Immune checkpoint inhibitors have shaped the landscape of cancer treatment. However, many patients either do not respond or suffer from later progression. Numerous proteins can control immune system activity, including multiple tumor necrosis factor (TNF) superfamily (TNFSF) and TNF receptor superfamily (TNFRSF) members; these proteins play a complex role in regulating cell survival and death, cellular differentiation, and immune system activity. Notably, TNFSF/TNFRSF molecules may display either pro-tumoral or anti-tumoral activity, or even both, depending on tumor type. Therefore, TNF is a prototype of an enigmatic two-faced mediator in oncogenesis. To date, multiple anti-TNF agents have been approved and/or included in guidelines for treating autoimmune disorders and immune-related toxicities after immune checkpoint blockade for cancer. A confirmed role for the TNFSF/TNFRSF members in treating cancer has proven more elusive. In this review, we highlight the cancer-relevant TNFSF/TNFRSF family members, focusing on the death domain-containing and co-stimulation members and their signaling pathways, as well as their complicated role in the life and death of cancer cells. Graphical Abstract

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of different malignancies. However, their efficacy in advanced adrenocortical carcinoma (ACC) remains uncertain. Thus, we conducted a systematic review and meta-analysis to summarize the efficacy and tolerability of ICIs in patients with advanced ACC. We searched PubMed, Scopus, and CENTRAL for studies that used ICIs in ACC. Studies with more than five patients were included in the meta-analysis of the objective response rate (ORR), disease control rate (DCR), overall survival (OS), progression-free survival (PFS), and grade 3/4 adverse events. Twenty studies with 23 treatment arms and 250 patients were included. Single-agent anti-PD1 or anti-PD-L1 treatment was utilized in 13 treatment arms, whereas an anti-PD1 or anti-PD-L1 and anti-CTLA4 combination was used in 4 treatment arms. Other anti-PD1- or anti-PD-L1-based combinations were used in five treatment arms. The ORR was 14% (95% CI = 10–19%, I² = 0%), and the DCR was 43% (95% CI = 37–50%, I² = 13%). The combination anti-PD1- or anti-PD-L1-based treatment strategies did not correlate with higher responses compared with monotherapy. The median OS was 13.9 months (95% CI = 7.85–23.05), and the median PFS was 2.8 months (95% CI = 1.8–5.4). ICIs have a modest efficacy in advanced ACC but a good OS. Further studies are needed to investigate predictive biomarkers for ICI response and to compare ICI-based strategies with the current standard of care.

This study explored the role of radioiodine therapy (RAI) in low-risk thyroid cancer patients and examined the disease-specific survival (DSS) rates in a large cohort of differentiated thyroid cancer patients (DTC). We obtained patient data from SEER database. Patients who underwent total thyroidectomy were included and categorized into three groups based on histology: classical papillary thyroid carcinoma (C-PTC), follicular type variant carcinoma (FV-PTC), and follicular thyroid cancer (FTC). Patients with distant metastasis, tumor size ≥ 200 mm, chemotherapy, or any type of radiation other than RAI were also excluded. A nomogram was developed and tested for discrimination and calibration. In total, 96,532 thyroid cancer cases were examined, including 59,460 C-PTC, 31,583 FV-PTC, and 5,489 FTC cases. Age > 65 years and male sex were correlated with lower survival rates across the subtypes. In addition, extrathyroidal extension had a worse survival effect in patients with FTC. DSS rates were compared between patients who received RAI and those who did not, with a 3% difference in C-PTC (94% vs. 91%, p < 0.001), 2% in FV-PTC (92% vs. 90%, p < 0.001), and 1% in FTC (89% vs. 88%, p = 0.084) at 15 years. The nomograms for long-term DSS showed high discriminatory abilities with C-indices of 0.815, 0.805, and 0.781 for C-PTC, FV-PTC, and FTC, respectively. The developed nomogram can be used in the treatment plan for patients with DTC. Our study emphasizes the prognostic factors for DTC and highlights the need for personalized treatment plans based on individual risk profiles.

Gastrointestinal cancers are highly aggressive malignancies with significant mortality rates. Recent research emphasizes the critical role of the tumor microenvironment (TME) in these cancers, which includes cancer-associated fibroblasts (CAFs), a key component of the TME that have diverse origins, including fibroblasts, mesenchymal stem cells, and endothelial cells. Several markers, such as α-SMA and FAP, have been identified to label CAFs, and some specific markers may serve as potential therapeutic targets. In this review article, we summarize the literature on the multifaceted role of CAFs in tumor progression, including their effects on angiogenesis, immune suppression, invasion, and metastasis. In addition, we highlight the use of single-cell transcriptomics to understand CAF heterogeneity and their interactions within the TME. Moreover, we discuss the dynamic interplay between CAFs and the immune system, which contributes to immunosuppression in the TME, and the potential for CAF-targeted therapies and combination approaches with immunotherapy to improve cancer treatment outcomes.

Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.