Dynamic Growth of Macroscopically Structured Supramolecular Hydrogels through Orchestrated Reaction-Diffusion.

Living organisms are capable of dynamically changing their structures for adaptive functions through sophisticated reaction-diffusion processes. Here we show how active supramolecular hydrogels with programmable lifetimes and macroscopic structures can be created by relying on a simple reaction-diffusion strategy. Two hydrogel precursors (poly(acrylic acid) PAA/CaCl2 and Na2 CO3 ) diffuse from different locations and generate amorphous calcium carbonate (ACC) nanoparticles at the diffusional fronts, leading to the formation of hydrogel structures driven by electrostatic interactions between PAA and ACC nanoparticles. Interestingly, the formed hydrogels are capable of autonomously disintegrating over time because of a delayed influx of electrostatic-interaction inhibitors (NaCl). The hydrogel growth process is well explained by a reaction-diffusion model which offers a theoretical means to program the dynamic growth of structured hydrogels. Furthermore, we demonstrate a conceptual access to dynamic information storage in soft materials using the developed reaction-diffusion strategy. This work may serve as a starting point for the development of life-like materials with adaptive structures and functionalities.

Transcriptome analyses shed light on floral organ morphogenesis and bract color formation in Bougainvillea.

BACKGROUND: Bougainvillea is a popular ornamental plant with brilliant color and long flowering periods. It is widely distributed in the tropics and subtropics. The primary ornamental part of the plant is its colorful and unusual bracts, rich in the stable pigment betalain. The developmental mechanism of the bracts is not clear, and the pathway of betalain biosynthesis is well characterized in Bougainvillea. RESULTS: At the whole-genome level, we found 23,469 protein-coding genes by assembling the RNA-Seq and Iso-Seq data of floral and leaf tissues. Genome evolution analysis revealed that Bougainvillea is related to spinach; the two diverged approximately 52.7 million years ago (MYA). Transcriptome analysis of floral organs revealed that flower development of Bougainvillea was regulated by the ABCE flower development genes; A-class, B-class, and E-class genes exhibited high expression levels in bracts. Eight key genes of the betalain biosynthetic pathway were identified by homologous alignment, all of which were upregulated concurrently with bract development and betalain accumulation during the bract initiation stage of development. We found 47 genes specifically expressed in stamens, including seven highly expressed genes belonging to the pentose and glucuronate interconversion pathways. BgSEP2b, BgSWEET11, and BgRD22 are hub genes and interacted with many transcription factors and genes in the carpel co-expression network. CONCLUSIONS: We assembled protein-coding genes of Bougainvilea, identified the floral development genes, and constructed the gene co-expression network of petal, stamens, and carpel. Our results provide fundamental information about the mechanism of flower development and pigment accumulation in Bougainvillea, and will facilitate breeding of cultivars with high ornamental value.

Cardiomyocyte orientation modulated by the Numb family proteins-N-cadherin axis is essential for ventricular wall morphogenesis.

The roles of cellular orientation during trabecular and ventricular wall morphogenesis are unknown, and so are the underlying mechanisms that regulate cellular orientation. Myocardial-specific Numb and Numblike double-knockout (MDKO) hearts display a variety of defects, including in cellular orientation, patterns of mitotic spindle orientation, trabeculation, and ventricular compaction. Furthermore, Numb- and Numblike-null cardiomyocytes exhibit cellular behaviors distinct from those of control cells during trabecular morphogenesis based on single-cell lineage tracing. We investigated how Numb regulates cellular orientation and behaviors and determined that N-cadherin levels and membrane localization are reduced in MDKO hearts. To determine how Numb regulates N-cadherin membrane localization, we generated an mCherry:Numb knockin line and found that Numb localized to diverse endocytic organelles but mainly to the recycling endosome. Consistent with this localization, cardiomyocytes in MDKO did not display defects in N-cadherin internalization but rather in postendocytic recycling to the plasma membrane. Furthermore, N-cadherin overexpression via a mosaic model partially rescued the defects in cellular orientation and trabeculation of MDKO hearts. Our study unravels a phenomenon that cardiomyocytes display spatiotemporal cellular orientation during ventricular wall morphogenesis, and its disruption leads to abnormal trabecular and ventricular wall morphogenesis. Furthermore, we established a mechanism by which Numb modulates cellular orientation and consequently trabecular and ventricular wall morphogenesis by regulating N-cadherin recycling to the plasma membrane.

Stockpiled Avian Influenza A(H7N9) Vaccines Induce Robust, Nonneutralizing Functional Antibodies Against Antigenically Drifted Fifth-Wave A(H7N9) Viruses.

Human infections caused by avian influenza A(H7N9) viruses have raised concerns of a pandemic. The capability of the current stockpiled A(H7N9) vaccines to induce cross-protective, nonneutralizing functional antibodies against antigenically drifted A(H7N9) viruses has not been evaluated before. Here we show that vaccination with either MF59- or AS03-adjuvanted inactivated A(H7N9) vaccines elicited robust, cross-reactive antibody-dependent cell-mediated cytotoxicity-mediating and neuraminidase-inhibiting functional antibodies against the antigenically drifted A(H7N9) viruses that emerged recently during the fifth-wave outbreak in China, including a highly pathogenic A(H7N9) human isolate. Such cross-reactive humoral immunity may provide vital first-line defense against fatal outcomes in case of an A(H7N9) pandemic.

Genome-wide profiling of the alternative splicing provides insights into development in Plutella xylostella.

BACKGROUND: The diamondback moth (DBM), Plutella xylostella (L.), is a major pest of cruciferous crops worldwide. While the species has become a model for genomics, post-transcriptional mechanisms associated with development and sex determination have not been comprehensively studied and the lack of complete structure of mRNA transcripts limits further research. RESULTS: Here, we combined the methods of single-molecule long-read sequencing technology (IsoSeq) and RNA-seq to re-annotate the published DBM genome and present the genome-wide identification of alternative splicing (AS) associated with development and sex determination of DBM. In total, we identified ~ 13,900 genes (~ 77%) annotated in the DBM genome (version-2), resulting in the correction of 1586 wrongly annotated genes and identification of 78,000 previously unannotated transcripts. We also identified 1804 genes showing alternative splicing (AS) in each of the developmental stages and sexes, suggesting that AS events are ubiquitous in DBM. Comparative analyses showed that these AS events were rarely shared among developmental stages, indicating that they may play key specific roles in regulation of insect development. Further, we found 156 genes showing different AS events and expression patterns between males and females, linking them to potential functions in sex determination. CONCLUSION: Overall, the P. xylostella transcriptome provides the significant information about regulatory alternative splicing events, which are shown to be involved in development and sex determination. Our work presents a solid foundation to better understand the mechanism of post-transcriptional regulation, and offers wider insights into insect development and sex determination.

Numb family proteins are essential for cardiac morphogenesis and progenitor differentiation.

Numb family proteins (NFPs), including Numb and numb-like (Numbl), are cell fate determinants for multiple progenitor cell types. Their functions in cardiac progenitor differentiation and cardiac morphogenesis are unknown. To avoid early embryonic lethality and study NFP function in later cardiac development, Numb and Numbl were deleted specifically in heart to generate myocardial double-knockout (MDKO) mice. MDKOs were embryonic lethal and displayed a variety of defects in cardiac progenitor differentiation, cardiomyocyte proliferation, outflow tract (OFT) and atrioventricular septation, and OFT alignment. By ablating NFPs in different cardiac populations followed by lineage tracing, we determined that NFPs in the second heart field (SHF) are required for OFT and atrioventricular septation and OFT alignment. MDKOs displayed an SHF progenitor cell differentiation defect, as revealed by a variety of methods including mRNA deep sequencing. Numb regulated cardiac progenitor cell differentiation in an endocytosis-dependent manner. Studies including the use of a transgenic Notch reporter line showed that Notch signaling was upregulated in the MDKO. Suppression of Notch1 signaling in MDKOs rescued defects in p57 expression, proliferation and trabecular thickness. Further studies showed that Numb inhibits Notch1 signaling by promoting the degradation of the Notch1 intracellular domain in cardiomyocytes. This study reveals that NFPs regulate trabecular thickness by inhibiting Notch1 signaling, control cardiac morphogenesis in a Notch1-independent manner, and regulate cardiac progenitor cell differentiation in an endocytosis-dependent manner. The function of NFPs in cardiac progenitor differentiation and cardiac morphogenesis suggests that NFPs might be potential therapeutic candidates for cardiac regeneration and congenital heart diseases.

Antibody to Influenza Virus Neuraminidase: An Independent Correlate of Protection.

BACKGROUND: Laboratory correlates of influenza vaccine protection can best be identified by examining people who are infected despite vaccination. While the importance of antibody to viral hemagglutinin (HA) has long been recognized, the level of protection contributed independently by antibody to viral neuraminidase (NA) has not been determined. METHODS: Sera from a controlled trial of the efficacies of inactivated influenza vaccine (IIV) and live attenuated influenza vaccine (LAIV) were tested by hemagglutination inhibition (HAI) assay, microneutralization (MN) assay, and a newly standardized lectin-based neuraminidase inhibition (NAI) assay. RESULTS: The NAI assay detected a vaccine response in 37% of IIV recipients, compared with 77% and 67% of participants in whom responses were detected by the HAI and MN assays, respectively. For LAIV recipients, the NAI, HAI, and MN assays detected responses in 6%, 21%, and 17%, respectively. In IIV recipients, as NAI assay titers rose, the frequency of infection fell, similar to patterns seen with HAI and MN assays. HAI and MN assay titers were highly correlated, but NAI assay titers exhibited less of a correlation. Analyses suggested an independent role for NAI antibody in protection, which was similar in the IIV, LAIV, and placebo groups. CONCLUSIONS: While NAI antibody is not produced to a large extent in response to current IIV, it appears to have an independent role in protection. As new influenza vaccines are developed, NA content should be considered. CLINICAL TRIALS REGISTRATION: NCT00538512.

Serum antibody response to matrix protein 2 following natural infection with 2009 pandemic influenza A(H1N1) virus in humans.

Natural infection-induced humoral immunity to matrix protein 2 (M2) of influenza A viruses in humans is not fully understood. Evidence suggests that anti-M2 antibody responses following influenza A virus infection are weak and/or transient. We show that the seroprevalence of anti-M2 antibodies increased with age in 317 serum samples from healthy individuals in the United States in 2007-2008. Infection with 2009 pandemic H1N1 influenza A virus (A[H1N1]pdm09) elicited a recall serum antibody response to M2 protein of A(H1N1)pdm09 in 47% of the affected 118 individuals tested. Anti-M2 antibody responses were more robust among individuals with preexisting antibodies to M2 protein. Moreover, the antibodies induced as a result of infection with A(H1N1)pdm09 were cross-reactive with M2 protein of seasonal influenza A viruses. These results emphasize the need to further investigate the possible roles of anti-M2 antibodies in human influenza A virus infection.

Integration of Encoding and Temporal Forecasting: Toward End-to-End NOx Prediction for Industrial Chemical Process.

Forecasting NOx concentration in fluid catalytic cracking (FCC) regeneration flue gas can guide the real-time adjustment of treatment devices, and then furtherly prevent the excessive emission of pollutants. The process monitoring variables, which are usually high-dimensional time series, can provide valuable information for prediction. Although process features and cross-series correlations can be captured through feature extraction techniques, they are commonly linear transformation, and conducted or trained separately from forecasting model. This process is inefficient and might not be an optimal solution for the following forecasting modeling. Therefore, we propose a time series encoding temporal convolutional network (TSE-TCN). By parameterizing the hidden representation of the encoding-decoding structure with the temporal convolutional network (TCN), and combining the reconstruction error and the prediction error in the objective function, the encoding-decoding procedure and the temporal predicting procedure can be trained by a single optimizer. The effectiveness of the proposed method is verified through an industrial reaction and regeneration process of an FCC unit. Results demonstrate that TSE-TCN outperforms some state-of-art methods with lower root mean square error (RMSE) by 2.74% and higher R2 score by 3.77%.

Large-Scale Data-Driven Optimization in Deep Modeling With an Intelligent Decision-Making Mechanism.

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies how specific design choices in the large-scale data-driven optimization and creation process affect the balance between the accuracy and effectiveness of the designed deep intelligent model. To this end, this study presents a novel architecture unit, which is termed as the "Activate-and-Freeze" block on popular and highly competitive datasets. In order to extract informative features by fusing spatial and channel-wise information together within local receptive fields and boost the representation power, this study constructs a Dense-attention module (pyramid spatial channel (PSC) attention) to perform feature recalibration, and through the PSC attention to model the interdependence among convolution feature channels. We join the PSC attention module in the activating and back-freezing strategy to search for one of the most important parts of the network for extraction and optimization. Experiments on various large-scale datasets demonstrate that the proposed method can achieve substantially better performance for improving the ConvNets representation power than the other state-of-the-art deep models.

N6-Methyladenosine Regulator-Mediated Methylation Modification Patterns with Distinct Prognosis, Oxidative Stress, and Tumor Microenvironment in Renal Cell Carcinoma.

OBJECTIVE: Emerging evidence suggests the biological implications of N6-methyladenosine (m6A) in carcinogenesis. Herein, we systematically analyzed the role of m6A modification in renal cell carcinoma (RCC) progression. METHODS: Based on 23 m6A regulators, unsupervised clustering analyses were conducted to determine m6A modification subtypes across 893 RCC specimens in the Cancer Genome Atlas (TCGA) cohort. By performing principal component analysis (PCA) analysis, m6A scoring system was developed for evaluating m6A modification patterns of individual RCC patients. The activity of signaling pathways was assessed by gene-set variation analysis (GSVA) algorithm. The single-sample gene set enrichment analysis (ssGSEA) algorithm was applied for quantifying the infiltration levels of immune cells and the activity of cancer immunity cycle. Drug responses were estimated by genomics of drug sensitivity in cancer (GDSC), the Cancer Therapeutics Response Portal (CTRP) and Preservice Research Institute for Science and Mathematics (PRISM) database. RESULTS: Five m6A modification subtypes were characterized by different survival outcomes, oxidative stress, cancer stemness, infiltrations of immune cells, activity of cancer immunity cycle, programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) expression and microsatellite instability (MSI) levels. According to m6A score, RCC patients were categorized into high and low m6A score groups. Patients with high m6A score displayed a prominent survival advantage, and the prognostic value of m6A score was confirmed in two anti-PD-1/PD-L1 immunotherapy cohorts. m6A score was significantly linked to oxidative stress-related genes, and high m6A score indicated the higher sensitivity to axitinib, pazopanib and sorafenib and the lower sensitivity to sunitinib. CONCLUSION: This study analyzed the extensive regulatory mechanisms of m6A modification on oxidative stress, the tumor microenvironment, and immunity. Quantifying m6A scores may enhance immunotherapeutic effects and assist in developing more effective agents.

An machine learning model to predict quality of life subtypes of disabled stroke survivors.

OBJECTIVE: Stroke causes serious physical disability with impaired quality of life (QoL) and heavy burden on health. The goal of this study is to explore the impaired QoL typologies and their predicting factors in physically disabled stroke survivors with machine learning approach. METHODS: Non-negative matrix factorization (NMF) was applied to clustering 308 physically disabled stroke survivors in rural China based on their responses on the short form 36 (SF-36) assessment of quality of life. Principal component analysis (PCA) was conducted to differentiate the subtypes, and the Boruta algorithm was used to identify the variables relevant to the categorization of two subtypes. A gradient boosting machine(GBM) and local interpretable model-agnostic explanation (LIME) algorithms were used to apply to interpret the variables that drove subtype predictions. RESULTS: Two distinct subtypes emerged, characterized by short form 36 (SF-36) domains. The feature difference between worsen QoL subtype and better QoL subtype was as follows: role-emotion (RE), body pain (BP) and general health (GH), but not physical function (PF); the most relevant predictors of worsen QoL subtypes were help from others, followed by opportunities for community activity and rehabilitation needs, rather than disability severity or duration since stroke. INTERPRETATION: The results suggest that the rehabilitation programs should be tailored toward their QoL clustering feature; body pain and emotional-behavioral problems are more crucial than motor deficit; stroke survivors with worsen QoL subtype are most in need of social support, return to community, and rehabilitation.

Multi-omics analysis uncovers clinical, immunological, and pharmacogenomic implications of cuproptosis in clear cell renal cell carcinoma.

OBJECTIVE: The latest research proposed a novel copper-dependent programmed cell death named cuproptosis. We aimed to elucidate the influence of cuproptosis in clear cell renal cell carcinoma (ccRCC) from a multi-omic perspective. METHODS: This study systematically assessed mRNA expression, methylation, and genetic alterations of cuproptosis genes in TCGA ccRCC samples. Through unsupervised clustering analysis, the samples were classified as different cuproptosis subtypes, which were verified through NTP method in the E-MTAB-1980 dataset. Next, the cuproptosis score (Cuscore) was computed based on cuproptosis-related genes via PCA. We also evaluated clinical and immunogenomic features, drug sensitivity, immunotherapeutic response, and post-transcriptional regulation. RESULTS: Cuproptosis genes presented multi-layer alterations in ccRCC, and were linked with patients' survival and immune microenvironment. We defined three cuproptosis subtypes [C1 (moderate cuproptosis), C2 (low cuproptosis), and C3 (high cuproptosis)], and the robustness and reproducibility of this classification was further proven. Overall survival was best in C3, moderate in C1, and worst in C2. C1 had the highest sensitivity to pazopanib, and sorafenib, while C2 was most sensitive to sunitinib. Furthermore, C1 patients benefited more from anti-PD-1 immunotherapy. Patients with high Cuscore presented the notable survival advantage. Cuscore was highly linked with immunogenomic features, and post-transcriptional events that contributed to ccRCC development. Finally, several potential compounds and druggable targets (NMU, RARRES1) were selected for low Cuscore group. CONCLUSION: Overall, our study revealed the non-negligible role of cuproptosis in ccRCC development. Evaluation of the cuproptosis subtypes improves our cognition of immunogenomic features and better guides personalized prognostication and precision therapy.

Deep Adaptive Fuzzy Clustering for Evolutionary Unsupervised Representation Learning.

Cluster assignment of large and complex datasets is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the resampled data from estimated bottleneck space have consistent clustering properties to improve the deep clustering model progressively. Experiments on various datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments.

Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes.

Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling T2 statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.

Inflammatory response-based prognostication and personalized therapy decisions in clear cell renal cell cancer to aid precision oncology.

OBJECTIVE: The impact of inflammatory response on tumor development and therapeutic response is of significant importance in clear cell renal cell carcinoma (ccRCC). The customization of specialized prognostication approaches and the exploration of supplementary treatment options hold critical clinical implications in relation to the inflammatory response. METHODS: In the present study, unsupervised clustering was implemented on TCGA-KIRC tumors using transcriptome profiles of inflammatory response genes, which was then validated in two ccRCC datasets (E-MATB-1980 and ICGC) and two immunotherapy datasets (IMvigor210 and Liu et al.) via SubMap and NTP algorithms. Combining co-expression and LASSO analyses, inflammatory response-based scoring system was defined, which was evaluated in pan-cancer. RESULTS: Three reproducible inflammatory response subtypes (named IR1, IR2 and IR3) were determined and independently verified, each exhibiting distinct molecular, clinical, and immunological characteristics. Among these subtypes, IR2 had the best OS outcomes, followed by IR3 and IR1. In terms of anti-angiogenic agents, sunitinib may be appropriate for IR1 patients, while axitinib and pazopanib may be suitable for IR2 patients, and sorafenib for IR3 patients. Additionally, IR1 patients might benefit from anti-CTLA4 therapy. A scoring system called IRscore was defined for individual ccRCC patients. Patients with high IRscore presented a lower response rate to anti-PD-L1 therapy and worse prognostic outcomes. Pan-cancer analysis demonstrated the immunological features and prognostic relevance of the IRscore. CONCLUSION: Altogether, characterization of inflammatory response subtypes and IRscore provides a roadmap for patient risk stratification and personalized treatment decisions, not only in ccRCC, but also in pan-cancer.

Cross-reactive antibody responses to the 2009 pandemic H1N1 influenza virus.

BACKGROUND: A new pandemic influenza A (H1N1) virus has emerged, causing illness globally, primarily in younger age groups. To assess the level of preexisting immunity in humans and to evaluate seasonal vaccine strategies, we measured the antibody response to the pandemic virus resulting from previous influenza infection or vaccination in different age groups. METHODS: Using a microneutralization assay, we measured cross-reactive antibodies to pandemic H1N1 virus (2009 H1N1) in stored serum samples from persons who either donated blood or were vaccinated with recent seasonal or 1976 swine influenza vaccines. RESULTS: A total of 4 of 107 persons (4%) who were born after 1980 had preexisting cross-reactive antibody titers of 40 or more against 2009 H1N1, whereas 39 of 115 persons (34%) born before 1950 had titers of 80 or more. Vaccination with seasonal trivalent inactivated influenza vaccines resulted in an increase in the level of cross-reactive antibody to 2009 H1N1 by a factor of four or more in none of 55 children between the ages of 6 months and 9 years, in 12 to 22% of 231 adults between the ages of 18 and 64 years, and in 5% or less of 113 adults 60 years of age or older. Seasonal vaccines that were formulated with adjuvant did not further enhance cross-reactive antibody responses. Vaccination with the A/New Jersey/1976 swine influenza vaccine substantially boosted cross-reactive antibodies to 2009 H1N1 in adults. CONCLUSIONS: Vaccination with recent seasonal nonadjuvanted or adjuvanted influenza vaccines induced little or no cross-reactive antibody response to 2009 H1N1 in any age group. Persons under the age of 30 years had little evidence of cross-reactive antibodies to the pandemic virus. However, a proportion of older adults had preexisting cross-reactive antibodies.

Learning of Iterative Learning Control for Flexible Manufacturing of Batch Processes.

Flexible manufacturing as an essential component of smart manufacturing implements the customized production mode, thereby requesting fast controller adaptation for producing different goods but still with high precision. This problem becomes even more acute for batch processes. Here we present a solution called learning of iterative learning control (ILC) based on neural networks. It is able to recommend control parameters for ILC controllers accordingly, so as to yield fast tracking error convergence and smaller steady-state error for disparate set-point profiles, which is deemed an abstraction of different production needs. The method substantially outperforms a benchmark ILC on a variety of systems and cases, thereby showing its potential for deployment in the industrial Internet of Things.

Alternative splicing and alternative polyadenylation define tumor immune microenvironment and pharmacogenomic landscape in clear cell renal carcinoma.

Two major posttranscriptional mechanisms-alternative splicing (AS) and alternative polyadenylation (APA)-have attracted much attention in cancer research. Nevertheless, their roles in clear cell renal carcinoma (ccRCC) are still ill defined. Herein, this study was conducted to uncover the implications of AS and APA events in ccRCC progression. Through consensus molecular clustering analysis, two AS or APA RNA processing phenotypes were separately constructed with distinct prognosis, tumor-infiltrating immune cells, responses to immunotherapy, and chemotherapy. The AS or APA score was constructed to quantify AS or APA RNA processing patterns of individual ccRCCs with principal-component analysis. Both high AS and APA scores were characterized by undesirable survival outcomes, relatively high response to immunotherapy, and low sensitivity to targeted drugs, such as sorafenib and pazopanib. Moreover, several small molecular compounds were predicted for patients with a high AS or APA score. There was a positive correlation between AS and APA scores. Their interplay contributed to poor prognosis and reshaped the tumor immune microenvironment. Collectively, this study is the first to comprehensively analyze two major posttranscriptional events in ccRCC. Our findings uncovered the potential functions of AS and APA events and identified their therapeutic potential in immunotherapy and targeted therapy.