Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma.

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.

AIMS: In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early-stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering the sudden death of HCM, developing a novel diagnostic model to clarify the patients with early-stage HCM and the immunological characteristics can avoid misdiagnosis and attenuate disease progression. METHODS AND RESULTS: Three hundred eighty-five samples from four independent cohorts were systematically retrieved. The weighted gene co-expression network analysis, differential expression analysis (|log2(foldchange)| > 0.5 and adjusted P < 0.05), and protein-protein interaction network were sequentially performed to identify HCM-related hub genes. With a machine learning algorithm, the least absolute shrinkage and selection operator regression algorithm, a stable diagnostic model was developed. The immune-cell infiltration and biological functions of HCM were also explored to characterize its underlying pathogenic mechanisms and the immune signature. Two key modules were screened based on weighted gene co-expression network analysis. Pathogenic mechanisms relevant to extracellular matrix and immune pathways have been discovered. Twenty-seven co-regulated genes were recognized as HCM-related hub genes. Based on the least absolute shrinkage and selection operator algorithm, a stable HCM diagnostic model was constructed, which was further validated in the remaining three cohorts (n = 385). Considering the tight association between HCM and immune-related functions, we assessed the infiltrating abundance of various immune cells and stromal cells based on the xCell algorithm, and certain immune cells were significantly different between high-risk and low-risk groups. CONCLUSIONS: Our study revealed a number of hub genes and novel pathways to provide potential targets for the treatment of HCM. A stable model was developed, providing an efficient tool for the diagnosis of HCM.

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma.

BACKGROUND: Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD). METHODS: Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS). RESULTS: This study identified TIME-related lncRNAs from integrated analysis of single­cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy. CONCLUSIONS: This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

Crosstalk of cell death pathways unveils an autophagy-related gene AOC3 as a critical prognostic marker in colorectal cancer.

The intricate crosstalk of various cell death forms was recently implicated in cancers, laying a foundation for exploring the association between cell death and cancers. Recent evidence has demonstrated that biological networks outperform snapshot gene expression profiles at discovering promising biomarkers or heterogenous molecular subtypes across different cancer types. In order to investigate the behavioral patterns of cell death-related interaction perturbation in colorectal cancer (CRC), this study constructed the interaction-perturbation network with 11 cell death pathways and delineated four cell death network (CDN) derived heterogeneous subtypes (CDN1-4) with distinct molecular characteristics and clinical outcomes. Specifically, we identified a subtype (CDN4) endowed with high autophagy activity and the worst prognosis. Furthermore, AOC3 was identified as a potential autophagy-related biomarker, which demonstrated exceptional predictive performance for CDN4 and significant prognostic value. Overall, this study sheds light on the complex interplay of various cell death forms and reveals an autophagy-related gene AOC3 as a critical prognostic marker in CRC.

Corrigendum: Pyruvate kinase M2 promotes prostate cancer metastasis through regulating ERK1/2-COX-2 signaling.

[This corrects the article DOI: 10.3389/fonc.2020.544288.].

Assessment of isthmus filling using two obturation techniques performed by students with different levels of clinical experience.

Background/purpose: Root canal filling is a necessary skill for dental students and an important aspect of endodontic education. This study aimed to evaluate the effect of students' clinical experiences on isthmus filling using different techniques and sealers. Materials and methods: One hundred eight three-dimensional-printed resin replicas of isthmus were divided into six groups and either continuous wave of condensation (CWC) or single-cone obturation (SC) was performed. One of three sealers (AH Plus Jet®, GuttaFlow2, iRoot SP) was used together with a size-fitted gutta-percha master cone. All the obturations were completed by students with three different levels of clinical experience including senior postgraduate students (SPS), junior postgraduate students (JPS), and undergraduate students (US). The percentages of filled areas (PFA) at 2, 4, 6, and 8 mm from the apex were analyzed using a light microscope. Data were analyzed using the Mann-Whitney U test or Kruskal-Wallis 1-way ANOVA with Dunn's tests (α = 0.05). Results: The CWC group exhibited a higher PFA than the SC group (P < 0.05). The PFA was higher in the SPS group than in the JPS group or the US group with CWC (P < 0.05). The three clinical experience groups showed similar PFAs with SC (P > 0.05); however, when using SC with iRoot SP, the PFA was higher than with either of the other two sealers (P < 0.05). Conclusion: CWC was found to be technique-sensitive and required clinical training. With SC, clinical experience did not improve the quality of isthmus filling without additional training. CWC was superior to SC for type IV isthmuses. When using SC, better filling quality was obtained with a bioceramic sealer.

Monitoring of Sweat Ions and Physiological Parameters via a Reconfigurable Modular System.

In recent years, wearable sensors have revolutionized health monitoring by enabling continuous, real-time tracking of human health and performance. These noninvasive devices are usually designed to monitor human physical state and biochemical markers. However, enhancing their functionalities often demands intricate customization by designers and additional expenses for users. Here, we present a strategy using assembled modular circuits to customize health monitoring wearables. The modular circuits can be effortlessly reconfigured to meet various specific requirements, facilitating the incorporation of diverse functions at a lower cost. To validate this approach, modular circuits were employed to develop four distinct systems for in vitro evaluations. These systems enabled the detection of sweat biomarkers and physical signals under various scenarios, including sedentary state, exercise, and daily activities with or without incorporating iontophoresis to induce sweat. Four key sweat markers (K+, Ca2+, Na+, and pH) and three essential physical indicators (heart rate, blood oxygen levels, and skin temperature) are selected as the detection targets. Commercial methods were also used to evaluate the potential for effective health monitoring with our technique. This reconfigurable modular wearable (ReModuWear) system promises to provide more easy-to-use and comprehensive health assessments. Additionally, it may contribute to environmental sustainability by reusing modules.

Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer.

Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.

Immune perturbation network identifies an EMT subtype with chromosomal instability and tumor immune-desert microenvironment.

Most gastric cancer (GC) subtypes are identified through transcriptional profiling overlooking dynamic changes and interactions in gene expression. Based on the background network of global immune genes, we constructed sample-specific edge-perturbation matrices and identified four molecular network subtypes of GC (MNG). MNG-1 displayed the best prognosis and vigorous cell cycle activity. MNG-2 was enriched by immune-hot phenotype with the potential for immunotherapy response. MNG-3 and MNG-4 were identified with epithelial-mesenchymal transition (EMT) peculiarity and worse prognosis, termed EMT subtypes. MNG-3 was characterized by low mutational burden and stromal cells and considered a replica of previous subtypes associated with poor prognosis. Notably, MNG-4 was considered a previously undefined subtype with a dismal prognosis, characterized by chromosomal instability and immune-desert microenvironment. This subtype tended to metastasize and was resistant to respond to immunotherapy. Pharmacogenomics analysis showed three therapeutic agents (NVP-BEZ235, LY2606368, and rutin) were potential interventions for MNG-4.

Multi-center validation of an immune-related lncRNA signature for predicting survival and immune status of patients with renal cell carcinoma: an integrating machine learning-derived study.

BACKGROUND: Long noncoding RNAs (lncRNAs) have been reported to play an important role in tumor immune modification. Nonetheless, the clinical implication of immune-associated lncRNAs in renal cell carcinoma (RCC) remains to be further explored. METHODS: 76 combinations of machine learning algorithms were integrated to develop and validate a machine learning-derived immune-related lncRNA signature (MDILS) in five independent cohorts (n = 801). We collected 28 published signatures and collated clinical variables for comparison with MDILS to verify its efficacy. Subsequently, molecular mechanisms, immune status, mutation landscape, and pharmacological profile were further investigated in different stratified patients. RESULTS: Patients with high MDILS displayed worse overall survival than those with low MDILS. The MDILS could independently predict overall survival and convey robust performance across five cohorts. MDILS has a significantly better performance compared with traditional clinical variables and 28 published signatures. Patients with low MDILS exhibited more abundant immune infiltration and higher potency of immunotherapeutic response, while patients with high MDILS might be more sensitive to multiple chemotherapeutic drugs (e.g., sunitinib and axitinib). CONCLUSION: MDILS is a robust and promising tool to facilitate clinical decision-making and precision treatment of RCC.

Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma.

Suppressor of cytokine signalling (SOCS) 1/2/3/4 are involved in the occurrence and progression of multiple malignancies; however, their prognostic and developmental value in patients with glioblastoma (GBM) remains unclear. The present study used TCGA, ONCOMINE, SangerBox3.0, UALCAN, TIMER2.0, GENEMANIA, TISDB, The Human Protein Atlas (HPA) and other databases to analyse the expression profile, clinical value and prognosis of SOCS1/2/3/4 in GBM, and to explore the potential development mechanism of action of SOCS1/2/3/4 in GBM. The majority of analyses showed that SOCS1/2/3/4 transcription and translation levels in GBM tissues were significantly higher than those in normal tissues. qRT-PCR, western blotting (WB) and immunohistochemical staining were used to verify that SOCS3 was expressed at higher mRNA and protein levels in GBM than in normal tissues or cells. High SOCS1/2/3/4 mRNA expression was associated with poor prognosis in patients with GBM, especially SOCS3. SOCS1/2/3/4 were highly contraindicated, which had few mutations, and were not associated with clinical prognosis. Furthermore, SOCS1/2/3/4 were associated with the infiltration of specific immune cell types. In addition, SOCS3 may affect the prognosis of patients with GBM through JAK/STAT signalling pathway. Analysis of the GBM-specific protein interaction (PPI) network showed that SOCS1/2/3/4 were involved in multiple potential carcinogenic mechanisms of GBM. In addition, colony formation, Transwell, wound healing and western blotting assays revealed that inhibition of SOCS3 decreased the proliferation, migration and invasion of GBM cells. In conclusion, the present study elucidated the expression profile and prognostic value of SOCS1/2/3/4 in GBM, which may provide potential prognostic biomarkers and therapeutic targets for GBM, especially SOCS3.

Impact of a staggered scaffold structure on the mechanical properties and cell response in bone tissue engineering.

The primary goal of bone tissue engineering is to fabricate scaffolds that can provide a microenvironment similar to that of natural bone. Therefore, various scaffolds have been designed to replicate the bone structure. Although most tissues exhibit complicated structures, their basic structural unit includes stiff platelets arranged in a staggered micro-array. Therefore, many researchers have designed scaffolds with staggered patterns. However, relatively few studies have comprehensively analyzed this type of scaffold. In this review, we have analyzed scientific research pertaining to staggered scaffold designs and summarized their effects on the physical and biological properties of scaffolds. Compression tests or finite element analysis are typically used to evaluate the mechanical properties of scaffolds, and most studies have performed experiments in cell cultures. Staggered scaffolds improve mechanical strength and are beneficial for cell attachment, proliferation, and differentiation in comparison with conventional designs. However, very few have been studied in vivo experiments. Additionally, studies on the effect of staggered structures on angiogenesis or bone regeneration in vivo, particularly in large animals, are required. Currently, with the prevalence of artificial intelligence (AI)-based technologies, highly optimized models can be developed, resulting in better discoveries. In the future, AI can be used to deepen our understanding on the staggered structure, promoting its use in clinical applications.

Systematic analysis of transcriptome signature for improving outcomes in lung adenocarcinoma.

PURPOSE: The updated guidelines highlight gene expression-based multigene panel as a critical tool to assess overall survival (OS) and improve treatment for lung adenocarcinoma (LUAD) patients. Nevertheless, genome-wide expression signatures are still limited in real clinical utility because of insufficient data utilization, a lack of critical validation, and inapposite machine learning algorithms. METHODS: 2330 primary LUAD samples were enrolled from 11 independent cohorts. Seventy-six algorithm combinations based on ten machine learning algorithms were applied. A total of 108 published gene expression signatures were collected. Multiple pharmacogenomics databases and resources were utilized to identify precision therapeutic drugs. RESULTS: We comprehensively developed a robust machine learning-derived genome-wide expression signature (RGS) according to stably OS-associated RNAs (OSRs). RGS was an independent risk element and remained robust and reproducible power by comparing it with general clinical parameters, molecular characteristics, and 108 published signatures. RGS-based stratification possessed different biological behaviors, molecular mechanisms, and immune microenvironment patterns. Integrating multiple databases and previous studies, we identified that alisertib was sensitive to the high-risk group, and RITA was sensitive to the low-risk group. CONCLUSION: Our study offers an appealing platform to screen dismal prognosis LUAD patients to improve clinical outcomes by optimizing precision therapy.

Epigenetically regulated gene expression profiles decipher four molecular subtypes with prognostic and therapeutic implications in gastric cancer.

BACKGROUND: Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract which seriously endangers the health of human beings worldwide. Transcriptomic deregulation by epigenetic mechanisms plays a crucial role in the heterogeneous progression of GC. This study aimed to investigate the impact of epigenetically regulated genes on the prognosis, immune microenvironment, and potential treatment of GC. RESULTS: Under the premise of verifying significant co-regulation of the aberrant frequencies of microRNA (miRNA) correlated (MIRcor) genes and DNA methylation-correlated (METcor) genes. Four GC molecular subtypes were identified and validated by comprehensive clustering of MIRcor and METcor GEPs in 1521 samples from five independent multicenter GC cohorts: cluster 1 was characterized by up-regulated cell proliferation and transformation pathways, with good prognosis outcomes, driven by mutations, and was sensitive to 5-fluorouracil and paclitaxel; cluster 2 performed moderate prognosis and benefited more from apatinib and cisplatin; cluster 3 was featured by an up-regulated ligand-receptor formation-related pathways, poor prognosis, an immunosuppression phenotype with low tumor purity, resistant to chemotherapy (e.g., 5-fluorouracil, paclitaxel, and cisplatin), and targeted therapy drug (apatinib) and sensitive to dasatinib; cluster 4 was characterized as an immune-activating phenotype, with advanced tumor stages, benefit more from immunotherapy and displayed worst prognosis. CONCLUSIONS: According to the epigenetically regulated GEPs, we developed four robust GC molecular subtypes, which facilitated the understanding of the epigenetic mechanisms underlying GC heterogeneity, offering an optimized decision-making and surveillance platform for GC patients.

Multi-omics analyses of CD276 in pan-cancer reveals its clinical prognostic value in glioblastoma and other major cancer types.

BACKGROUND: CD276 (also known as B7-H3) is one of the most important immune checkpoints of the CD28 and B7 superfamily, and its abnormal expression is closely associated with various types of cancer. It has been shown that CD276 is able to inhibit the function of T cells, and that this gene may potentially be a promising immunotherapy target for different types of cancer. METHODS: Since few systematic studies have been published on the role of CD276 in cancer to date, the present study has employed single-cell sequencing and bioinformatics methods to analyze the expression patterns, clinical significance, prognostic value, epigenetic alterations, DNA methylation level, tumor immune cell infiltration and immune functions of CD276 in different types of cancer. In order to analyze the potential underlying mechanism of CD276 in glioblastoma (GBM) to assess its prognostic value, the LinkedOmics database was used to explore the biological function and co-expression pattern of CD276 in GBM, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. In addition, a simple validation of the above analyses was performed using reverse transcription-quantitative (RT-q)PCR assay. RESULTS: The results revealed that CD276 was highly expressed, and was often associated with poorer survival and prognosis, in the majority of different types of cancer. In addition, CD276 expression was found to be closely associated with T cell infiltration, immune checkpoint genes and immunoregulatory interactions between lymphoid and a non-lymphoid cell. It was also shown that the CD276 expression network exerts a wide influence on the immune activation of GBM. The expression of CD276 was found to be positively correlated with neutrophil-mediated immunity, although it was negatively correlated with the level of neurotransmitters, neurotransmitter transport and the regulation of neuropeptide signaling pathways in GBM. It is noteworthy that CD276 expression was found to be significantly higher in GBM compared with normal controls according to the RT-qPCR analysis, and the co-expression network, biological function and chemotherapeutic drug sensitivity of CD276 in GBM were further explored. In conclusion, the findings of the present study have revealed that CD276 is strongly expressed and associated with poor prognosis in most types of cancer, including GBM, and its expression is strongly associated with T-cell infiltration, immune checkpoint genes, and immunomodulatory interactions between lymphocytes and non-lymphoid cells. CONCLUSIONS: Taken together, based on our systematic analysis, our findings have revealed important roles for CD276 in different types of cancers, especially GBM, and CD276 may potentially serve as a biomarker for cancer.

Machine learning algorithm-generated and multi-center validated melanoma prognostic signature with inspiration for treatment management.

BACKGROUND: Although immunotherapy and targeted treatments have dramatically improved the survival of melanoma patients, the intra- or intertumoral heterogeneity and drug resistance have hindered the further expansion of clinical benefits. METHODS: The 96 combination frames constructed by ten machine learning algorithms identified a prognostic consensus signature based on 1002 melanoma samples from nine independent cohorts. Clinical features and 26 published signatures were employed to compare the predictive performance of our model. RESULTS: A machine learning-based prognostic signature (MLPS) with the highest average C-index was developed via 96 algorithm combinations. The MLPS has a stable and excellent predictive performance for overall survival, superior to common clinical traits and 26 collected signatures. The low MLPS group with a better prognosis had significantly enriched immune-related pathways, tending to be an immune-hot phenotype and possessing potential immunotherapeutic responses to anti-PD-1, anti-CTLA-4, and MAGE-A3. On the contrary, the high MLPS group with more complex genomic alterations and poorer prognoses is more sensitive to the BRAF inhibitor dabrafenib, confirmed in patients with BRAF mutations. CONCLUSION: MLPS could independently and stably predict the prognosis of melanoma, considered a promising biomarker to identify patients suitable for immunotherapy and those with BRAF mutations who would benefit from dabrafenib.

Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer.

Molecular subtypes of colorectal cancer (CRC) are currently identified via the snapshot transcriptional profiles, largely ignoring the dynamic changes of gene expressions. Conversely, biological networks remain relatively stable irrespective of time and condition. Here, we introduce an individual-specific gene interaction perturbation network-based (GIN) approach and identify six GIN subtypes (GINS1-6) with distinguishing features: (i) GINS1 (proliferative, 24%~34%), elevated proliferative activity, high tumor purity, immune-desert, PIK3CA mutations, and immunotherapeutic resistance; (ii) GINS2 (stromal-rich, 14%~22%), abundant fibroblasts, immune-suppressed, stem-cell-like, SMAD4 mutations, unfavorable prognosis, high potential of recurrence and metastasis, immunotherapeutic resistance, and sensitive to fluorouracil-based chemotherapy; (iii) GINS3 (KRAS-inactivated, 13%~20%), high tumor purity, immune-desert, activation of EGFR and ephrin receptors, chromosomal instability (CIN), fewer KRAS mutations, SMOC1 methylation, immunotherapeutic resistance, and sensitive to cetuximab and bevacizumab; (iv) GINS4 (mixed, 10%~19%), moderate level of stromal and immune activities, transit-amplifying-like, and TMEM106A methylation; (v) GINS5 (immune-activated, 12%~24%), stronger immune activation, plentiful tumor mutation and neoantigen burden, microsatellite instability and high CpG island methylator phenotype, BRAF mutations, favorable prognosis, and sensitive to immunotherapy and PARP inhibitors; (vi) GINS6, (metabolic, 5%~8%), accumulated fatty acids, enterocyte-like, and BMP activity. Overall, the novel high-resolution taxonomy derived from an interactome perspective could facilitate more effective management of CRC patients.

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer.

As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.