• CRC
• LAMPs
• autophagy
• drug resistance
• lysosomes

• Humans
• Colorectal Neoplasms/drug therapy
• Colorectal Neoplasms/metabolism
• Colorectal Neoplasms/pathology
• Autophagy/drug effects
• Drug Resistance, Neoplasm
• Lysosomes/metabolism
• Lysosomes/drug effects
• Animals
• Antineoplastic Agents/pharmacology
• Antineoplastic Agents/therapeutic use

• НО-16/2022 Medical University of Plovdiv
• КП-06-Н-63/8 from 13.12.2022 Bulgarian National Science Fund
• BG-RRP-2.004-0007-C01 European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria

• cancer microenvironment
• tumour biomarkers
• lymphatic system

• Humans
• Adenocarcinoma
• Colonic Neoplasms
• Single-Cell Gene Expression Analysis
• Tumor Microenvironment
• Prognosis

• Humans
• Antigens
• Colonic Neoplasms/genetics
• Colonic Neoplasms/surgery
• DNA Mismatch Repair/genetics
• Intermediate Filament Proteins
• Microsatellite Instability
• Mutation
• Neoplasm Staging
• Prognosis
• Retrospective Studies
• Ki-67 Antigen/genetics

• 2021-RC310-009 Special Funds for the Basic Research and Development Program in the Central Non-profit Research Institutes of China

Regulatory T cells (Tregs) and natural killer (NK) cells play an essential role in the development of bladder urothelial carcinoma (BUC).

To construct a prognosis-related model to judge the prognosis of patients with bladder cancer, meanwhile, predict the sensitivity of patients to chemotherapy and immunotherapy.

Bladder cancer information data was obtained from The Cancer Genome Atlas and GSE32894. The CIBERSORT was used to calculate the immune score of each sample. Weighted gene co-expression network analysis was used to find genes that will have the same or similar expression patterns. Subsequently, multivariate cox regression and lasso regression was used to further screen prognosis-related genes. The prrophetic package was used to predict phenotype from gene expression data, drug sensitivity of external cell line and predict clinical data.

The stage and risk scores are independent prognostic factors in patients with BUC. Mutations in FGFR3 lead to an increase in Tregs percolation and affect the prognosis of the tumor, and additionally, EMP1, TCHH and CNTNAP3B in the model are mainly positively correlated with the expression of immune checkpoints, while CMTM8, SORT1 and IQSEC1 are negatively correlated with immune checkpoints and the high-risk group had higher sensitivity to chemotherapy drugs.

Prognosis-related models of bladder tumor patients, based on Treg and NK cell percolation in tumor tissue. In addition to judging the prognosis of patients with bladder cancer, it can also predict the sensitivity of patients to chemotherapy and immunotherapy. At the same time, patients were divided into high and low risk groups based on this model, and differences in genetic mutations were found between the high and low risk groups.

• Natural killer cells
• Tregs
• Bladder cancer
• Weighted gene coexpression network analysis
• Bladder cancer treatment
• Immunotherapy
• Computational molecular biology

• Alzheimer’s disease
• Amyloid-β
• Hyperexcitation
• LAMP5
• Neuronal network
• Tau

• Alzheimer Disease/pathology
• Amyloid beta-Peptides/physiology
• Animals
• Disease Models, Animal
• Interneurons/pathology
• Lysosomal Membrane Proteins/metabolism
• Mice
• Mice, Transgenic
• Neurons/pathology
• tau Proteins/genetics

• Centre of Excellence in Cognition and its Disorders, Australian Research Council
• National Health and Medical Research Council
• Macquarie University

Pyroptosis is a novel type of programmed cell death, initiated by inflammasome. Pyroptosis inhibits the development and metastasis of colon cancer and is associated with patients’ prognosis. However, how the pyroptosis-related genes predict the survival of patients is still unclear. In the study, colon adenocarcinoma (COAD) patients were divided into two groups according to the expression of pyroptosis-related regulators through consensus clustering. DEGs between two clusters were analyzed by using COX and Lasso regression. Then, regression coefficients in Lasso were used to calculate the risk score for every patient. Patients were classified into two types: low- and high-risk group according to their risk score. The difference of immune microenvironment infiltration and clinicopathological characteristics between subgroups was performed. Moreover, the nomogram model was built on the bases of risk model and clinicopathological factors. The TCGA-COAD cohort and GEO cohort were used as training and validating set respectively. 398 COAD patients in TCGA training set were identified as two regulation patterns via unsupervised clustering method. Patients in cluster 2 showed better prognosis (P = 0.002). Through differentiated expression analysis, COX and Lasso regression, a 5-gene prognostic risk model was constructed. This risk model was significantly associated with OS (HR: 2.088, 95% CI: 1.183–3.688, P = 0.011), validated in GEO set (HR:1.344, 95%CI: 1.061–1.704, P = 0.014), and patients with low risk had better prognosis (P < 0.001 in TCGA; P = 0.038 in GEO). Through ROC analysis, it can be found that this model presented better predictive accuracy for long-term survival. Clinical analyses demonstrated that high-risk group had more advanced N stage, higher risk of metastasis and later pathological stage. Immune-related analysis illustrated that low-risk group had more immune cell infiltration and more activated immune pathways. The pyroptosis-related risk model can be predictive for the survival of COAD patients. That patients with higher risk had poorer prognosis was associated with more advanced tumor stage and higher risk of metastasis, and resulted from highly activated pro-tumor pathways and inhibited immune system and poorer integrity of intestinal epithelial. This study proved the relationship between pyroptosis and immune, which offered basis for future studies.

Necroptosis, is intimately linked to tumor development and prognosis and has been considered as a target for anticancer therapy. However, the role of necroptosis-related genes (NRGs) in colon cancer is unclear.

In the present study, we screened 76 NRGs from previous studies and described the landscape of transcriptomic and genetic variation of NRGs in colon cancer (CC) patient samples. Molecular subtypes of necroptosis in colon cancer were identified by clustering analysis, and these molecular subtypes were linked to patient prognosis and TME cell infiltration characteristics. Then, the NRS-score for predicting overall survival (OS) was built based on the TCGA database and validated in the GSE39582 cohort for its predictive power in CC patients. Besides, the ESTIMATE and CIBERSORT algorithms were applied to explore the relationship between NRS-score and tumor immune microenvironment.

We identified two molecular subtypes associated with necroptosis in CC, which have diverse prognosis and immune microenvironment characteristics. Based on the differentially expressed genes between the two molecular subtypes, we further developed a necroptosis risk score signature, referred to as NRS-score. High NRS-score was associated with poor prognosis in CC through immunosuppressive microenvironment and immune escape mechanisms. The nomogram based on NRS-score showed excellent ability to predict prognosis. In addition, NRS-score presented a positive correlation with tumor mutational burden (TMB) and immune checkpoint blockade (ICB) expression and was closely correlated with multiple anticancer agent susceptibility.

This work revealed a close relationship between necroptosis and the prognosis and immune microenvironment of colon cancer. The NRS-score based on the 8-gene signature may be used to predict the sensitivity of immunotherapy and chemotherapy in colon cancer patients, and provides a foundation for future studies targeting necroptosis and its immune microenvironment.

Osteosarcoma is the most common malignant bone tumor in adolescents and young adults, and identifying biomarkers for prognosis and therapy is necessary. Bone morphogenetic protein receptor 2 (BMPR2) is involved in various cellular functions, including cell adhesion, proliferation and invasion, inflammation, apoptosis and metastatic spread. However, the correlation between BMPR2 expression levels and prognosis and tumor-infiltrating immune cells in osteosarcoma is not well understood. In the present study, the expression level of BMPR2 was investigated using the Oncomine and R2 databases. The association between the expression level of BMPR2 and the clinical prognosis of patients with cancer was analyzed using the R2 database. The relationship between the expression level of BMPR2 and immune cell infiltration in the stroma of osteosarcoma was assessed using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT. The correlations between BMPR2 expression level and infiltrated immune cell gene marker sets in osteosarcoma were validated in the TIMER and R2 databases. Analysis of a cohort of patients with osteosarcoma revealed that BMPR2 expression was significantly higher in osteosarcoma compared with in normal tissue and was correlated with poor prognosis. M0 macrophages, M2 macrophages, resting mast, γ δ T and CD8+ T cells were the top five immune cells with the highest degrees of infiltration in osteosarcoma. In addition, BMPR2 expression level showed a significant negative correlation with the gene markers of CD8+ T cells, monocytes and M2 macrophages. Low levels of infiltrating CD8+ T cells, monocytes or M2 macrophages in osteosarcoma was significantly associated with poor survival. These data suggested that CD8+ T cells, monocytes and M2 macrophages play significant roles in the establishment of the immune microenvironment of osteosarcoma. High BMPR2 expression was associated with poor prognosis and low infiltration of CD8+ T cells, monocytes and M2 macrophages in osteosarcoma. Hence, BMPR2 can be considered a biomarker of the immune infiltration, metastasis and prognosis of osteosarcoma.

• BMPR2
• immune infiltration
• metastasis
• osteosarcoma
• prognosis