A Twist between ROS and Sperm-Mediated Intergenerational Epigenetic Inheritance.

Yoshida et al. (2020) report in this issue of Molecular Cell that a paternal low-protein diet elevates ROS in the testicular germ cells, altering ATF7 activity and H3K9me2 abundance on target genes, including tRNA loci. These changes are maintained in spermatozoa, regulating tsRNA biogenesis, and together transmit intergenerational effects.

The deflection of fatigued neck.

The human neck is a unique mechanical structure, highly flexible but fatigue prone. The rising prevalence of neck pain and chronic injuries has been attributed to increasing exposure to fatigue loading in activities such as prolonged sedentary work and overuse of electronic devices. However, a causal relationship between fatigue and musculoskeletal mechanical changes remains elusive. This work aimed to establish this relationship through a unique experiment design, inspired by a cantilever beam mechanical model of the neck, and an orchestrated deployment of advanced motion-force measurement technologies including dynamic stereo-radiographic imaging. As a group of 24 subjects performed sustained-till-exhaustion neck exertions in varied positions-neutral, extended, and flexed, their cervical spine musculoskeletal responses were measured. Data verified the occurrence of fatigue and revealed fatigue-induced neck deflection which increased cervical lordosis or kyphosis by 4-5° to 11°, depending on the neck position. This finding and its interpretations render a renewed understanding of muscle fatigue from a more unified motor control perspective as well as profound implications on neck pain and injury prevention.

Flow-electrode capacitive separation of organic acid products and recovery of alkali cations after acidic CO2 electrolysis.

Acidic CO2 electrolysis, enhanced by the introduction of alkali cations, presents a strategic approach for improving carbon efficiency compared to processes conducted in neutral and alkaline environments. However, a significant challenge arises from the dissolution of both organic acids and alkali cations in a strongly acidic feed stream, resulting in a considerable energy penalty for downstream separation. In this study, we investigate the feasibility of using flow-electrode capacitive deionization (FCDI) technology to separate organic acids and recover alkali cations from a strongly acidic feed stream (pH ~ 1). We show that organic acids, such as formic acid and acetic acid, are retained in molecular form in the separation chamber, achieving a rejection rate of over 90% under all conditions. Alkali cations, such as K+ and Cs+, migrate to the cathode chamber in ionic form, with their removal and recovery significantly influenced by their concentration and the pH of the feed stream, but responding differently to the types and concentrations of organic acids. The energy consumption for the removal and recovery of K+ is 4 to 8 times higher than for Cs+, and the charge efficiency is significantly influenced by the types of organic acid products and alkali cations. We conduct a series of electrochemical measurements and analyze the impedance spectroscopy, identifying that hindered mass transfer governed the electrode process. Our findings underscore the potential of FCDI as an advanced downstream separation technology for acidic electrocatalysis processes.

Monocytes Release Pro-Cathepsin D to Drive Blood-to-Brain Transcytosis in Diabetes.

BACKGROUND: Microvascular complications are the major outcome of type 2 diabetes progression, and the underlying mechanism remains to be determined. METHODS: High-throughput RNA sequencing was performed using human monocyte samples from controls and diabetes. The transgenic mice expressing human CTSD (cathepsin D) in the monocytes was constructed using CD68 promoter. In vivo 2-photon imaging, behavioral tests, immunofluorescence, transmission electron microscopy, Western blot analysis, vascular leakage assay, and single-cell RNA sequencing were performed to clarify the phenotype and elucidate the molecular mechanism. RESULTS: Monocytes expressed high-level CTSD in patients with type 2 diabetes. The transgenic mice expressing human CTSD in the monocytes showed increased brain microvascular permeability resembling the diabetic microvascular phenotype, accompanied by cognitive deficit. Mechanistically, the monocytes release nonenzymatic pro-CTSD to upregulate caveolin expression in brain endothelium triggering caveolae-mediated transcytosis, without affecting the paracellular route of brain microvasculature. The circulating pro-CTSD activated the caveolae-mediated transcytosis in brain endothelial cells via its binding with low-density LRP1 (lipoprotein receptor-related protein 1). Importantly, genetic ablation of CTSD in the monocytes exhibited a protective effect against the diabetes-enhanced brain microvascular transcytosis and the diabetes-induced cognitive impairment. CONCLUSIONS: These findings uncover the novel role of circulatory pro-CTSD from monocytes in the pathogenesis of cerebral microvascular lesions in diabetes. The circulatory pro-CTSD is a potential target for the intervention of microvascular complications in diabetes.

Origins and evolving functionalities of tRNA-derived small RNAs.

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) are among the most ancient small RNAs in all domains of life and are generated by the cleavage of tRNAs. Emerging studies have begun to reveal the versatile roles of tsRNAs in fundamental biological processes, including gene silencing, ribosome biogenesis, retrotransposition, and epigenetic inheritance, which are rooted in tsRNA sequence conservation, RNA modifications, and protein-binding abilities. We summarize the mechanisms of tsRNA biogenesis and the impact of RNA modifications, and propose how thinking of tsRNA functionality from an evolutionary perspective urges the expansion of tsRNA research into a wider spectrum, including cross-tissue/cross-species regulation and harnessing of the 'tsRNA code' for precision medicine.

Compact angle-resolved metasurface spectrometer.

Light scattered or radiated from a material carries valuable information on the said material. Such information can be uncovered by measuring the light field at different angles and frequencies. However, this technique typically requires a large optical apparatus, hampering the widespread use of angle-resolved spectroscopy beyond the lab. Here we demonstrate compact angle-resolved spectral imaging by combining a tunable metasurface-based spectrometer array and a metalens. With this approach, even with a miniaturized spectrometer footprint of only 4 × 4 µm2, we demonstrate a wavelength accuracy of 0.17 nm, spectral resolution of 0.4 nm and a linear dynamic range of 149 dB. Moreover, our spectrometer has a detection limit of 1.2 fJ, and can be patterned to an array for spectral imaging. Placing such a spectrometer array directly at the back focal plane of a metalens, we achieve an angular resolution of 4.88 × 10-3 rad. Our angle-resolved spectrometers empowered by metalenses can be employed towards enhancing advanced optical imaging and spectral analysis applications.

Ultracompact and Uniform Nanoemitter Array Based on Periodic Scattering.

As emerging gain materials, lead halide perovskites have drawn considerable attention in coherent light sources. With the development of patterning and integration techniques, a perovskite laser array has been realized by distributing perovskite microcrystals periodically. Nevertheless, the packing density is limited by the crystal size and the channel gap distance. More importantly, the lasing performance for individual laser units is quite random due to variation of size and crystal quality. Herein an ultracompact perovskite nanoemitter array with uniform emission has been demonstrated. Individual emitters are formed via scattering evanescent components from a shared Fabry-Perot laser, ensuring uniform lasing emission in a unit cell with a side length of 160 nm and lattice constant of 400 nm. And the periodic silicon scatterers do not deteriorate the lasing threshold dramatically. In addition, the surface emitting efficiency increased significantly. The direct integration of a densely packed nanoemitter array with a silicon platform promises high-throughput sensing and high-capacity optical interconnects.

Mass Spectrometry-Based Direct Sequencing of tRNAs De Novo and Quantitative Mapping of Multiple RNA Modifications.

Despite the extensive use of next-generation sequencing (NGS) of RNA, simultaneous direct sequencing and quantitative mapping of multiple RNA nucleotide modifications remains challenging. Mass spectrometry (MS)-based sequencing can directly sequence all RNA modifications without being limited to specific ones, but it requires a perfect MS ladder that few tRNAs can provide. Here, we describe an MS ladder complementation sequencing approach (MLC-Seq) that circumvents the perfect ladder requirement, allowing de novo MS sequencing of full-length heterogeneous cellular tRNAs with multiple nucleotide modifications at single-nucleotide precision. Unlike NGS-based methods, which lose RNA modification information, MLC-Seq preserves RNA sequence diversity and modification information, revealing new detailed stoichiometric tRNA modification profiles and their changes upon treatment with the dealkylating enzyme AlkB. It can also be combined with reference sequences to provide quantitative analysis of diverse tRNAs and modifications in total tRNA samples. MLC-Seq enables systematic, quantitative, and site-specific mapping of RNA modifications, revealing the truly complete informational content of tRNA.

Time to lymphoma treatment within 24 months in 'watch and wait' follicular lymphoma is associated with inferior outcomes: A multicentre analysis.

Some 'watch and wait' (W&W) FL patients suffer from rapid progression in a short term. Herein, we sought to identify these patients and also develop a risk score to screen them at diagnosis. Between 2008 and 2022, a total of 411 FL patients managed by the W&W strategy from 16 cancer centres were retrospectively enrolled in this study, and their time to lymphoma treatment (TLT) and progression-free survival (PFS) were evaluated. Thirty-five percent of W&W FL patients experienced TLT within 24 months (TLT24) after diagnosis. Their 5-year PFS rate was significantly lower than those without treatment at 24 months (62.3% vs. 89.5%). In multivariable analysis, five factors were identified as independent predictors of TLT24: stages III-IV, ß2 microglobulin ≥3 mg/L, lymphocyte-to-monocyte ratio <3.8, bone marrow involvement and spleen enlargement (above umbilical line). Their AUCs for TLT24 were 0.76 (95% CI, 0.70-0.82) in the training cohort and 0.76 (95% CI, 0.67-0.85) in the validation cohort respectively. Risk groups were also associated with PFS (p < 0.001). In FL patients initially managed by W&W, TLT24 was associated with poor outcomes. This multivariable model helps screening for predicting TLT24, which may be useful to identify candidates for early interventional treatment.

Allogeneic CAR-T cells with of HLA-A/B and TRAC disruption exhibit promising antitumor capacity against B cell malignancies.

BACKGROUND: Although chimeric antigen receptor T (CAR-T) cells have been proven to be an effective way of treating B cell malignancies, a lot of patients could not benefit from it because of failure in CAR-T cell manufacturing, disease progression, and unaffordable price. The study aimed to explore universal CAR-T cell products to extend the clinical accessibility. METHODS: The antitumor activity of CRISPR/Cas9-edited allogeneic anti-CD19 CAR-T (CAR-T19) cells was assessed in vitro, in animal models, and in patients with relapsed/refractory (R/R) acute B cell lymphoblastic leukemia (B-ALL) or diffuse large B cell lymphoma. RESULTS: B2M-/TRAC- universal CAR-T19 (U-CAR-T19) cells exhibited powerful anti-leukemia abilities both in vitro and in animal models, as did primary CD19+ leukemia cells from leukemia patients. However, expansion, antitumor efficacy, or graft-versus-host-disease (GvHD) was not observed in six patients with R/R B cell malignancies after U-CAR-T19 cell infusion. Accordingly, significant activation of natural killer (NK) cells by U-CAR-T19 cells was proven both clinically and in vitro. HLA-A-/B-/TRAC- novel CAR-T19 (nU-CAR-T19) cells were constructed with similar tumoricidal capacity but resistance to NK cells in vitro. Surprisingly, robust expansion of nU-CAR-T19 cells, along with rapid eradication of CD19+ abnormal B cells, was observed in the peripheral blood and bone marrow of another three patients with R/R B-ALL. The patients achieved complete remission with no detectable minimal residual disease 14 days after the infusion of nU-CAR-T19 cells. Two of the three patients had grade 2 cytokine release syndrome, which were managed using an IL-6 receptor blocker. Most importantly, GvHD was not observed in any patient, suggesting the safety of TRAC-disrupted CAR-T cells generated using the CRISPR/Cas9 method for clinical application. CONCLUSIONS: The nU-CAR-T19 cells showed a strong response in R/R B-ALL. nU-CAR-T19 cells have the potential to be a promising new approach for treating R/R B cell malignancies.

AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug-drug interaction prediction.

The properties of the drug may be altered by the combination, which may cause unexpected drug-drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction. In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder (AMDE). Specifically, in AMDE, we encode drug features from multiple dimensions, including information from both Simplified Molecular-Input Line-Entry System sequence and atomic graph of the drug. Data experiments are conducted on DDI data set selected from Drugbank, involving a total of 34 282 DDI relationships with 17 141 positive DDI samples and 17 141 negative samples. Experimental results show that our AMDE performs better than some state-of-the-art baseline methods, including Random Forest, One-Dimension Convolutional Neural Networks, DeepDrug, Long Short-Term Memory, Seq2seq, Deepconv, DeepDDI, Graph Attention Networks and Knowledge Graph Neural Networks. In practice, we select a set of 150 drugs with 3723 DDIs, which are never appeared in training, validation and test sets. AMDE performs well in DDIs prediction task, with AUROC and AUPRC 0.981 and 0.975. As well, we use Torasemide (DB00214) as an example and predict the most likely drug to interact with it. The top 15 scores all have been reported with clear interactions in literatures.

Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction.

Multi-drug combinations for the treatment of complex diseases are gradually becoming an important treatment, and this type of treatment can take advantage of the synergistic effects among drugs. However, drug-drug interactions (DDIs) are not just all beneficial. Accurate and rapid identifications of the DDIs are essential to enhance the effectiveness of combination therapy and avoid unintended side effects. Traditional DDIs prediction methods use only drug sequence information or drug graph information, which ignores information about the position of atoms and edges in the spatial structure. In this paper, we propose Molormer, a method based on a lightweight attention mechanism for DDIs prediction. Molormer takes the two-dimension (2D) structures of drugs as input and encodes the molecular graph with spatial information. Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the Siamese network architecture to serve as the architecture of Molormer, which can make full use of the limited data to train the model for better performance and also limit the differences to some extent between networks dealing with drug features. Experiments show that our proposed method outperforms state-of-the-art methods in Accuracy, Precision, Recall and F1 on multi-label DDIs dataset. In the case study section, we used Molormer to make predictions of new interactions for the drugs Aliskiren, Selexipag and Vorapaxar and validated parts of the predictions. Code and models are available at https://github.com/IsXudongZhang/Molormer.

Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma.

BACKGROUND: Concurrent chemoradiotherapy (CCRT) is a crucial treatment for non-small cell lung carcinoma (NSCLC). However, the use of deep learning (DL) models for predicting the response to CCRT in NSCLC remains unexplored. Therefore, we constructed a DL model for estimating the response to CCRT in NSCLC and explored the associated biological signaling pathways. METHODS: Overall, 229 patients with NSCLC were recruited from six hospitals. Based on contrast-enhanced computed tomography (CT) images, a three-dimensional ResNet50 algorithm was used to develop a model and validate the performance in predicting response and prognosis. An associated analysis was conducted on CT image visualization, RNA sequencing, and single-cell sequencing. RESULTS: The DL model exhibited favorable predictive performance, with an area under the curve of 0.86 (95% confidence interval [CI] 0.79-0·92) in the training cohort and 0.84 (95% CI 0.75-0.94) in the validation cohort. The DL model (low score vs. high score) was an independent predictive factor; it was significantly associated with progression-free survival and overall survival in both the training (hazard ratio [HR] = 0.54 [0.36-0.80], P = 0.002; 0.44 [0.28-0.68], P < 0.001) and validation cohorts (HR = 0.46 [0.24-0.88], P = 0.008; 0.30 [0.14-0.60], P < 0.001). The DL model was also positively related to the cell adhesion molecules, the P53 signaling pathway, and natural killer cell-mediated cytotoxicity. Single-cell analysis revealed that differentially expressed genes were enriched in different immune cells. CONCLUSION: The DL model demonstrated a strong predictive ability for determining the response in patients with NSCLC undergoing CCRT. Our findings contribute to understanding the potential biological mechanisms underlying treatment responses in these patients.

A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma.

BACKGROUND: Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression. METHODS: We categorized 264 patients from retrospective databases of two centers into training (n = 159) and validation (n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed. RESULTS: Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84-0.96) and 0.86 (95% CI 0.76-0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24-0.50], P < 0.001) and validation (HR = 0.42 [0.26-0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes (H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552). CONCLUSIONS: The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.

SEC-HPLC analysis of column load and flow-through provides critical understanding of low Protein A step yield.

For a certain number of mAbs, bispecific antibodies (bsAbs) and Fc-fusion proteins that we worked on, the Protein A capture step experienced low yield (i.e., ∼80%). A previous case study suggested that non-binding aggregate formed in cell culture was the root cause of low Protein A step yield. In the current work, we selected five projects with the low Protein A yield issue to further illustrate this phenomenon. In all cases, existence of non-binding aggregates was confirmed by size-exclusion chromatography-high performance liquid chromatography (SEC-HPLC) analysis of Protein A load and flow-through. In addition, we demonstrated that aggregates failed to bind to Protein A resin mainly due to their large sizes, which prevented them from entering the resin beads. As the data suggested, SEC-HPLC analysis of Protein A load and flow-through, although not a standard procedure, can provide information that is critical for understanding the unexpected performance of Protein A chromatography in cases like those being presented here. Thus, SEC-HPLC analysis of Protein A load and flow-through is highly recommended for antibodies/Fc-fusions suffering from low Protein A yield.

Ultrasensitive Eu-Based MOF Luminescence Sensor for Clenbuterol Visible Recognition.

Clenbuterol (CLB) as an illegal feed additive may cause a great security risk to food safety. However, convenient and efficient detection means for CLB in practical application remain a formidable challenge. Herein, a stable Eu-based organic framework {[H2N(CH3)2]2[Eu2(ttca)2]·H2O}n (compound 1) (H4ttca = [1,1':2',1″-terphenyl]-4,4',4″,5'-tetracarboxylic acid) has been harvested, exhibiting excellent chemical stability and thermal stability. Luminescence investigation reveals that compound 1 can sensitively and selectively detect CLB without being affected by different components from simulated serum and urine (limit detection: 22.7 nM). Furthermore, sensor 1 can also be applicable to CLB recognition in real swine feeds, presenting excellent anti-interference performance. The good cyclicity of compound 1 endows CLB determination with many advantages: low cost, high stability, and simplicity. Importantly, in view of the indication of the luminescence color (red to blue), test membranes were fabricated and employed for convenient and fast CLB detection, providing a valuable scheme for the visual monitoring of CLB in meat products. This work enriches rare earth metal compounds and luminescence sensor portfolios and breaks the concentration record (nM) for detecting CLB compared with reported complex materials, providing an effective monitoring platform for CLB visually.

Mixing due to Solution Switch Limits the Performance of Electrosorption for Desalination.

Electrosorption (ES) is a research frontier in electrochemical separation, with proven potential applications in desalination, wastewater treatment, and selective resource extraction. However, due to the limited adsorption capacity of film electrodes, ES requires short circuiting or circuit reversal, accompanied by a solution switch between the feed solution and receiving solution, to sustain desalination over many charge-discharge cycles. In previously reported studies, solution switches have been commonly ignored to simplify experimental procedures, and their impacts on separation performance are thus not well understood. This study aims to provide a quantitative analysis of the impacts of mixing due to a solution switch on the performance of ES-based desalination. A numerical model of ES has been employed to evaluate the adverse effects of the solution switch on the desalination performance in three commonly used operation modes. The analysis reveals that the impacts of mixing due to solution-switch are more severe with a larger concentration difference between the desalinated water and the brine and provides insights into the effectiveness of increasing electrode loading or specific capacity in mitigating the detrimental impacts of mixing. Even with state-of-the-art systems, producing freshwater from seawater or even brackish water with medium-to-high salinity is practically challenging due to the presence of solution switch.

A novel prognostic index for extranodal natural killer/T-cell lymphoma in the era of pegaspargase/L-asparaginase.

Aim: This multicenter retrospective study aimed to develop a novel prognostic system for extranodal natural killer/T-cell lymphoma (ENKTL) patients in the era of pegaspargase/L-asparaginase. Materials & methods: A total of 844 newly diagnosed ENKTL patients were included. Results: Multivariable analysis confirmed that Eastern Cooperative Oncology Group performance status, lactate dehydrogenase, Chinese Southwest Oncology Group and Asia Lymphoma Study Group ENKTL (CA) system, and albumin were independent prognostic factors. By rounding up the hazard ratios from four significant variables, a maximum of 7 points were assigned. The model of Huaihai Lymphoma Working Group-Natural killer/T-cell Lymphoma prognostic index (NPI) was identified with four risk groups and the 5-year overall survival was 88.2, 66.7, 54.3 and 30.5%, respectively. Conclusion: Huaihai Lymphoma Working Group-NPI provides a feasible stratification system for patients with ENKTL in the era of pegaspargase/L-asparaginase.

[Box: see text].

Feasibility of sludge deep dewaterability improvement for incineration disposal by combined conditioning of freeze-thaw and sawdust.

Sludge incineration is the main strategy for sludge reduction in China. The combined conditioning of lime and chemical agents has been proven to achieve sludge dewatering by disrupting the extracellular polymeric substances (EPS) of sludge and reducing its compressibility. However, when incineration is the intended disposal purpose, this method poses challenges such as incomplete combustion, equipment corrosion, secondary pollution, and decreased calorific value of sludge cake. In contrast, freeze-thaw conditioning, coupled with sawdust as a high-calorific-value bio-waste, emerges as an efficient and clean alternative. The research investigates the synergistic effects of freeze-thaw and sawdust co-conditioning on various sludge properties, including dewaterability, compressibility, consolidation, permeability, microscopicity, and calorific value. The study reveals that the combined conditioning significantly reduces water content and compressibility while increasing void ratio, consolidation, permeability, and enhancing the calorific value of the sludge cake. Specifically, sludge cake conditioned with 60% dried solids (DS) sawdust and freeze-thaw achieved a water content (Wc) of 49.07% and a calorific value of 1422.3 kcal/kg, meeting standards for self-sustained incineration. With heat recovery, the combined conditioning generates an economic revenue of 25.1 $/t DS after deducting costs, thereby reducing the overall cost of sludge reduction treatment. This research offers a clean and practical solution for sludge incineration and reduction, demonstrating great economic value and application potential.

Deciphering the impact of circRNA-mediated autophagy on tumor therapeutic resistance: a novel perspective.

Cancer therapeutic resistance remains a significant challenge in the pursuit of effective treatment strategies. Circular RNAs (circRNAs), a class of non-coding RNAs, have recently emerged as key regulators of various biological processes, including cancer progression and drug resistance. This review highlights the emerging role of circRNAs-mediated autophagy in cancer therapeutic resistance, a cellular process that plays a dual role in cancer by promoting both cell survival and death. Increasing evidence suggests that circRNAs can modulate autophagy pathways, thereby influencing the response of cancer cells to therapeutic agents. In this context, the intricate interplay between circRNAs, autophagy, and therapeutic resistance is explored. Various mechanisms are discussed through which circRNAs can impact autophagy, including direct interactions with autophagy-related genes, modulation of signaling pathways, and cross-talk with other non-coding RNAs. Furthermore, the review delves into specific examples of how circRNA-mediated autophagy regulation can contribute to resistance against chemotherapy and radiotherapy. Understanding these intricate molecular interactions provides valuable insights into potential strategies for overcoming therapeutic resistance in cancer. Exploiting circRNAs as therapeutic targets or utilizing them as diagnostic and predictive biomarkers opens new avenues for developing personalized treatment approaches. In summary, this review underscores the importance of circRNA-mediated autophagy in cancer therapeutic resistance and proposes future directions for research in this exciting and rapidly evolving field.