Identification of a prognostic model based on costimulatory molecule-related subtypes and characterization of tumor microenvironment infiltration in acute myeloid leukemia

Comprehensive analysis of basement membrane-related gene based on single-cell and bulk RNA sequencing data to predict prognosis and evaluate immune characteristics in colorectal cancer

AIMS

Basement membrane-related genes (BMs) participate in regulating cell polarity, invasion, metastasis, and survival across different tumor types. Nevertheless, the specific functions of BMs in colorectal cancer (CRC) remain uncertain.

METHODS

To investigate the clinical relevance of BMs in CRC, we retrieved both gene expression and clinical data from The Cancer Genome Atlas (TCGA) datasets for subsequent analysis. The Kaplan-Meier (K-M) survival curve was employed to evaluate prognosis in high- and low-risk groups. Furthermore, additional analyses, including nomogram construction, functional enrichment, examination of the tumor immune microenvironment, prediction of small-molecule drugs, and more, were conducted to delve into the significance of BM-related signatures in CRC. Single-cell data from seven CRC patients were obtained from the TISCH2 database, and expression validation and cell source exploration of BM-related signatures were performed. Lastly, the expression and function of TIMP1, a key gene in BMs that may play a role in the progression of CRC, was validated in vitro through a series of basic experiments.

RESULTS

We constructed a seven BMs-based model to categorize CRC patients into high-risk and low-risk groups. K-M survival analysis indicated a poorer prognosis for high-risk CRC patients. Cox regression analysis further identified the risk score as an independent prognostic factor for CRC patients. The nomogram model exhibited superior discrimination and calibration abilities of CRC patients. Based on the results from GO/KEGG and GSEA, genes in the high-risk subgroup were implicated in immune-related pathways and exhibited a positive correlation with immune checkpoints. In single-cell data, we found that TIMP1 is highly expressed in many cells, especially in malignant tumor cells. We also observed up-regulation of TIMP1 in CRC cell lines, promoting cancer invasion and migration in vitro.

CONCLUSIONS

Our study has discovered a novel prognostic index derived from BM-related genes in CRC patients. Specifically, the new model enables patient stratification, improving the selection of individuals likely to benefit from immunotherapy.

Machine Learning-Based Integrated Analysis of PANoptosis Patterns in Acute Myeloid Leukemia Reveals a Signature Predicting Survival and Immunotherapy

Objective

We conducted a meticulous bioinformatics analysis leveraging expression data of 226 PANRGs obtained from previous studies, as well as clinical data from AML patients derived from the HOVON database.

Methods

Through meticulous data analysis and manipulation, we were able to categorize AML cases into two distinct PANRG clusters and subsequently identify differentially expressed genes (PRDEGs) with prognostic significance. Furthermore, we organized the patient data into two corresponding gene clusters, allowing us to investigate the intricate relationship between the risk score, patient prognosis, and the immune landscape.

Results

Our findings disclosed significant associations between the identified PANRGs, gene clusters, patient survival, immune system, and cancer-related biological processes and pathways. Importantly, we successfully constructed a prognostic signature comprising nineteen genes, enabling the stratification of patients into high-risk and low-risk groups based on individually calculated risk scores. Furthermore, we developed a robust and practical nomogram model, integrating the risk score and other pertinent clinical features, to facilitate accurate patient survival prediction. Our comprehensive analysis demonstrated that the high-risk group exhibited notably worse prognosis, with the risk score proving to be significantly correlated with infiltration of most immune cells. The qRT-PCR results revealed significant differential expression patterns of LGR5 and VSIG4 in normal and human leukemia cell lines (HL-60 and MV-4-11).

Conclusions

Our findings underscore the potential utility of PANoptosis-based molecular clustering and prognostic signatures as predictive tools for assessing patient survival in AML.

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.

Prognostic Model for Clear-cell Renal Cell Carcinoma Based on Natural Killer Cell-related Genes

BACKGROUND

Natural killer (NK) cells are a key factor affecting progression and immune surveillance of clear-cell renal cell carcinoma (ccRCC). This study sought to construct a natural killer cell-related prognostic signature (NKRPS) to predict the outcome of ccRCC patients and to furnish guidance for finding appropriate treatment strategies.

METHODS

From the TCGA and ArrayExpress databases, transcriptomic profiles and relevant clinical information of ccRCC patients were downloaded for the TCGA cohort (n = 515) and the E-MTAB-1980 cohort (n = 101). With the univariate Cox analysis and LASSO-Cox regression algorithm, a NKRPS was built to evaluate patients' prognosis. Receiver operating characteristic (ROC) curves and calibration curves were drawn to estimate the predictive power of the prognostic model. Then, tumor microenvironment (TME), tumor mutational burden (TMB), sensitization to immune checkpoint inhibitors (ICIs) therapy and targeted drug treatment were analyzed in ccRCC patients.

RESULTS

Nine genes (BID, CCL7, CSF2, IL23A, KNSTRN, RHBDD3, PIK3R3, RNF19B and VAV3) were identified to construct a NKRPS. High-risk group displayed undesirable survival compared to low-risk group (P < .05). Moreover, the area under the curve (AUC) of ROC at 1-, 3- and 5-year were 0.766, 0.755, and 0.757, respectively. High-risk group was characterized by superior immune infiltration, higher TMB, and higher expression of 5 ICI-related genes. Additionally, this model enabled to predict the sensitivity of patients to chemotherapy drugs.

CONCLUSION

NKRPS had a strong predictive power on prognosis of ccRCC patients, which may facilitate individualized treatment and medical decision making.