Recent advances in oncological research have highlighted the potential of naturally derived compounds in cancer prevention and treatment. Notably, sulforaphane (SFN), an isothiocyanate derived from cruciferous vegetables including broccoli and cabbage, has exhibited potent chemosensitizing capabilities across diverse cancer types of bone, brain, breast, lung, skin, etc. Chemosensitization refers to the enhancement of cancer cell sensitivity to chemotherapy agents, counteracting the chemoresistance often developed by tumor cells. Mechanistically, SFN orchestrates this sensitization by modulating an array of cellular signaling pathways (e.g., Akt/mTOR, NF-κB, Wnt/β-catenin), and regulating the expression and activity of pivotal genes, proteins, and enzymes (e.g., p53, p21, survivin, Bcl-2, caspases). When combined with conventional chemotherapeutic agents, SFN synergistically inhibits cancer cell proliferation, invasion, migration, and metastasis while potentiating drug-induced apoptosis. This positions SFN as a potential adjunct in cancer therapy to augment the efficacy of standard treatments. Ongoing preclinical and clinical investigations aim to further delineate the therapeutic potential of SFN in oncology. This review illuminates the multifaceted role of this phytochemical, emphasizing its potential to enhance the therapeutic efficacy of anti-cancer agents, suggesting its prospective contributions to cancer chemosensitization and management.

Background: Lung cancer has been a prominent research focus in recent years due to its role in cancer-related fatalities globally, with lung adenocarcinoma (LUAD) being the most prevalent histological form. Nonetheless, no signature of lactate metabolism-related long non-coding RNAs (LMR-lncRNAs) has been developed for patients with LUAD. Accordingly, we aimed to develop a unique LMR-lncRNA signature to determine the prognosis of patients with LUAD.Method: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to derive the lncRNA expression patterns. Identification of LMR-lncRNAs was accomplished by analyzing the co-expression patterns between lncRNAs and LMR genes. Subsequently, the association between lncRNA levels and survival outcomes was determined to develop an effective signature. In the TCGA cohort, Cox regression was enlisted to build an innovative signature consisting of three LMR-lncRNAs, which was validated in the GEO validation cohort. GSEA and immune infiltration analysis were conducted to investigate the functional annotation of the signature and the function of each type of immune cell.Results: Fourteen differentially expressed LMR-lncRNAs were strongly correlated with the prognosis of patients with LUAD and collectively formed a new LMR-lncRNA signature. The patients could be categorized into two cohorts based on their LMR-lncRNA signatures: a low-risk and high-risk group. The overall survival of patients with LUAD in the high-risk group was considerably lower than those in the low-risk group. Using Cox regression, this signature was shown to have substantial potential as an independent prognostic factor, which was further confirmed in the GEO cohort. Moreover, the signature could anticipate survival across different groups based on stage, age, and gender, among other variables. This signature also correlated with immune cell infiltration (including B cells, neutrophils, CD4⁺ T cells, CD8⁺ T cells, etc.) as well as the immune checkpoint blockade target CTLA-4.Conclusion: We developed and verified a new LMR-lncRNA signature useful for anticipating the survival of patients with LUAD. This signature could give potentially critical insight for immunotherapy interventions in patients with LUAD.

Microbial communities are not simply remnants of the past but dynamic entities that continuously evolve under the selective pressures of nature, reflecting the intricate and adaptive processes of evolution. The microbiota residing in the various regions of the human body has numerous roles in different physiological processes such as nutrition, metabolism, immune regulation, etc. In the zeal of achieving empirical insights into the ambit of the gut microbiome, the research over the years led to the revelation of reciprocal interaction between the gut microbiome and the cognitive functioning of the human body. Dysbiosis in the gut microbial composition disturbs the homeostatic cognitive functioning of the human body. This dysbiosis has been associated with various chronic diseases, including brain cancer, such as glioma, glioblastoma, etc. This review explores the mechanistic role of dysbiosis-mediated progression of brain cancers and their subtypes. Moreover, it demonstrates the regulatory role of microbial metabolites produced by the gut microbiota, such as short-chain fatty acids, amino acids, lipids, etc., in the tumour progression. Further, we also provide valuable insights into the microbiota mediating the efficiency of therapeutic regimens, thereby leveraging gut microbiota as potential biomarkers and targets for improved treatment outcomes.

Nanoparticles (NPs) have emerged as highly efficient drug delivery vehicles. NPs are characterised by their ability to safeguard drugs, enhance stability, prolong durability, and facilitate targeted tissue delivery. Moreover, NPs can be customised to either stimulate or suppress immune responses while evading immune detection. An increasing body of research demonstrated the therapeutic advantages of delivering various substances via surface-engineered NPs particularly in targeting immune cells. A broad spectrum of nanomaterials has been recognised for their superior ability to elicit immune responses, thereby enhancing disease prevention and therapeutic outcomes in vivo. In this comprehensive review, we discuss the impact of bioresponsive NPs on immunostimulation, immunomodulation, and immunosuppression. Particular focus is placed on how the physiochemical properties of these NPs influence the interaction between immune cells and host tissues, thereby achieving the desired immune regulation to combat various human diseases while reducing immunotoxicity. Additionally, we examined immune-inspired and immune-mediated strategies for the targeted delivery of NPs to specific sites within the body. Finally, we highlight the importance of understanding and focusing on immune cell trafficking mechanisms, as deeper insights into these processes may guide the rational design and fabrication of NPs capable of specifically targeting altered immune pathways under pathological conditions. This review offers an in-depth understanding of the diverse roles of bioresponsive NPs in immune regulation and targeted drug delivery, aiming to advance therapeutic strategies for a wide range of human diseases. Nanomaterials possess significant immunomodulatory capabilities, enhancing both disease prevention and therapeutic efficacy in vivo, yet their clinical translation is hindered by complex fabrication methods and high production costs. Future efforts must focus on simplifying synthesis and improving cost-efficiency to facilitate their adoption in clinical practice.

The role of the gut microbiome in enhancing the efficacy of anticancer treatments like chemotherapy and radiotherapy is well acknowledged. However, there is limited empirical evidence on its predictive capabilities for neoadjuvant immunochemotherapy (NICT) responses in esophageal squamous cell carcinoma (ESCC). Our study fills this gap by comprehensively analyzing the gut microbiome’s influence on NICT outcomes. We analyzed 16 S rRNA gene sequences from 136 fecal samples from 68 ESCC patients before and after NICT, along with 19 samples from healthy controls. After NICT, marked microbiome composition changes were noted, including a decrease in ESCC-associated pathogens and an increase in beneficial microbes such as Limosilactobacillus , Lacticaseibacillus , and Staphylococcus. Baseline microbiota profiles effectively differentiated responders from nonresponders, with responders showing higher levels of short-chain fatty acid (SCFA)-producing bacteria such as Faecalibacterium , Eubacterium_eligens_group , Anaerostipes , and Odoribacter , and nonresponders showing increases in Veillonella , Campylobacter , Atopobium , and Trichococcus. We then divided our patient cohort into training and test sets at a 4:1 ratio and utilized the XGBoost-RFE algorithm to identify 7 key microbial biomarkers— Faecalibacterium , Subdoligranulum , Veillonella , Hungatella , Odoribacter , Butyricicoccus , and HT002. A predictive model was developed using LightGBM, which achieved an area under the receiver operating characteristic curve (AUC) of 86.8% [95% confidence interval (CI), 73.8% to 99.4%] in the training set, 76.8% (95% CI, 41.2% to 99.7%) in the validation set, and 76.5% (95% CI, 50.4% to 100%) in the testing set. Our findings underscore the gut microbiome as a novel source of biomarkers for predicting NICT responses in ESCC, highlighting its potential to enhance personalized treatment strategies and advance the integration of microbiome profiling into clinical practice for modulating cancer treatment responses.

Despite the effectiveness of infliximab in treating Crohn's disease (CD), up to 40 % of patients fail to respond adequately. This study aimed to identify predictive biomarkers of primary non-response to infliximab in treatment-naïve CD patients by characterizing baseline gut microbiome-metabolome interactions and to validate their mechanistic role in driving therapeutic resistance. In a prospective cohort of 100 CD patients initiating infliximab therapy and 49 healthy controls, we performed longitudinal 16S rRNA sequencing and untargeted metabolomics on pre-/post-treatment fecal samples. Machine learning (twelve algorithms including K-Nearest Neighbors, Linear Discriminant Analysis, Naive Bayes, and LightGBM) identified predictive microbial and metabolic features, with findings experimentally validated through fecal microbiota transplantation (FMT) in a murine TNBS-induced colitis model. Non-responders at baseline demonstrated significant microbial dysbiosis marked by β-diversity variation, depletion of Bifidobacterium, Blautia, and Lachnospiraceae, and enrichment of Escherichia/Shigella. Metabolomic profiling identified 179 differentially abundant metabolites, including deficiencies in taurochenodeoxycholic acid (TCDCA) and perturbations in glycerophospholipid metabolism and primary bile acid biosynthesis pathways. Among single-omics models, the microbiome-based Linear Discriminant Analysis achieved optimal performance (test AUC = 0.805), surpassing metabolomics-only (best AUC = 0.634) and integrated multi-omics approaches (best AUC = 0.779). SHAP analysis revealed Bifidobacterium as the dominant protective predictor, with its depletion strongly associated with non-response. Mechanistically, MIMOSA2 analysis linked Bifidobacterium catenulatum to TCDCA production, while FMT from non-responders exacerbated murine colitis through Treg depletion and M1 macrophage polarization, confirming microbiome-driven immune dysregulation. These findings establish gut microbiome composition, particularly Bifidobacterium abundance, as a critical determinant of anti-TNF response in CD, mediated through bile acid-dependent regulation of Treg/M1 macrophage homeostasis. While multi-omics integration did not enhance predictive performance, microbiome-based machine learning models offer clinically actionable biomarkers for treatment stratification, providing a roadmap for precision therapy to overcome biological resistance in inflammatory bowel disease.