Splicing factor TRA2A contributes to esophageal cancer progression via a noncanonical role in lncRNA m⁶A methylation

Transformer 2 alpha homolog (TRA2A), a member of the serine/arginine-rich splicing factor family, has been shown to control mRNA splicing in development and cancers. However, it remains unclear whether TRA2A is involved in lncRNA regulation. In the present study, we found that TRA2A was upregulated and correlated with poor prognosis in esophageal cancer. Downregulation of TRA2A suppressed the tumor growth in xenograft nude mice. Epitranscriptomic microarray showed that depletion of TRA2A affected global lncRNA methylation similarly to the key m6A methyltransferase, METTL3, by silencing. MeRIP-qPCR, RNA pull-down, CLIP analyses, and stability assays indicated that ablation of TRA2A reduced m6A-modification of the oncogenic lncRNA MALAT1, thus inducing structural alterations and reduced stability. Furthermore, Co-IP experiments showed TRA2A directly interacted with METTL3 and RBMX, which also affected the writer KIAA1429 expression. Knockdown of TRA2A inhibited cell proliferation in a manner restored by RBMX/KIAA1429 overexpression. Clinically, MALAT1, RBMX, and KIAA1429 were prognostic factors of worse survival in ESCA patients. Structural similarity-based virtual screening in FDA-approved drugs repurposed nebivolol, a β1-adrenergic receptor antagonist, as a potent compound to suppress the proliferation of esophageal cancer cells. Cellular thermal shift and RIP assay indicated that nebivolol may compete with MALAT1 to bind TRA2A. In conclusion, our study revealed the noncanonical function of TRA2A, which coordinates with multiple methylation proteins to promote oncogenic MALAT1 during ESCA carcinogenesis.</p

Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMs

The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towards their on-device deployment. As one of the most well-established pre-LLMs approaches in reducing model complexity, network pruning appears to lag behind in the era of LLMs, due mostly to its costly fine-tuning (or re-training) necessity under the massive volumes of model parameter and training data. To close this industry-academia gap, we introduce Dynamic Sparse No Training (DSnoT), a training-free fine-tuning approach that slightly updates sparse LLMs without the expensive backpropagation and any weight updates. Inspired by the Dynamic Sparse Training, DSnoT minimizes the reconstruction error between the dense and sparse LLMs, in the fashion of performing iterative weight pruning-and-growing on top of sparse LLMs. To accomplish this purpose, DSnoT particularly takes into account the anticipated reduction in reconstruction error for pruning and growing, as well as the variance w.r.t. different input data for growing each weight. This practice can be executed efficiently in linear time since its obviates the need of backpropagation for fine-tuning LLMs. Extensive experiments on LLaMA-V1/V2, Vicuna, and OPT across various benchmarks demonstrate the effectiveness of DSnoT in enhancing the performance of sparse LLMs, especially at high sparsity levels. For instance, DSnoT is able to outperform the state-of-the-art Wanda by 26.79 perplexity at 70% sparsity with LLaMA-7B. Our paper offers fresh insights into how to fine-tune sparse LLMs in an efficient training-free manner and open new venues to scale the great potential of sparsity to LLMs. Codes are available at https://github.com/zyxxmu/DSnoT.Comment: Published as a conference paper at ICLR 202

A novel anoikis-related gene prognostic signature and its correlation with the immune microenvironment in colorectal cancer

Background: Anoikis is a type of apoptosis associated with cell detachment. Resistance to anoikis is a focal point of tumor metastasis. This study aimed to explore the relationship among anoikis-related genes (ARGs), immune infiltration, and prognosis in colorectal cancer (CRC).Methods: The transcriptome profile and clinical data on patients with CRC were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Patients were divided into two clusters based on the expression of ARGs. Differences between the two ARG molecular subtypes were analyzed in terms of prognosis, functional enrichment, gene mutation frequency, and immune cell infiltration. An ARG-related prognostic signature for predicting overall survival in patients with CRC was developed and validated using absolute value convergence and selection operator (LASSO) regression analysis. The correlation between the signature risk score and clinicopathological features, immune cell infiltration, immune typing, and immunotherapy response was analyzed. The risk score combined with clinicopathological characteristics was used to construct a nomogram to assess CRC patients’ prognosis.Results: Overall, 151 ARGs were differentially expressed in CRC. Two ARG subtypes, namely, ARG-high and ARG-low groups, were identified and correlated with CRC prognosis. The gene mutation frequency and immune, stromal, and ESTIMATE scores of the ARG-high group were higher than those of the ARG-low group. Moreover, CD8, natural killer cells, M1 macrophages, human leukocyte antigen (HLA), and immune checkpoint-related genes were significantly increased in the ARG-high group. An optimized 25-gene CRC prognostic signature was successfully constructed, and its prognostic predictive ability was validated. The high-risk score was correlated with T, N, M, and TNM stages. Risk scores were negatively correlated with dendritic cells, eosinophils, and CD4 cells, and significantly positively correlated with regulatory T cells. Patients in the high-risk group were more likely to exhibit immune unresponsiveness. Finally, the nomogram model was constructed and showed good prognostic predictive power.Conclusion: ARGs are associated with clinicopathological features and the prognosis of CRC, and play important roles in the immune microenvironment. Herein, we underpinned the usefulness of ARGs in CRC to develop more effective immunotherapy techniques

A novel deep-learning based weighted feature fusion architecture for precise classification of pressure injury

Introduction: Precise classification has an important role in treatment of pressure injury (PI), while current machine-learning or deeplearning based methods of PI classification remain low accuracy.Methods: In this study, we developed a deeplearning based weighted feature fusion architecture for fine-grained classification, which combines a top-down and bottom-up pathway to fuse high-level semantic information and low-level detail representation. We validated it in our established database that consist of 1,519 images from multi-center clinical cohorts. ResNeXt was set as the backbone network.Results: We increased the accuracy of stage 3 PI from 60.3% to 76.2% by adding weighted feature pyramid network (wFPN). The accuracy for stage 1, 2, 4 PI were 0.870, 0.788, and 0.845 respectively. We found the overall accuracy, precision, recall, and F1-score of our network were 0.815, 0.808, 0.816, and 0.811 respectively. The area under the receiver operating characteristic curve was 0.940.Conclusions: Compared with current reported study, our network significantly increased the overall accuracy from 75% to 81.5% and showed great performance in predicting each stage. Upon further validation, our study will pave the path to the clinical application of our network in PI management

La notion de « lieu » dans <i>W ou le souvenir d’enfance</i> et dans <i>Dora Bruder</i> : un voyage de la mémoire à travers le lieu quotidien

Dans W ou le souvenir d’enfance et Dora Bruder, Georges Perec et Patrick Modiano, deux écrivains issus de la génération d’après-guerre, mènent une enquête sur la mémoire individuelle et la Shoah : les souvenirs d’enfance de Perec, et ceux d’une jeune fille juive nommée Dora Bruder à travers lesquels Modiano parle aussi des histoires de son père . Dans ces deux œuvres, Perec et Modiano utilisent les lieux quotidiens pour raconter la mémoire. Comment ces lieux sont-ils capables de retracer le passé ? Cependant, pour quelle raison ce rappel des souvenirs ne fonctionne-t-il souvent pas ? La réflexion sur la notion de lieu nous amène à comprendre le monde onirique, créé par ces deux auteurs, qui conserve une mémoire à la fois authentique et imaginaire

fRNC: Uncovering the dynamic and condition-specific RBP-ncRNA circuits from multi-omics data

The RNA binding protein (RBP) and non-coding RNA (ncRNA) interacting networks are increasingly recognized as the main mechanism in gene regulation, and are tightly associated with cellular malfunction and disease. Here, we present fRNC, a systems biology tool to uncover the dynamic spectrum of RBP-ncRNA circuits (RNC) by integrating transcriptomics, interactomics and proteomics data. fRNC constructs the RBP-ncRNA network derived from CLIP-seq or PARE experiments. Given scoring on nodes and edges according to differential analysis of expression data, it finds an RNC containing global maximum significant RBPs and ncRNAs. Alternatively, it can also capture the locally maximum scoring RNC according to user-defined starting nodes with the greedy search. When compared with existing tools, fRNC can detect more accurate and robust sub-network with scalability. As shown in the cases of esophageal carcinoma, breast cancer and Alzheimer’s disease, fRNC enables users to analyze the collective behaviors between RBP and the interacting ncRNAs, and reveal novel insights into the disease-associated processes. The fRNC R package is available at https://github.com/BioinformaticsSTU/fRNC

Hypervolume Niche Dynamics and Global Invasion Risk of <i>Phenacoccus solenopsis</i> under Climate Change

As a globally invasive quarantine pest, the cotton mealybug, Phenacoccus solenopsis, is spreading rapidly, posing serious threats against agricultural and forestry production and biosecurity. In recent years, the niche conservatism hypothesis has been widely debated, which is particularly evident in invasive biology research. Identifying the niche dynamics of P. solenopsis, as well as assessing its global invasion risk, is of both theoretical and practical importance. Based on 462 occurrence points and 19 bioclimatic variables, we used n-dimensional hypervolume analysis to quantify the multidimensional climatic niche of this pest in both its native and invasive ranges. We examined niche conservatism and further optimized the MaxEnt model parameters to predict the global invasion risk of P. solenopsis under both current and future climate conditions. Our findings indicated that the niche hypervolume of this pest in invasive ranges was significantly larger than that in its native ranges, with 99.45% of the niche differentiation contributed by niche expansion, with the remaining less than 1% explained by space replacement. Niche expansion was most evident in Oceania and Eurasia. The area under the receiver operating characteristic curve (0.83) and true skill statistic (0.62) indicated the model’s robust performance. The areas of suitable habitats for P. solenopsis are increasing significantly and the northward spread is obvious in future climate change scenarios. North Africa, northern China, Mediterranean regions, and northern Europe had an increased invasion risk of P. solenopsis. This study provided scientific support for the early warning and control of P. solenopsis.</i

Immune activity score to assess the prognosis, immunotherapy and chemotherapy response in gastric cancer and experimental validation

Background Gastric cancer (GC) is an extremely heterogeneous malignancy with a complex tumor microenvironment (TME) that contributes to unsatisfactory prognosis. Methods The overall activity score for assessing the immune activity of GC patients was developed based on cancer immune cycle activity index in the Tracking Tumor Immunophenotype (TIP). Genes potentially affected by the overall activity score were screened using weighted gene co-expression network analysis (WGCNA). Based on the expression profile data of GC in The Cancer Genome Atlas (TCGA) database, COX analysis was applied to create an immune activity score (IAS). Differences in TME activity in the IAS groups were analyzed. We also evaluated the value of IAS in estimating immunotherapy and chemotherapy response based on immunotherapy cohort. Gene expression in IAS model and cell viability were determined by real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) and Cell Counting Kit-8 (CCK-8) assay, respectively. Results WGCAN analysis screened 629 overall activity score-related genes, which were mainly associated with T cell response and B cell response. COX analysis identified AKAP5, CTLA4, LRRC8C, AOAH-IT1, NPC2, RGS1 and SLC2A3 as critical genes affecting the prognosis of GC, based on which the IAS was developed. Further RT-qPCR analysis data showed that the expression of AKAP5 and CTLA4 was downregulated, while that of LRRC8C, AOAH-IT1, NPC2, RGS1 and SLC2A3 was significantly elevated in GC cell lines. Inhibition of AKAP5 increased cell viability but siAOAH-IT1 promoted viability of GC cells. IAS demonstrated excellent robustness in predicting immunotherapy outcome and GC prognosis, with low-IAS patients having better prognosis and immunotherapy. In addition, resistance to Erlotinib, Rapamycin, MG-132, Cyclopamine, AZ628, and Sorafenib was reduced in patients with low IAS. Conclusion IAS was a reliable prognostic indicator. For GC patients, IAS showed excellent robustness in predicting GC prognosis, immune activity status, immunotherapy response, and chemotherapeutic drug resistance. Our study provided novel insights into the prognostic assessment in GC

Role of postoperative radiotherapy in dermatofibrosarcoma protuberans: a propensity score-matched analysis

Abstract Objective This study aimed to evaluate the role of postoperative radiotherapy (RT) in dermatofibrosarcoma protuberans (DFSP) and identify the prognostic factors influencing the disease-free survival (DFS). Methods A total of 184 patients with DFSP were analyzed from 2000 to 2016. The regression model was used to examine the prognostic factors for DFS. Baseline covariates were balanced using a propensity score model. The role of RT was assessed by comparing the DFS of the surgery + RT group with that of the surgery group. Results The median follow-up was 58 months (range, 6–203 months). The 5-year DFS rate was 89.8%. The univariate analysis showed that age ≥ 50 years, presence of fibrosarcoma, margins < 2 cm, and tumor size ≥5 cm were associated with worse DFS (P = 0.002, P <  0.001, P = 0.030, and P = 0.032, respectively). The multivariate Cox regression model revealed that age, margin width, lesion number, and histological subtype independently affected DFS. The Ki-67 expression was related to age and histological subtype. Patients with Ki-67 ≥ 17% showed a worse DFS than those with Ki-67 < 17% (35.8% vs 87.8%, P = 0.002). In the matched cohort, DFS was significantly higher in the S + RT group than in the S group (5-year DFS, 88.1% vs 56.2%, P = 0.044). Conclusions Age, margin width, lesion number, and histological subtype were independent risk factors for DFS in patients with DFSP. The high expression of Ki-67 could predict a poor prognosis. Postoperative RT could improve DFS for patients with DFSP