The ZMYND8-regulated mevalonate pathway endows YAP-high intestinal cancer with metabolic vulnerability.

Cholesterol metabolism is tightly associated with colorectal cancer (CRC). Nevertheless, the clinical benefit of statins, the inhibitor of cholesterol biogenesis mevalonate (MVA) pathway, is inconclusive, possibly because of a lack of patient stratification criteria. Here, we describe that YAP-mediated zinc finger MYND-type containing 8 (ZMYND8) expression sensitizes intestinal tumors to the inhibition of the MVA pathway. We show that the oncogenic activity of YAP relies largely on ZMYND8 to enhance intracellular de novo cholesterol biogenesis. Disruption of the ZMYND8-dependent MVA pathway greatly restricts the self-renewal capacity of Lgr5+ intestinal stem cells (ISCs) and intestinal tumorigenesis. Mechanistically, ZMYND8 and SREBP2 drive the enhancer-promoter interaction to facilitate the recruitment of Mediator complex, thus upregulating MVA pathway genes. Together, our results establish that the epigenetic reader ZMYND8 endows YAP-high intestinal cancer with metabolic vulnerability.

Mid-infrared trace detection with parts-per-quadrillion quantitation accuracy: Expanding frontiers of radiocarbon sensing.

Detection sensitivity is a critical characteristic to consider during selection of spectroscopic techniques. However, high sensitivity alone is insufficient for spectroscopic measurements in spectrally congested regions. Two-color cavity ringdown spectroscopy (2C-CRDS), based on intra-cavity pump-probe detection, simultaneously achieves high detection sensitivity and selectivity. This combination enables mid-infrared detection of radiocarbon dioxide ([Formula: see text]CO[Formula: see text]) molecules in room-temperature CO[Formula: see text] samples, with 1.4 parts-per-quadrillion (ppq, 10[Formula: see text]) sensitivity (average measurement precision) and 4.6-ppq quantitation accuracy (average calibrated measurement error for 21 samples from four separate trials) demonstrated on samples with [Formula: see text]C/C up to [Formula: see text]1.5[Formula: see text] natural abundance ([Formula: see text]1,800 ppq). These highly reproducible measurements, which are the most sensitive and quantitatively accurate in the mid-infrared, are accomplished despite the presence of orders-of-magnitude stronger, one-photon signals from other CO[Formula: see text] isotopologues. This is a major achievement in laser spectroscopy. A room-temperature-operated, compact, and low-cost 2C-CRDS sensor for [Formula: see text]CO[Formula: see text] benefits a wide range of scientific fields that utilize [Formula: see text]C for dating and isotope tracing, most notably atmospheric [Formula: see text]CO[Formula: see text] monitoring to track CO[Formula: see text] emissions from fossil fuels. The 2C-CRDS technique significantly enhances the general utility of high-resolution mid-infrared detection for analytical measurements and fundamental chemical dynamics studies.

Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model.

Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral characteristics to dynamic structures poses a formidable challenge. Here, we present a pretrained machine learning model based on 2DIR spectra analysis. This model has learned signal features from approximately 204,300 spectra to establish a "spectrum-structure" correlation, thereby tracing the dynamic conformations of proteins. It excels in accurately predicting the dynamic content changes of various secondary structures and demonstrates universal transferability on real folding trajectories spanning timescales from microseconds to milliseconds. Beyond exceptional predictive performance, the model offers attention-based spectral explanations of dynamic conformational changes. Our 2DIR-based pretrained model is anticipated to provide unique insights into the dynamic structural information of proteins in their native environments.

A genomic and epigenomic atlas of prostate cancer in Asian populations.

Prostate cancer is the second most common cancer in men worldwide1. Over the past decade, large-scale integrative genomics efforts have enhanced our understanding of this disease by characterizing its genetic and epigenetic landscape in thousands of patients2,3. However, most tumours profiled in these studies were obtained from patients from Western populations. Here we produced and analysed whole-genome, whole-transcriptome and DNA methylation data for 208 pairs of tumour tissue samples and matched healthy control tissue from Chinese patients with primary prostate cancer. Systematic comparison with published data from 2,554 prostate tumours revealed that the genomic alteration signatures in Chinese patients were markedly distinct from those of Western cohorts: specifically, 41% of tumours contained mutations in FOXA1 and 18% each had deletions in ZNF292 and CHD1. Alterations of the genome and epigenome were correlated and were predictive of disease phenotype and progression. Coding and noncoding mutations, as well as epimutations, converged on pathways that are important for prostate cancer, providing insights into this devastating disease. These discoveries underscore the importance of including population context in constructing comprehensive genomic maps for disease.

Slow-release synthesis of Cu single-atom catalysts with the optimized geometric structure and density of state distribution for Fenton-like catalysis.

Single-atom Fenton-like catalysis has attracted significant attention, yet the quest for controllable synthesis of single-atom catalysts (SACs) with modulation of electron configuration is driven by the current disadvantages of poor activity, low selectivity, narrow pH range, and ambiguous structure-performance relationship. Herein, we devised an innovative strategy, the slow-release synthesis, to fabricate superior Cu SACs by facilitating the dynamic equilibrium between metal precursor supply and anchoring site formation. In this strategy, the dynamics of anchoring site formation, metal precursor release, and their binding reaction kinetics were regulated. Bolstered by harmoniously aligned dynamics, the selective and specific monatomic binding reactions were ensured to refine controllable SACs synthesis with well-defined structure-reactivity relationship. A copious quantity of monatomic dispersed metal became deposited on the C3N4/montmorillonite (MMT) interface and surface with accessible exposure due to the convenient mass transfer within ordered MMT. The slow-release effect facilitated the generation of targeted high-quality sites by equilibrating the supply and demand of the metal precursor and anchoring site and improved the utilization ratio of metal precursors. An excellent Fenton-like reactivity for contaminant degradation was achieved by the Cu1/C3N4/MMT with diminished toxic Cu liberation. Also, the selective ·OH-mediated reaction mechanism was elucidated. Our findings provide a strategy for regulating the intractable anchoring events and optimizing the microenvironment of the monatomic metal center to synthesize superior SACs.

Machine learning of spectra-property relationship for imperfect and small chemistry data.

Machine learning (ML) is causing profound changes to chemical research through its powerful statistical and mathematical methodological capabilities. However, the nature of chemistry experiments often sets very high hurdles to collect high-quality data that are deficiency free, contradicting the need of ML to learn from big data. Even worse, the black-box nature of most ML methods requires more abundant data to ensure good transferability. Herein, we combine physics-based spectral descriptors with a symbolic regression method to establish interpretable spectra-property relationship. Using the machine-learned mathematical formulas, we have predicted the adsorption energy and charge transfer of the CO-adsorbed Cu-based MOF systems from their infrared and Raman spectra. The explicit prediction models are robust, allowing them to be transferrable to small and low-quality dataset containing partial errors. Surprisingly, they can be used to identify and clean error data, which are common data scenarios in real experiments. Such robust learning protocol will significantly enhance the applicability of machine-learned spectroscopy for chemical science.

Fractional Flow Reserve or Intravascular Ultrasonography to Guide PCI.

BACKGROUND: In patients with coronary artery disease who are being evaluated for percutaneous coronary intervention (PCI), procedures can be guided by fractional flow reserve (FFR) or intravascular ultrasonography (IVUS) for decision making regarding revascularization and stent implantation. However, the differences in clinical outcomes when only one method is used for both purposes are unclear. METHODS: We randomly assigned 1682 patients who were being evaluated for PCI for the treatment of intermediate stenosis (40 to 70% occlusion by visual estimation on coronary angiography) in a 1:1 ratio to undergo either an FFR-guided or IVUS-guided procedure. FFR or IVUS was to be used to determine whether to perform PCI and to assess PCI success. In the FFR group, PCI was to be performed if the FFR was 0.80 or less. In the IVUS group, the criteria for PCI were a minimal lumen area measuring either 3 mm2 or less or measuring 3 to 4 mm2 with a plaque burden of more than 70%. The primary outcome was a composite of death, myocardial infarction, or revascularization at 24 months after randomization. We tested the noninferiority of the FFR group as compared with the IVUS group (noninferiority margin, 2.5 percentage points). RESULTS: The frequency of PCI was 44.4% among patients in the FFR group and 65.3% among those in the IVUS group. At 24 months, a primary-outcome event had occurred in 8.1% of the patients in the FFR group and in 8.5% of those in the IVUS group (absolute difference, -0.4 percentage points; upper boundary of the one-sided 97.5% confidence interval, 2.2 percentage points; P = 0.01 for noninferiority). Patient-reported outcomes as reported on the Seattle Angina Questionnaire were similar in the two groups. CONCLUSIONS: In patients with intermediate stenosis who were being evaluated for PCI, FFR guidance was noninferior to IVUS guidance with respect to the composite primary outcome of death, myocardial infarction, or revascularization at 24 months. (Funded by Boston Scientific; FLAVOUR ClinicalTrials.gov number, NCT02673424.).

The RNA ligase RNA terminal phosphate cyclase B regulates mRNA alternative splicing and is required for mouse oocyte development and maintenance.

Recent large-scale mRNA sequencing has shown that introns are retained in 5-10% of mRNA, and these events are named intron retention (IR). IR has been recognized as a key mechanism in the regulation of gene expression. However, the role of this mechanism in female reproduction in mammals remains unclear. RNA terminal phosphate cyclase B (RTCB) is a RNA ligase; we found that RTCB conditional knockout mice have premature ovarian failure and that RTCB plays a crucial role in follicular development. RTCB regulated the splicing of transcripts related to DNA methylation and DNA damage repair. In addition, it regulated the resumption of oocyte meiosis by affecting CDK1 activation. Moreover, the loss of RTCB suppressed zygotic genome activation (ZGA) and decreased translation at the global level. In addition, Rtcb deletion resulted in the accumulation of maternal mRNAs containing unspliced introns and in a decline in the overall level of transcripts. As a result, the Rtcb-/- females were sterile. Our study highlights the important role of RTCB-regulated noncanonical alternative splicing in female reproduction.

Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers.

The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain types, particularly conformational and chiral ones that share the same bonding connectivity but differ in spatial arrangement. Here, we designed a hybrid molecular graph network, Chemical Feature Fusion Network (CFFN), to address the issue by integrating planar and stereo information of molecules in an interweaved fashion. The three-dimensional (3D, i.e., stereo) modality guarantees precision and completeness by providing unabridged information, while the two-dimensional (2D, i.e., planar) modality brings in chemical intuitions as prior knowledge for guidance. The zipper-like arrangement of 2D and 3D information processing promotes cooperativity between them, and their synergy is the key to our model's success. Experiments on various molecules or conformational datasets including a special newly created chiral molecule dataset comprised of various configurations and conformations demonstrate the superior performance of CFFN. The advantage of CFFN is even more significant in datasets made of small samples. Ablation experiments confirm that fusing 2D and 3D molecular graphs as unambiguous molecular descriptors can not only effectively distinguish molecules and their conformations, but also achieve more accurate and robust prediction of quantum chemical properties.

Potato virus Y viral protein 6K1 inhibits the interaction between defense proteins during virus infection.

14-3-3 proteins play vital roles in plant defense against various pathogen invasions. To date, how 14-3-3 affects virus infections in plants remains largely unclear. In this study, we found that Nicotiana benthamiana 14-3-3h interacts with TRANSLATIONALLY CONTROLLED TUMOR PROTEIN (TCTP), a susceptibility factor of potato virus Y (PVY). Silencing of Nb14-3-3h facilitates PVY accumulation, whereas overexpression of Nb14-3-3h inhibits PVY replication. The antiviral activities of 3 Nb14-3-3h dimerization defective mutants are significantly decreased, indicating that dimerization of Nb14-3-3h is indispensable for restricting PVY infection. Our results also showed that the mutant Nb14-3-3hE16A, which is capable of dimerizing but not interacting with NbTCTP, has reduced anti-PVY activity; the mutant NbTCTPI65A, which is unable to interact with Nb14-3-3h, facilitates PVY replication compared with the wild-type NbTCTP, indicating that dimeric Nb14-3-3h restricts PVY infection by interacting with NbTCTP and preventing its proviral function. As a counter-defense, PVY 6K1 interferes with the interaction between Nb14-3-3h and NbTCTP by competitively binding to Nb14-3-3h and rescues NbTCTP to promote PVY infection. Our results provide insights into the arms race between plants and potyviruses.

Neural mechanisms of social comparison in subthreshold depression.

Social comparison is a common phenomenon in our daily life, through which people get to know themselves, and plays an important role in depression. In this study, event-related potential (ERP) was used to explore the temporal course of social comparison processing in the subthreshold depression group. Electrophysiological recordings were acquired from 30 subthreshold depressed individuals and 31 healthy individuals while they conducted the adapted dot estimation task. The ERP results revealed that there was a significant difference of feedback-related negativity (FRN) in the process of social comparison. Especially only in the subthreshold depression, the FRN amplitudes of worse off than some, better off than many comparisons were larger than those of upward comparisons and downward comparisons. Our results suggested that the abnormal reward sensitivity for worse off than some, better off than many comparisons might be prodromal symptoms in the subthreshold depression.

Machine learning recognition of protein secondary structures based on two-dimensional spectroscopic descriptors.

Protein secondary structure discrimination is crucial for understanding their biological function. It is not generally possible to invert spectroscopic data to yield the structure. We present a machine learning protocol which uses two-dimensional UV (2DUV) spectra as pattern recognition descriptors, aiming at automated protein secondary structure determination from spectroscopic features. Accurate secondary structure recognition is obtained for homologous (97%) and nonhomologous (91%) protein segments, randomly selected from simulated model datasets. The advantage of 2DUV descriptors over one-dimensional linear absorption and circular dichroism spectra lies in the cross-peak information that reflects interactions between local regions of the protein. Thanks to their ultrafast (∼200 fs) nature, 2DUV measurements can be used in the future to probe conformational variations in the course of protein dynamics.

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.

TIMP2 protects against sepsis-associated acute kidney injury by cAMP/NLRP3 axis-mediated pyroptosis.

The tissue inhibitor of metalloproteinases 2 (TIMP2) has emerged as a promising biomarker for predicting the risk of sepsis-associated acute kidney injury (SA-AKI). However, its exact role in SA-AKI and the underlying mechanism remains unclear. In this study, we investigated the impact of kidney tubule-specific Timp2 knockout mice on kidney injury and inflammation. Our findings demonstrated that Timp2-knockout mice exhibited more severe kidney injury than wild-type mice, along with elevated levels of pyroptosis markers NOD-like receptor protein 3 (NLRP3), Caspase1, and gasdermin D (GSDMD) in the early stage of SA-AKI. Conversely, the expression of exogenous TIMP2 in TIMP2-knockout mice still protected against kidney damage and inflammation. In in vitro experiments, using recombinant TIMP2 protein, TIMP2 knockdown demonstrated that exogenous TIMP2 inhibited pyroptosis of renal tubular cells stimulated by lipopolysaccharide (LPS). Mechanistically, TIMP2 promoted the ubiquitination and autophagy-dependent degradation of NLRP3 by increasing intracellular cyclic adenosine monophosphate (cAMP), which mediated NLRP3 degradation through recruiting the E3 ligase MARCH7, attenuating downstream pyroptosis, and thus alleviating primary tubular cell damage. These results revealed the renoprotective role of extracellular TIMP2 in SA-AKI by attenuating tubular pyroptosis, and suggested that exogenous administration of TIMP2 could be a promising therapeutic intervention for SA-AKI treatment.NEW & NOTEWORTHY Tissue inhibitor of metalloproteinase 2 (TIMP-2) has been found to be the best biomarker for predicting the risk of sepsis-associated acute kidney injury (SA-AKI). However, its role and the underlying mechanism in SA-AKI remain elusive. The authors demonstrated in this study using kidney tubule-specific knockout mice model of SA-AKI and primary renal tubule cells stimulated with lipopolysaccharide (LPS) that extracellular TIMP-2 promoted NOD-like receptor protein 3 (NLRP3) ubiquitination and autophagy-dependent degradation by increasing intracellular cyclic adenosine monophosphate (cAMP), thus attenuated pyroptosis and alleviated renal damage.

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics.

The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

Modulating the Primary and Secondary Coordination Spheres of Single Ni(II) Sites in Metal-Organic Frameworks for Boosting Photocatalysis.

Though the coordination environment of single metal sites has been recognized to be of great importance in promoting catalysis, the influence of simultaneous precise modulation of primary and secondary coordination spheres on catalysis remains largely unknown. Herein, a series of single Ni(II) sites with altered primary and secondary coordination spheres have been installed onto metal-organic frameworks (MOFs) with UiO-67 skeleton, affording UiO-Ni-X-Y (X = S, O; Y = H, Cl, CF3) with X and Y on the primary and secondary coordination spheres, respectively. Upon deposition with CdS nanoparticles, the resulting composites present high photocatalytic H2 production rates, in which the optimized CdS/UiO-Ni-S-CF3 exhibits an excellent activity of 13.44 mmol g-1, ∼500 folds of the pristine catalyst (29.6 µmol g-1 for CdS/UiO), in 8 h, highlighting the key role of microenvironment modulation around Ni sites. Charge kinetic analysis and theoretical calculation results demonstrate that the charge transfer dynamics and reaction energy barrier are closely correlated with their coordination spheres. This work manifests the advantages of MOFs in the fabrication of structurally precise catalysts and the elucidation of particular influences of microenvironment modulation around single metal sites on the catalytic performance.

Spectroscopy-Guided Deep Learning Predicts Solid-Liquid Surface Adsorbate Properties in Unseen Solvents.

Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types. Herein, we employ a combination of the first-principles-based approach and data-driven model. Specifically, we utilize density functional theory (DFT) to calculate theoretical spectral data of CO-Ag adsorption in 23 different solvent systems as a data source. Subsequently, we propose a hierarchical knowledge extraction multiexpert neural network (HMNN) to bridge the knowledge gaps among different solvent systems. HMNN undergoes two training tiers: in tier I, it learns fundamental quantitative spectra-property relationships (QSPRs), and in tier II, it inherits the fundamental QSPR knowledge from previous steps through a dynamic integration of expert modules and subsequently captures the solvent differences. The results demonstrate HMNN's superiority in estimating a range of molecular adsorbate properties, with an error range of less than 0.008 eV for zero-shot predictions on unseen solvents. The findings underscore the usability, reliability, and convenience of HMNN and could pave the way for real-time access to microscopic properties by exploiting QSPR.

Manipulating the Spin State of Co Sites in Metal-Organic Frameworks for Boosting CO2 Photoreduction.

Photocatalytic CO2 reduction holds great potential for alleviating global energy and environmental issues, where the electronic structure of the catalytic center plays a crucial role. However, the spin state, a key descriptor of electronic properties, is largely overlooked. Herein, we present a simple strategy to regulate the spin states of catalytic Co centers by changing their coordination environment by exchanging the Co species into a stable Zn-based metal-organic framework (MOF) to afford Co-OAc, Co-Br, and Co-CN for CO2 photoreduction. Experimental and DFT calculation results suggest that the distinct spin states of the Co sites give rise to different charge separation abilities and energy barriers for CO2 adsorption/activation in photocatalysis. Consequently, the optimized Co-OAc with the highest spin-state Co sites presents an excellent photocatalytic CO2 activity of 2325.7 µmol·g-1·h-1 and selectivity of 99.1% to CO, which are among the best in all reported MOF photocatalysts, in the absence of a noble metal and additional photosensitizer. This work underlines the potential of MOFs as an ideal platform for spin-state manipulation toward improved photocatalysis.

Fibroblastic reticular cell-derived exosomes are a promising therapeutic approach for septic acute kidney injury.

Sepsis-induced acute kidney injury (S-AKI) is highly lethal, and effective drugs for treatment are scarce. Previously, we reported the robust therapeutic efficacy of fibroblastic reticular cells (FRCs) in sepsis. Here, we demonstrate the ability of FRC-derived exosomes (FRC-Exos) to improve C57BL/6 mouse kidney function following cecal ligation and puncture-induced sepsis. In vivo imaging confirmed that FRC-Exos homed to injured kidneys. RNA-Seq analysis of FRC-Exo-treated primary kidney tubular cells (PKTCs) revealed that FRC-Exos influenced PKTC fate in the presence of lipopolysaccharide (LPS). FRC-Exos promoted kinase PINK1-dependent mitophagy and inhibited NLRP3 inflammasome activation in LPS-stimulated PKTCs. To dissect the mechanism underlying the protective role of Exos in S-AKI, we examined the proteins within Exos by mass spectrometry and found that CD5L was the most upregulated protein in FRC-Exos compared to macrophage-derived Exos. Recombinant CD5L treatment in vitro attenuated kidney cell swelling and surface bubble formation after LPS stimulation. FRCs were infected with a CD5L lentivirus to increase CD5L levels in FRC-Exos, which were then modified in vitro with the kidney tubular cell targeting peptide LTH, a peptide that binds to the biomarker protein kidney injury molecule-1 expressed on injured tubule cells, to enhance binding specificity. Compared with an equivalent dose of recombinant CD5L, the modified CD5L-enriched FRC-Exos selectively bound PKTCs, promoted kinase PINK-ubiquitin ligase Parkin-mediated mitophagy, inhibiting pyroptosis and improved kidney function by hindering NLRP3 inflammasome activation, thereby improving the sepsis survival rate. Thus, strategies to modify FRC-Exos could be a new avenue in developing therapeutics against kidney injury.

Two Hematological Markers Predicting the Efficacy and Prognosis of Neoadjuvant Chemotherapy Using Lobaplatin Against Triple-Negative Breast Cancer.

BACKGROUND: Our previous work indicated that the addition of lobaplatin to combined therapy with taxane and anthracycline can improve the pathological complete response rate of neoadjuvant therapy for triple-negative breast cancer (TNBC) and lengthen long-term survival significantly, but the therapeutic markers of this regimen are unclear. METHODS: Eighty-three patients who met the inclusion criteria were included in this post hoc analysis. We analyzed the association between platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) before neoadjuvant chemotherapy with the efficacy and prognosis after treatment with docetaxel, epirubicin, and lobaplatin neoadjuvant chemotherapy regimen. χ2 test and Cox regression were used to analyze the association between PLR and NLR with total pathologic complete response (tpCR), as well as the association between PLR and NLR with event-free survival (EFS) and overall survival (OS), respectively. RESULTS: The tpCR rate in the PLR- group was 49.0% (25/51), which was significantly higher than that in the PLR+ group (25.0% [8/32], P = .032). The tpCR rate in the NLR- group was 49.1% (26/53), which was significantly higher than that in the NLR+ group (23.3% [7/30], P = .024). The tpCR rate of the PLR-NLR- (PLR- and NLR-) group was 53.7% (22/41), which was significantly higher than that of the PLR+/NLR+ (PLR+ or/and NLR+) group (26.1% [11/42]; P = .012). EFS and OS in the NLR+ group were significantly shorter than those in the NLR- group (P = .028 for EFS; P = .047 for OS). Patients in the PLR-NLR- group had a longer EFS than those in the PLR+/NLR+ group (P = .002). CONCLUSION: PLR and NLR could be used to predict the efficacy of neoadjuvant therapy with the taxane, anthracycline, and lobaplatin regimen for patients with TNBC, as patients who had lower PLR and NLR values had a higher tpCR rate and a better long-term prognosis.