Depletion of CPNE7 sensitizes colorectal cancer to 5-fluorouracil by downregulating ATG9B expression.

We aimed to explore the biological function of CPNE7 and determine the impact of CPNE7 on chemotherapy resistance in colorectal cancer (CRC) patients. According to the Gene Expression Profiling Interactive Analysis database and previously published data, CPNE7 was identified as a potential oncogene in CRC. RT-qPCR and Western blotting were performed to verify the expression of CPNE7. Chi-square test was used to evaluate the associations between CPNE7 and clinical features. Cell proliferation, colony formation, cell migration and invasion, cell cycle and apoptosis were assessed to determine the effects of CPNE7. Transcriptome sequencing was used to identify potential downstream regulatory genes, and gene set enrichment analysis was performed to investigate downstream pathways. The effect of CPNE7 on 5-fluorouracil chemosensitivity was verified by half maximal inhibitory concentration (IC50). Subcutaneous tumorigenesis assay was used to examine the role of CPNE7 in sensitivity of CRC to chemotherapy in vivo. Transmission electron microscopy was used to detect autophagosomes. CPNE7 was highly expressed in CRC tissues, and its expression was correlated with T stage and tumour site. Knockdown of CPNE7 inhibited the proliferation and colony formation of CRC cells and promoted apoptosis. Knockdown of CPNE7 suppressed the expression of ATG9B and enhanced the sensitivity of CRC cells to 5-fluorouracil in vitro and in vivo. Knockdown of CPNE7 reversed the induction of the autophagy pathway by rapamycin and reduced the number of autophagosomes. Depletion of CPNE7 attenuated the malignant proliferation of CRC cells and enhanced the chemosensitivity of CRC cells to 5-fluorouracil.

Identification of angiogenesis-related subtypes, the development of a prognosis model, and features of tumor microenvironment in colon cancer.

Angiogenesis is associated with tumor progression, prognosis, and treatment effect. However, the angiogenesis' underlying mechanisms in the tumor microenvironment (TME) still remain unclear. Understanding the dynamic interactions between angiogenesis and TME in colon adenocarcinoma (COAD) is necessary. We downloaded the transcriptome data and corresponding clinical data of colon cancer patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct angiogenesis-related molecular subtypes (subtype A and subtype B) and assessed the clinical features, prognosis, and infiltrating immune cells of patients in the two subtypes. According to the prognostic differential genes, we defined two different gene clusters to further explore the correlation between angiogenesis and tumor heterogeneity. Then, we construct the prognostic risk scoring model angiogenesis-related gene (ARG-score) including seven genes (ARMCX2, latent transforming growth factor ß binding protein 1, ADAM8, FABP4, CCL11, CXCL11, ITLN1) using Lasso-multivariate cox method. We analyzed the correlation between ARG-score and prognosis, clinicopathological features, TME, molecular feature, cancer stem cells (CSCs), and microsatellite instability (MSI) status. To assess the application value of ARG-score in clinical treatment, immunophenotype score was used to predict patients' immunotherapy response in colon cancer. We found the mutations of ARGs in TCGA-COAD dataset from genetic levels and discussed their expression patterns based on TCGA and GEO datasets. We observed important differences in clinicopathological features, prognosis, immune feature, molecular feature between the two molecular subgroups. Then, we established an ARG-score for predicting OS and validated its predictive capability. A high ARG-score characterized by higher transcription level of ARGs, suggested lower MSI-high (MSI-H), lower immune score, and worse clinical stage and survival outcome. Additionally, the ARG-score was remarkably related to the CSCs index and immunotherapy sensitivity. We found two new molecular subtypes and two gene clusters based on ARGs and established an ARG-score. Multilayered analysis revealed that ARGs were remarkably correlated to the heterogeneity of colon cancer patients and explained the process of tumorigenesis and progression better. The ARG-score can help us better assess patients' survival outcomes and provide guidance for individualized treatment.

Breast cancer prognosis and immunological characteristics are predicted using the m6A/m5C/m1A/m7G-related long noncoding RNA signature.

According to statistics, breast cancer (BC) has replaced lung cancer as the most common cancer in the world. Therefore, specific detection markers and therapeutic targets need to be explored as a way to improve the survival rate of BC patients. We first identified m6A/m5C/m1A/m7G-related long noncoding RNAs (MRlncRNAs) and developed a model of 16 MRlncRNAs. Kaplan-Meier survival analysis was applied to assess the prognostic power of the model, while univariate Cox analysis and multivariate Cox analysis were used to assess the prognostic value of the constructed model. Then, we constructed a nomogram to illustrate whether the predicted results were in good agreement with the actual outcomes. We tried to use the model to distinguish the difference in sensitivity to immunotherapy between the two groups and performed some analyses such as immune infiltration analysis, ssGSEA and IC50 prediction. To explore the novel anti-tumor drug response, we reclassified the patients into two clusters. Next, we assessed their response to clinical treatment by the R package pRRophetic, which is determined by the IC50 of each BC patient. We finally identified 11 MRlncRNAs and based on them, a risk model was constructed. In this model, we found good agreement between calibration plots and prognosis prediction. The AUC of ROC curves was 0.751, 0.734, and 0.769 for 1-year, 2-year, and 3-year overall survival (OS), respectively. The results showed that the IC50 was significantly different between the risk groups, suggesting that the risk groups can be used as a guide for systemic treatment. We regrouped patients into two clusters based on 11 MRlncRNAs expression. Next, we conducted immune scores for 2 clusters, which showed that cluster 1 had higher stromal scores, immune scores and higher estimated (microenvironment) scores, demonstrating that TME of cluster 1 was different from cluster 2. The results of this study support that MRlncRNAs can predict tumor prognosis and help differentiate patients with different sensitivities to immunotherapy as a basis for individualized treatment for BC patients.

A novel prognostic model related to oxidative stress for treatment prediction in lung adenocarcinoma.

Background: The prognostic model based on oxidative stress for lung adenocarcinoma (LUAD) remains unclear. Methods: The information of LUAD patients were acquired from TCGA dataset. We also collected two external datasets from GEO for verification. Oxidative stress-related genes (ORGs) were extracted from Genecards. We performed machine learning algorithms, including Univariate Cox regression, Random Survival Forest, and Least Absolute Shrinkage and Selection Operator (Lasso) analyses on the ORGs to build the OS-score and OS-signature. We drew the Kaplan-Meier and time-dependent receiver operating characteristic curve (ROC) to evaluate the efficacy of the OS-signature in predicting the prognosis of LUAD. We used GISTIC 2.0 and maftool algorithms to explore Genomic mutation of OS-signature. To analyze characteristic of tumor infiltrating immune cells, ESTIMATE, TIMER2.0, MCPcounter and ssGSEA algorithms were applied, thus evaluating the immunotherapeutic strategies. Chemotherapeutics sensitivity analysis was based on pRRophetic package. Finally, PCR assays was also used to detect the expression values of related genes in the OS-signature in cell lines. Results: Ten ORGs with prognostic value and the OS-signature containing three prognostic ORGs were identified. The significantly better prognosis of LUAD patients was observed in LUAD patients. The efficiency and accuracy of OS-signature in predicting prognosis for LUAD patients was confirmed by survival ROC curves and two external validation data sets. It was clearly observed that patients with high OS-scores had lower immunomodulators levels (with a few exceptions), stromal score, immune score, ESTIMATE score and infiltrating immune cell populations. On the contrary, patients with higher OS-scores were more likely to have higher tumor purity. PCR assays showed that, MRPL44 and CYCS were significantly higher expressed in LUAD cell lines, while CAT was significantly lower expressed. Conclusion: The novel oxidative stress-related model we identified could be used for prognosis and treatment prediction in lung adenocarcinoma.

The novel subclusters based on cancer-associated fibroblast for pancreatic adenocarcinoma.

Introduction: Pancreatic adenocarcinoma (PAAD) is a fatal disease characterized by promoting connective tissue proliferation in the stroma. Activated cancer-associated fibroblasts (CAFs) play a key role in fibrogenesis in PAAD. CAF-based tumor typing of PAAD has not been explored. Methods: We extracted single-cell sequence transcriptomic data from GSE154778 and CRA001160 datasets from Gene Expression Omnibus or Tumor Immune Single-cell Hub to collect CAFs in PAAD. On the basis of Seurat packages and new algorithms in machine learning, CAF-related subtypes and their top genes for PAAD were analyzed and visualized. We used CellChat package to perform cell-cell communication analysis. In addition, we carried out functional enrichment analysis based on clusterProfiler package. Finally, we explored the prognostic and immunotherapeutic value of these CAF-related subtypes for PAAD. Results: CAFs were divided into five new subclusters (CAF-C0, CAF-C1, CAF-C2, CAF-C3, and CAF-C4) based on their marker genes. The five CAF subclusters exhibited distinct signaling patterns, immune status, metabolism features, and enrichment pathways and validated in the pan-cancer datasets. In addition, we found that both CAF-C2 and CAF-C4 subgroups were negatively correlated with prognosis. With their top genes of each subclusters, the sub-CAF2 had significantly relations to immunotherapy response in the patients with pan-cancer and immunotherapy. Discussion: We explored the heterogeneity of five subclusters based on CAF in signaling patterns, immune status, metabolism features, enrichment pathways, and prognosis for PAAD.

Identification of m6A-related long non-coding RNAs for predicting prognosis and immune characterizations in gastric cancer.

Background: N6-methyladenosine (m6A) mRNA modification triggers malignant behavior in tumor cells, which promotes malignant progression and migration of gastric cancer (GC). Nevertheless, studies on the prognostic value of m6A-related long non-coding RNA (MRlncRNA) in GC remain quite restricted. The study aimed to develop a reasonable predictive model to explore the prognostic potential of MRlncRNAs in predicting the prognosis of GC patients and monitoring the efficacy of immunotherapy. Methods: Transcriptomic and clinical data for GC were derived from TCGA. Next, univariate Cox, LASSO and multivariate Cox regression analyses were next used to identify prognostic MRlncRNAs, calculate risk scores and build risk assessment models. The predictive power of the risk models was then validated by Kaplan-Meier analysis, ROC curves, DCA, C-index, and nomogram. We attempted to effectively differentiate between groups in terms of immune cell infiltration status, ICI-related genes, immunotherapy responses, and common anti-tumor drug sensitivity. Results: A risk model based on 11 MRlncRNAs was developed with an AUC of 0.850, and the sensitivity and specificity of this model in predicting survival probability is satisfactory. The Kaplan-Meier analysis revealed that the low-risk group in the model had a significantly higher survival rate, and the model was highly associated with survival status, clinical features, and clinical stage. Furthermore, the model was verified to be an independent prognostic risk factor, and the low-risk group in the model had a remarkable positive correlation with a variety of immune cell infiltrates. The expression levels of ICI-related genes differed significantly between the different groups. Lastly, immunotherapy responses and common anti-tumor drug sensitivity also differed significantly between different groups. Conclusion: The risk model on the basis of 11-MRlncRNAs can serve as independent predictors of GC prognosis and may be useful in developing personalized treatment strategies for patients.

Identification of Hypoxia-Related Subtypes, Establishment of Prognostic Models, and Characteristics of Tumor Microenvironment Infiltration in Colon Cancer.

Background: Immunotherapy is a treatment that can significantly improve the prognosis of patients with colon cancer, but the response to immunotherapy is different in patients with colon cancer because of the heterogeneity of colon carcinoma and the complex nature of the tumor microenvironment (TME). In the precision therapy mode, finding predictive biomarkers that can accurately identify immunotherapy-sensitive types of colon cancer is essential. Hypoxia plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism, and chemotherapy and immunotherapy resistance. Thus, understanding the mechanism of hypoxia-related genes (HRGs) in colon cancer progression and constructing hypoxia-related signatures will help enrich our treatment strategies and improve patient prognosis. Methods: We obtained the gene expression data and corresponding clinical information of 1,025 colon carcinoma patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct hypoxia subtypes (subtype A and subtype B) according to unsupervised clustering analysis and assessed the clinical parameters, prognosis, and TME cell-infiltrating characteristics of patients in the two subtypes. We identified 1,132 differentially expressed genes (DEGs) between the two hypoxia subtypes, and all patients were randomly divided into the training group (n = 513) and testing groups (n = 512). Following univariate Cox regression with DEGs, we construct the prognostic model (HRG-score) including six genes (S1PR3, ETV5, CD36, FOXC1, CXCL10, and MMP12) through the LASSO-multivariate cox method in the training group. We comprehensively evaluated the sensitivity and applicability of the HRG-score model from the training group and the testing group, respectively. We explored the correlation between HRG-score and clinical parameters, tumor microenvironment, cancer stem cells (CSCs), and MMR status. In order to evaluate the value of the risk model in clinical application, we further analyzed the sensitivity of chemotherapeutics and immunotherapy between the low-risk group and high-risk group and constructed a nomogram for improving the clinical application of the HRG-score. Result: Subtype A was significantly enriched in metabolism-related pathways, and subtype B was significantly enriched in immune activation and several tumor-associated pathways. The level of immune cell infiltration and immune checkpoint-related genes, stromal score, estimate score, and immune dysfunction and exclusion (TIDE) prediction score was significantly different in subtype A and subtype B. The level of immune checkpoint-related genes and TIDE score was significantly lower in subtype A than that in subtype B, indicating that subtype A might benefit from immune checkpoint inhibitors. Finally, an HRG-score signature for predicting prognosis was constructed through the training group, and the predictive capability was validated through the testing group. The survival analysis and correlation analysis of clinical parameters revealed that the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. There were also significant differences in immune status, mismatch repair status (MMR), and cancer stem cell index (CSC), between the two risk groups. The correlation analysis of risk scores with IC50 and IPS showed that patients in the low-risk group had a higher benefit from chemotherapy and immunotherapy than those in the high-risk group, and the external validation IMvigor210 demonstrated that patients with low risk were more sensitive to immunotherapy. Conclusion: We identified two novel molecular subgroups based on HRGs and constructed an HRG-score model consisting of six genes, which can help us to better understand the mechanisms of hypoxia-related genes in the progression of colon cancer and identify patients susceptible to chemotherapy or immunotherapy, so as to achieve precision therapy for colon cancer.

Astrocyte dysfunction in Parkinson's disease: from the perspectives of transmitted α-synuclein and genetic modulation.

Parkinson's disease (PD) is a common neurodegenerative disorder that primarily affects the elderly. While the etiology of PD is likely multifactorial with the involvement of genetic, environmental, aging and other factors, α-synuclein (α-syn) pathology is a pivotal mechanism underlying the development of PD. In recent years, astrocytes have attracted considerable attention in the field. Although astrocytes perform a variety of physiological functions in the brain, they are pivotal mediators of α-syn toxicity since they internalize α-syn released from damaged neurons, and this triggers an inflammatory response, protein degradation dysfunction, mitochondrial dysfunction and endoplasmic reticulum stress. Astrocytes are indispensable coordinators in the background of several genetic mutations, including PARK7, GBA1, LRRK2, ATP13A2, PINK1, PRKN and PLA2G6. As the most abundant glial cells in the brain, functional astrocytes can be replenished and even converted to functional neurons. In this review, we discuss astrocyte dysfunction in PD with an emphasis on α-syn toxicity and genetic modulation and conclude that astrocyte replenishment is a valuable therapeutic approach in PD.

miR-205 regulation of ICT1 has an oncogenic potential via promoting the migration and invasion of gastric cancer cells.

Immature colon carcinoma transcript-1 (ICT1) is a newly identified oncogene, which regulates mobility, apoptosis, cell cycle progression and proliferation of cancer cells. Nevertheless, the role of ICT1 and its clinical significance in gastric cancer (GC) is largely uncovered. Here, we found that ICT1 displayed higher expression in GC tissues compared to corresponding tumor-adjacent tissues. Further investigation confirmed ICT1 overexpression in GC cell lines. Clinical data disclosed that high ICT1 expression correlated with distant metastasis and advanced tumor-node-metastasis (TNM) stage. The Cancer Genome Atlas (TCGA) data further demonstrated that GC tissues with metastasis showed a significant higher level of ICT1 compared to those without metastasis. Furthermore, ICT1 overexpression notably predicted poor prognosis of GC patients. Functionally, we demonstrated that ICT1 knockdown suppressed invasion and migration of MGC-803 and BGC-823 cells in vitro. ICT1 overexpression promoted the mobility of SGC-7901 cells. Mechanistically, microRNA-205 (miR-205) was recognized as a direct down-regulator and inversely modulated ICT1 abundance in GC cells. miR-205 expression was down-regulated and negatively associated with ICT1 level in GC tissues. Underexpression of miR-205 indicated an obvious shorter survival of GC patients. miR-205 overexpression inhibited migration and invasion of MGC-803 cells, while these inhibitory effects were reversed by ICT1 restoration. Taken together, we have the earliest evidence that miR-205 regulation of ICT1 functions as an oncogene and prognostic biomarker in GC.

Spectral tuning of a locally bent microfiber taper interferometer with a nanosized liquid crystal overlay.

In this paper, the tuning characteristics of locally bent microfiber taper covered with a nanosized high-refractive-index liquid crystal (LC) layer under different temperatures and electric field intensities have been theoretically analyzed and experimentally investigated. A locally bent microfiber taper interferometer with a waist diameter of ∼3.72 µm is fabricated by using the flame brushing technique, followed by bending the transition region of the taper to form a modal interferometer and later by placing a ∼200 nm LC layer over the uniform taper waist region. Experimental results indicate that a high-efficiency thermal or electric tuning of an LC-coated locally bent microfiber taper interferometer could be achieved. This suggests a potential application of this device as tunable all-fiber photonic devices, such as filters, modulators, and sensing elements.