In recent years, development in high‐throughput sequencing technologies has experienced an increasing application of statistics, pattern recognition, and machine learning in bioinformatics analyses. SangeBox platform to meet different scientific demands. The new version of Sangs is a widely used tool among many researchers, which encourages us to continuously improve the plerBox 2 (http://vip.sangerbox.com) and extends and optimizes the functions of interactive graphics and analysis of clinical bioinformatics data. We introduced novel analytical tools such as random forests and support vector machines, as well as corresponding plotting functions. At the same time, we also optimized the performance of the platform and fixed known problems to allow users to perform data analyses more quickly and efficiently. SangerBox 2 improved the speed of analysis, reduced resource required for computer performance, and provided more analysis methods, greatly promoting the research efficiency.

Tryptophan (Trp) metabolism involves three primary pathways: the kynurenine (Kyn) pathway (KP), the 5-hydroxytryptamine (serotonin, 5-HT) pathway, and the indole pathway. Under normal physiological conditions, Trp metabolism plays crucial roles in regulating inflammation, immunity, and neuronal function. Key rate-limiting enzymes such as indoleamine-2,3-dioxygenase (IDO), Trp-2,3-dioxygenase (TDO), and kynurenine monooxygenase (KMO) drive these metabolic processes. Imbalances in Trp metabolism are linked to various cancers and often correlate with poor prognosis and adverse clinical characteristics. Dysregulated Trp metabolism fosters tumor growth and immune evasion primarily by creating an immunosuppressive tumor microenvironment (TME). Activation of the KP results in the production of immunosuppressive metabolites like Kyn, which modulate immune responses and promote oncogenesis mainly through interaction with the aryl hydrocarbon receptor (AHR). Targeting Trp metabolism therapeutically has shown significant potential, especially with the development of small-molecule inhibitors for IDO1, TDO, and other key enzymes. These inhibitors disrupt the immunosuppressive signals within the TME, potentially restoring effective anti-tumor immune responses. Recently, IDO1 inhibitors have been tested in clinical trials, showing the potential to enhance the effects of existing cancer therapies. However, mixed results in later-stage trials underscore the need for a deeper understanding of Trp metabolism and its complex role in cancer. Recent advancements have also explored combining Trp metabolism inhibitors with other treatments, such as immune checkpoint inhibitors, chemotherapy, and radiotherapy, to enhance therapeutic efficacy and overcome resistance mechanisms. This review summarizes the current understanding of Trp metabolism and signaling in cancer, detailing the oncogenic mechanisms and clinical significance of dysregulated Trp metabolism. Additionally, it provides insights into the challenges in developing Trp-targeted therapies and future research directions aimed at optimizing these therapeutic strategies and improving patient outcomes.

Drug resistance in cancer cells significantly diminishes treatment efficacy, leading to recurrence and metastasis. A critical factor contributing to this resistance is the epigenetic alteration of gene expression via RNA modifications, such as N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), 7-methylguanosine (m7G), pseudouridine (Ψ), and adenosine-to-inosine (A-to-I) editing. These modifications are pivotal in regulating RNA splicing, translation, transport, degradation, and stability. Governed by “writers,” “readers,” and “erasers,” RNA modifications impact numerous biological processes and cancer progression, including cell proliferation, stemness, autophagy, invasion, and apoptosis. Aberrant RNA modifications can lead to drug resistance and adverse outcomes in various cancers. Thus, targeting RNA modification regulators offers a promising strategy for overcoming drug resistance and enhancing treatment efficacy. This review consolidates recent research on the role of prevalent RNA modifications in cancer drug resistance, with a focus on m6A, m1A, m5C, m7G, Ψ, and A-to-I editing. Additionally, it examines the regulatory mechanisms of RNA modifications linked to drug resistance in cancer and underscores the existing limitations in this field.

Background: Tumor-associated macrophages (TAMs), the most abundant non-tumor cell population in the glioma microenvironment, play a crucial role in immune evasion and immunotherapy resistance of glioblastoma (GBM). However, the regulatory mechanism of the immunosuppressive TME of GBM remains unclear.

Gliomas are the most aggressive type of malignant brain tumors. Recent studies have demonstrated that the existence of glioma stem cells (GSCs) is critical for glioma recurrence, metastasis, and chemo- or radio-therapy resistance. Temozolomide (TMZ) has been used as an initial therapy for gliomas. However, the overall survival time is still limiting due to the lack of effective targets and treatment options. Therefore, identifying novel biomarkers for gliomas, especially for GSCs, is important to improve the clinical outcome in the future. In this study, we identify a human-specific long non-coding RNA (lncRNA, ENSG00000250377), termed GSCAR (glioma stem cell associated lncRNA), which is highly expressed in glioma cancerous tissues and cell lines. We reveal that GSCAR positively correlates with tumor grade. Glioma patients with GSCAR high expression exhibit shortened overall survival time, compared to patients with GSCAR low expression. Furthermore, we show that GSCAR knockdown by shRNAs or antisense oligonucleotide (ASO) reduces tumor cell proliferation, migration and xenograft tumor formation abilities. Mechanistic study shows that GSCAR acts as a ceRNA (competing endogenous RNA) for miR-6760-5p to promote the expression of oncogene SRSF1 (serine and arginine rich splicing factor 1). In addition, GSCAR mediates the protein complex formation between DHX9 (DExH-Box helicase 9) and IGF2BP2 (insulin-like growth factor 2 mRNA-binding protein 2), leading to the stabilization of SOX2 (sex-determining region Y-box 2) mRNA and then the transcriptional activation of GSCAR. Depleting GSCAR reduces SOX2 expression and GSC self-renewal ability, but promotes tumor cell responses to TMZ. These findings uncover that GSCAR/miR-6760-5p/SRSF1 axis and GSCAR/DHX9-IGF2BP2/SOX2 positive feedback loop are critical for glioma progression, which could be used as prognostic biomarkers and therapeutic targets in the future.

Background: To develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes. Methods: A total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test. Results: Radscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes. Conclusion: Our findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.