells by endocytosis, and proper design of smart nanoparticles such as pH-, redox-, and light-responsive nanocarriers is of importance for targeted tumor therapy. Bladder cancer biomarkers can be detected using nanoparticles for timely diagnosis of patients. Based on their accumulation at the tumor site, they can be employed for tumor imaging. The clinical translation and challenges are also covered in current review.