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

ETV5 reduces androgen receptor expression and induces neural stem-like properties during neuroendocrine prostate cancer development

Neuroendocrine prostate cancer (NEPC), an aggressive subtype induced by hormone therapy, lacks effective treatments. This study explored the role of E26 transformation-specific variant 5 (ETV5) in NEPC development. Analysis of multiple prostate cancer datasets revealed that NEPC is characterized by significantly elevated ETV5 expression compared to other subtypes. ETV5 expression increased progressively under hormone therapy through epigenetic modifications. ETV5 induced neural stem-like features in prostate cancer cells and facilitated their differentiation into NEPC under hormone treatment conditions, both in vitro and in vivo. Our molecular mechanistic study identified PBX3 and TLL1 as target genes of ETV5 that contribute to ETV5 overexpression-induced castration resistance and stemness. Notably, obeticholic acid, identified as an ETV5 inhibitor in this study, exhibited promising efficacy in suppressing NEPC development. This study highlights ETV5 as a key transcription factor that facilitates NEPC development and underscores its potential as a therapeutic target for this aggressive cancer subtype.

Meta-analysis and systematic review of factors predicting conversion to radical nephrectomy following robotic-assisted partial nephrectomy in renal cancer patients

Evaluating the risk factors for the conversion from robotic-assisted partial nephrectomy (RAPN) to radical nephrectomy (RN). Through a comprehensive database search encompassing PubMed, Web of Science, Embase, and the Cochrane Library, we identified pertinent English-language research published by June 2024. We utilized the NOS scale for quality assessment. The aggregate effect was quantified via the odds ratio (OR), alongside a 95% confidence interval (CI). Sensitivity analyses were conducted using both fixed-effects and random-effects models to evaluate reliability. The meta-analytical process was facilitated by the Stata 18 software suite. Our meta-analysis encompassed a total of 8 retrospective studies and 3 prospective studies, totaling 4056 patients. We found that increasing patient age (OR: 1.04; 95% CI 1.00-1.08; P = 0.005), higher American Society of Anesthesiologists (ASA) scores (3 or above) (OR: 2.74; 95% CI 1.52-4.93; P = 0.001), elevated R.E.N.A.L. scores (7 or above) (OR: 2.49; 95% CI 1.57-3.95; P < 0.001), and the use of off-clamp RAPN (OR: 7.21; 95% CI 2.60-19.93; P < 0.001) significantly raised the odds of surgical conversion. On the other hand, male sex (OR: 1.04; 95% CI 0.67-1.62; P = 0.858), the side of the tumor (OR: 0.97; 95% CI 0.48-1.95; P = 0.936), tumor size (OR: 3.43; 95% CI 0.57-20.55; P = 0.177), body mass index (BMI) (OR: 1.03; 95% CI 0.96-1.11; P = 0.426), clinical stage (OR: 3.78; 95% CI 0.46-30.70; P = 0.214), and the use of single-port RAPN (OR: 0.54; 95% CI 0.16-1.78; P = 0.31) did not show a statistically significant link to an increased conversion risk. This meta-analysis elucidates the critical risk factors for the conversion from robotic-assisted partial nephrectomy to radical nephrectomy, providing significant guidance for preoperative risk assessment and clinical decision-making. However, our findings necessitate validation through studies with larger sample sizes.

Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy

BACKGROUND

Our previous study suggests that tumor CD8+ T cells and macrophages (defined as CD68+ cells) infiltration underwent dynamic and heterogeneous changes during concurrent chemoradiotherapy (CCRT) in cervical cancer patients, which correlated with their short-term tumor response. This study aims to develop a CT image-based radiomics signature for such dynamic changes.

METHODS

Thirty cervical squamous cell carcinoma patients, who were treated with CCRT followed by brachytherapy, were included in this study. Pre-therapeutic CT images were acquired. And tumor biopsies with immunohistochemistry at primary sites were performed at baseline (0 fraction (F)) and immediately after 10F. Radiomics features were extracted from the region of interest (ROI) of CT images using Matlab. The LASSO regression model with ten-fold cross-validation was utilized to select features and construct an immunomarker classifier and a radiomics signature. Their performance was evaluated by the area under the curve (AUC).

RESULTS

The changes of tumor-infiltrating CD8+T cells and macrophages after 10F radiotherapy as compared to those at baseline were used to generate the immunomarker classifier (AUC= 0.842, 95% CI:0.680-1.000). Additionally, a radiomics signature was developed using 4 key radiomics features to predict the immunomarker classifier (AUC=0.875, 95% CI:0.753-0.997). The patients stratified based on this signature exhibited significant differences in treatment response (p = 0.004).

CONCLUSION

The radiomics signature could be used as a potential predictor for the CCRT-induced dynamic alterations of CD8+ T cells and macrophages, which may provide a less invasive approach to appraise tumor immune status during CCRT in cervical cancer compared to tissue biopsy.

Necroptosis-related subtypes are associated with bronchiectasis in pulmonary non-tuberculous mycobacteria-infected patients: a perspective based on transcriptomic analysis

The aim of this study was to explore the potential mechanisms responsible for the different manifestations of bronchiectasis in patients with pulmonary non-tuberculous mycobacteria (pNTM) infection. We found that the necroptosis level increased significantly after NTM infection. Further, the 31 pNTM-infected patients were classified into two subtypes based on necroptosis-related genes (NRGs) by unsupervised cluster analysis. After that, we compared the differences in clinical parameters, immune cell infiltration, and gene expression between the two subtypes. We observed that the high-necroptosis subtype possessed higher CT scores for bronchiectasis extent (P = 0.008) and severity (P = 0.023). And, more neutrophil infiltration in the high-necroptosis subtype was demonstrated both by the CIBERSORT algorithm and by blood neutrophil count (P = 0.001). Next, 688 differentially expressed genes (DEGs) between two subtypes were identified. To explore the portion in DEGs that might contribute to bronchiectasis, we intersected the DEGs with two gene modules. These two gene modules were identified as the most associated with CT scores for bronchiectasis extent and severity by weighted gene co-expression network analysis (WGCNA). Ninety-three intersection genes were obtained. Finally, 7 hub genes including ACSL1, ANXA3, DYSF, HK3, SLC11A1, STX11, and TLR4 were further screened out by machine learning algorithms and protein-protein interaction network analysis. These results suggested that the differential levels of necroptosis in pNTM patients might lead to differential extent and severity of bronchiectasis on radiographic imaging. This process might be associated with neutrophil infiltration and the involvement of seven hub genes.

Predictive biomarkers of colon cancer immunotherapy: Present and future

Immunotherapy has revolutionized colon cancer treatment. Immune checkpoint inhibitors (ICIs) have shown clinical benefits for colon cancer patients, especially those with high microsatellite instability (MSI-H). In 2020, the US Food and Drug Administration (FDA)-approved ICI pembrolizumab as the first-line treatment for metastatic MSI-H colon cancer patients. Additionally, neoadjuvant immunotherapy has presented efficacy in treating early-stage colon cancer patients. Although MSI has been thought of as an effective predictive biomarker for colon cancer immunotherapy, only a small proportion of colon cancer patients were MSI-H, and certain colon cancer patients with MSI-H presented intrinsic or acquired resistance to immunotherapy. Thus, further search for predictive biomarkers to stratify patients is meaningful in colon cancer immunotherapy. Except for MSI, other biomarkers, such as PD-L1 expression level, tumor mutation burden (TMB), tumor-infiltrating lymphocytes (TILs), certain gut microbiota, ctDNA, and circulating immune cells were also proposed to be correlated with patient survival and ICI efficacy in some colon cancer clinical studies. Moreover, developing new diagnostic techniques helps identify accurate predictive biomarkers for colon cancer immunotherapy. In this review, we outline the reported predictive biomarkers in colon cancer immunotherapy and further discuss the prospects of technological changes for biomarker development in colon cancer immunotherapy.