Pancreatic ductal adenocarcinoma (PDAC) carries a poor prognosis largely due to lack of efficient diagnostic means. We applied mass spectrometry-based high-coverage plasma proteome analysis accompanying with machine learning to develop a 5-protein diagnostic model: SNCA, GCLC, LBP, ALAD, and SORD. For differentiating PDAC from healthy controls (HCs), this model reached an area under the curve (AUC) of 0.973 with 100% sensitivity and 85% specificity in the discovery cohort, with nested cross-validation confirming robust performance (AUC = 0.958). Further validation centered on SNCA achieved an AUC of 0.835 in an independent validation cohort. SNCA also showed good diagnostic performance in PDAC patients with low CA19-9 level (AUC = 0.868), underscoring its potential value for this subgroup. Overall, these findings indicate SNCA as a promising candidate plasma diagnostic marker for PDAC.

Niemann-Pick disease type C protein 1 (NPC1), classically associated with cholesterol transport and viral entry, has an emerging role in cancer biology. Here, we demonstrate that knockout of Npc1 in hepatocytes attenuates hepatocellular carcinoma (HCC) progression in both DEN (diethylnitrosamine)-CCl4 induced and MYC-driven HCC mouse models. Mechanistically, NPC1 significantly promotes HCC progression by modulating the TGF-β pathway, independent of its traditional role in cholesterol transport. We identify that the 692-854 amino acid region of NPC1's transmembrane domain is critical for its interaction with TGF-β receptor type-1 (TGFBR1). This interaction prevents the binding of SMAD7 and SMAD ubiquitylation regulatory factors (SMURFs) to TGFBR1, reducing TGFBR1 ubiquitylation and degradation, thus enhancing its stability. Notably, the NPC1 (P691S) mutant, which is defective in cholesterol transport, still binds TGFBR1, underscoring a cholesterol-independent mechanism. These findings highlight a cholesterol transport-independent mechanism by which NPC1 contributes to the stability of TGFBR1 in HCC and suggest potential therapeutic strategies targeting NPC1 for HCC treatment.

Hepatocellular carcinoma (HCC) is the third deadliest cancer in the world, however, the mechanisms of genesis, development and recurrence of HCC are not fully clarified yet. Previous studies on phosphoproteome in large-scale of HCC merely investigated the specific regulation on protein phosphorylation levels and identified subgroups consistent with proteomic subgroups. Here, we systematically interrogated the phosphoproteome of early-stage HCC and uncovered the dysregulated signaling pathways and kinases. Unlike previous studies on mRNA and protein levels, we observed a significant decline in phosphorylation regulation of EGFR-mediated canonical ERK/MAPK signaling alongside elevation of atypical MAPK signaling. Classification based on the heterogeneity of the phosphoproteome in tumor identified three phosphoproteomic subtypes, each characterized by distinct phosphorylation signatures, clinical characteristics, cellular signaling pathways and kinase activities. Further validation revealed hyperphosphorylation of SRSF3, one signature in the most malignant subtype, promote proliferation and metastasis of HCC cells. The mechanism of which was further demonstrated to be coordinated by activation of SRPK1/CLKs and potential suppression of PP1 phosphatase via phosphorylation of PPP1R7. Furthermore, inhibitors targeting SRPK1 or CLKs effectively suppressed hyperphosphorylation of SRSF3 mediated migration and invasion in HCC. Our study uncover the dysregulated phosphorylation signatures, offer new insights into the tumor heterogeneity, and identify phosphorylation regulating mechanism and corresponding key kinases with therapeutic value in early-stage HCC, which also spotlight the importance of protein phosphorylation in cancer progression. ### Competing Interest Statement The authors have declared no competing interest.

BackgroundAnti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis (MDA5+DM) is associated with poor prognosis and high mortality, presenting significant challenges for treating this intractable disease. This study aimed to compare the efficacy and safety of calcineurin inhibitor monotherapy (CNI) versus combination therapy [CNI and tofacitinib (TOF) or cyclophosphamide (CTX)] as initial immunosuppressive regimens for MDA5+DM.MethodsIn this retrospective observational study, MDA5+DM patients from the First Affiliated Hospital of Zhengzhou University between August 2019 and June 2024 were included. Patients were categorized into three groups according to the different immunosuppressive regimens. One-year mortality and the potential risk factors for death was analyzed using the Kaplan-Meier survival analysis and Cox proportional hazards regression, respectively.ResultsA total of 152 patients were divided into CNI group(n=49, 32.2%), CNI+TOF group(n=52, 34.2%), and CNI+CTX group(n=51, 33.6%). The 1-year survival rate was significantly lower in the CNI group compared to in the combination therapy groups (logrank P = 0.032). However, the CNI+CTX group showed a higher overall infection rate compared to CNI and CNI+TOF group (51.0% vs 28.6% vs 32.7%, p=0.048). Multivariate analysis identified combination therapy and higher CD8+ T cells act as protective factors, whereas co-infection is a major predictor of mortality.ConclusionsIn our study, combination therapy may improve survival prognosis in MDA5+ DM patients. Nevertheless, vigilant monitoring for opportunistic infections during treatment is essential.

To investigate the efficacy and safety of belimumab in the treatment of systemic lupus erythematosus (SLE) in a real-world setting and provide a valuable reference for clinical treatment. In this retrospective study, 101 patients with SLE who came to our hospital from March 2020 to September 2022, 56 of whom with lupus nephritis (LN), were selected. All patients received belimumab in combination with standard of care(SoC)therapy regimen for more than 52 weeks and their clinical/laboratory data, assessment of disease activity, glucocorticoids dosage and occurrence of adverse events were recorded. Lupus Low Disease Activity State (LLDAS) and DORIS remission as a primary goal in the treatment of SLE. The groups were classified according to the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2 K): SLEDAI-2 K < 6 was categorized as the mild group (mild activity) and SLEDAI-2 K ≥ 6 was categorized as the active group (moderate-severe activity). The disease of the two groups mentioned above were assessed using the SELENA-SLEDAI Flare Index (SFI) and the SLE Responder Index-4 (SRI-4), respectively. Furthermore, we used complete remission (CR) and partial remission (PR) in the kidney as the standard for efficacy evaluation for LN patients. After 52 weeks of treatment with belimumab, patients’ complement levels increased significantly (p < 0.05); Other indicators such as 24-hour urine protein quantification and daily glucocorticoids dose decreased compared to pretreatment (p < 0.05). At 52 weeks, (i) after evaluation, the whole group of patients showed significant improvement in their condition; (ii) 55.4

BackgroundAnti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis (anti-MDA5+DM) patients are associated with considerable mortality, and opportunistic infections including Pneumocystis jirovecii pneumonia (PJP)is the main cause. This study was to identify clinical characteristics, risk factors, and prognostic factors of PJP diagnosed by bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) in anti-MDA5+ DM patients.MethodsIn this retrospective observational study, all patients admitted with suspected pneumonia were detected for mNGS in BALF. The demographics, comorbidities, laboratory parameters, and treatments of the patients were compared and analyzed in both groups to identify the potential risk factors for PJP and death via Logistic regression and Cox proportional hazards regression, respectively.ResultsOverall, 92 patients were included in this study, 46(50.0%) were defined as PJP+ group, and the other 46 (50.0%) as PJP- group, and 31(67.4%) PJP occurred in the first 3 months. Increased neutrophil-lymphocyte ratio (NLR) and CRP were independent risk factors for PJP occurrence, while trimethoprim-sulfamethoxazole (TMP/SMZ) prophylaxis was an independent protective factor (all p<0.05). The three-months mortality rate was higher in the PJP+ group compared to PJP- group (43.5% vs 23.9%, p=0.047). Rapidly progressive interstitial lung disease (RPILD) was a main predictor of mortality in anti-MDA5+DM patients with PJP, whereas glucocorticoid use was a significant protective factor.ConclusionsPJP has high prevalence and mortality in anti-MDA5+DM, while TMP/SMZ prophylaxis significantly reduces PJP risk. Mortality in PJP+ patients is primarily concentrated within the first 3 months, associated with RPILD. Early intervention with corticosteroids and prophylactic measures are crucial in reducing mortality.

Hepatocellular carcinoma (HCC) seriously threatens human health, mostly developed from liver fibrosis or cirrhosis. Since diethylnitrosamine (DEN) and carbon tetrachloride (CCl4)-induced HCC mouse model almost recapitulates the characteristic of HCC with fibrosis and inflammation, it is taken as an essential tool to investigate the pathogenesis of HCC. However, a comprehensive understanding of the protein expression profile of this model is little. In this study, we performed proteomic analysis of this model to elucidate its proteomic characteristics. Compared with normal liver tissues, 432 differentially expressed proteins (DEPs) were identified in tumor tissues, among which 365 were up-regulated and 67 were down-regulated. Through Gene Ontology (GO) analysis, Ingenuity Pathway Analysis (IPA), protein-protein interaction networks (PPI) analysis and Gene-set enrichment analysis (GSEA) analysis of DEPs, we identified two distinguishing features of DEN and CCl4-induced HCC mouse model in protein expression, the upregulation of actin cytoskeleton and branched-chain amino acids metabolic reprogramming. In addition, matching DEPs from the mouse model to homologous proteins in the human HCC cohort revealed that the DEN and CCl4-induced HCC mouse model was relatively similar to the subtype of HCC with poor prognosis. Finally, combining clinical information from the HCC cohort, we screened seven proteins with prognostic significance, SMAD2, PTPN1, PCNA, MTHFD1L, MBOAT7, FABP5, and AGRN. Overall, we provided proteomic data of the DEN and CCl4-induced HCC mouse model and highlighted the important proteins and pathways in it, contributing to the rational application of this model in HCC research.

Hepatocellular carcinoma (HCC) is a highly lethal cancer, and proteomic studies have shown increased protein diversity and abundance in HCC tissues, whereas the role of protein translation has not been extensively explored in HCC. Our research focused on key molecules in the translation process to identify a potential contributor in HCC. We discovered that EIF4G2, a crucial translation initiation factor, is significantly upregulated in HCC tissues and associated with poor prognosis. This study uniquely highlights the impact of EIF4G2 deletion, which suppresses tumor growth and metastasis both in vitro and in vivo. Furthermore, polysome analysis and nascent protein synthesis assays revealed EIF4G2’s role in regulating protein translation, specifically identifying PLEKHA1 as a key translational product. This represents a novel mechanistic insight into HCC malignancy. RNA immunoprecipitation (RIP) and Dual-luciferase reporter assays further revealed that EIF4G2 facilitates PLEKHA1 translation via an IRES-dependent manner. Importantly, the synergistic effects of EIF4G2 depletion and PLEKHA1 reduction in inhibiting cell migration and invasion underscore the therapeutic potential of targeting this axis. This study not only advances our understanding of translational regulation in HCC but also identifies the EIF4G2-PLEKHA1 axis as a promising therapeutic target, offering new avenues for intervention in HCC treatment.

Background Pulmonary arterial hypertension (PAH) is a severe complication of systemic lupus erythematosus (SLE). This study aims to explore the clinical characteristics and prognosis in SLE-PAH based on consensus clustering and risk prediction model. Methods A total of 205 PAH (including 163 SLE-PAH and 42 idiopathic PAH) patients were enrolled retrospectively based on medical records at the First Affiliated Hospital of Zhengzhou University from July 2014 to June 2021. Unsupervised consensus clustering was used to identify SLE-PAH subtypes that best represent the data pattern. The Kaplan–Meier survival was analyzed in different subtypes. Besides, the least absolute shrinkage and selection operator combined with Cox proportional hazards regression model were performed to construct the SLE-PAH risk prediction model. Results Clustering analysis defined two subtypes, cluster 1 ( n = 134) and cluster 2 ( n = 29). Compared with cluster 1, SLE-PAH patients in cluster 2 had less favorable levels of poor cardiac, kidney, and coagulation function markers, with higher SLE disease activity, less frequency of PAH medications, and lower survival rate within 2 years (86.2% vs. 92.8%) ( P < 0.05). The risk prediction model was also constructed, including older age at diagnosis (≥ 38 years), anti-dsDNA antibody, neuropsychiatric lupus, and platelet distribution width (PDW). Conclusions Consensus clustering identified two distinct SLE-PAH subtypes which were associated with survival outcomes. Four prognostic factors for death were discovered to construct the SLE-PAH risk prediction model.

Abstract BackgroundDermatomyositis (DM) is a cell-mediated autoimmune disease of intricate aetiology. Necroptosis is a newly identified form of programmed cell death. This research aimed to explore the value of necroptosis-related genes in DM. Methods DM datasets were obtained from Gene Expression Omnibus (GEO) database. Necroptosis-related differentially expressed genes (NRDEGs) of DM were identified by intersecting differentially expressed genes (DEGs) with necroptosis gene set. Then, signature genes of NRDEGs were determined by the machine learning method of random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and the least absolute shrinkage and selection operator regression (LASSO). Moreover, immune microenvironment of DM and its correlation with signature genes were created to assess immune dysregulation. Besides, functional enrichment analysis, protein-protein interaction (PPI) co-expression network construction, transcription factor (TF)-miRNA network analysis were collectively performed on signature genes. In addition, the Mfuzz expression pattern clustering and functional enrichment based on the optimal signature was conducted. Results A total of 2524 DEGs in GSE143323 were obtained, including BAX, BIRC3, JAK3, SPATA2L and TNFSF10. Through the intersection with necroptosis gene set, 28 NRDEGs were examined. Furthermore, five signature genes were identified via machine learning and were verified in GSE1551. In immune landscape evaluation, signature genes were positively correlated with most immunocytes, human leukocyte antigen (HLA) genes, and immune checkpoints. Among them, TNFSF10 was the best diagnostic signature of DM. The most highly associated module genes with TNFSF10 by Mfuzz expression pattern clustering mainly enriched in immunity and immunoregulation. Conclusions Necroptosis occurs in DM, and is closely related to DM immune microenvironment, which merits further investigations in the necroptosis of DM pathogenesis.

Abstract Objective To analyze the high-throughput sequencing data of giant cell arteritis by bioinformatics technology, to initially identify the core genes associated with giant cell arteritis and to explore potential therapeutic agents. Methods Gene expression profile (GSE174694) was obtained from the Gene Expression Database (GEO), and the differential genes were calculated, the differentially expressed genes were analyzed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG), and the protein interaction network was constructed to obtain the core genes. Finally, drug correlation analysis (connectivity map, CMap) was used to identify small molecule drugs with potential therapeutic effects on giant cell arteritis. Results A total of 771 differentially expressed genes were screened, including 481 up-regulated and 290 down-regulated. The GO analysis showed that the differentially expressed genes were mainly involved in cell surface receptor signaling pathway, T cell receptor signaling pathway, cell adhesion and intrinsic immune response, and the KEGG pathway analysis showed that the differentially expressed genes were mainly involved in chemokine signaling pathway, Th17 cell differentiation and Th1 and Th2 cell differentiation. The KEGG pathway analysis showed that the differential genes were mainly involved in chemokine signaling pathway, Th17 cell differentiation and Th1 and Th2 cell differentiation. The protein interaction network was constructed to screen five core genes, PTPRC, FCGR2B, ITGAM, SPI1 and ITGB2, which were mainly involved in promoting T cell value-added and differentiation, inhibiting apoptosis, increasing cell adhesion and promoting inflammatory response. CMap analysis suggested that small molecules such as warfarin A and anisomycin have potential therapeutic effects on giant cell arteritis. The CMap analysis suggested the potential therapeutic effects of small molecules such as warfarin A and anisomycin on giant cell arteritis. Conclusion This study provides a holistic view of the gene transcriptome in giant cell arteritis, and the core genes and small molecule drugs screened may provide new ideas for the pathogenesis of giant cells and drug development.

Abstract Background: This study aims to probe the clinical characteristics and prognosis in SLE-PAH based on consensus clustering and risk prediction model. Methods: A total of 163 SLE-PAH patients were enrolled retrospectively based on medical records at the First Affiliated Hospital of Zhengzhou University from July 2014 to June 2021. Unsupervised consensus clustering was used to identify SLE-PAH subtypes that best represent the data pattern. The Kaplan-Meier survival was analyzed in different subtypes. Besides, the least absolute shrinkage and selection operator (LASSO) combining with Cox proportional hazards regression model were performed to construct the SLE-PAH risk prediction model. Results: Clustering analysis defined two subtypes, cluster 1 (n = 134) and cluster 2 (n = 29). Compared with cluster 1, SLE-PAH patients in cluster 2 had less favorable levels of poor cardiac, kidney, and coagulation functions, along with high SLE disease activity, low survival rate within 2 years (86.2% vs. 92.8%) (P < 0.05). The risk prediction model was then constructed, including older age (≥ 38 years), anti-dsDNA antibody, neuropsychiatric lupus and platelet distribution width (PDW). Conclusions: Consensus clustering identified two distinct SLE-PAH subtypes, of which cluster 2had more organ involvement, higher disease activity, and poorer survivability within 2 years. Older age (≥38 years), anti-dsDNA antibody, neuropsychiatric lupus and PDW could be regarded as prognostic factors for death with SLE-PAH patients.

Hepatocellular Carcinoma(HCC)is a serious health problem that poses a severe threat to hu-man.Although a growing list of evidence shows that surgical resection of early-stage HCC can strikingly improve the survival of patients,the high recurrence rate remains an obstacle to treatment.According to two independent early-stage HCC proteomic datasets,we found that prolyl 4-hydroxylase subunit alpha-2(P4HA2)was dramatically elevated in HCC tissues contrasted with paired normal liver tissues.Aberrant expression of P4HA2 showed worse prognosis by using the Kaplan-Meier(KM)analyses and Cox regres-sion models.Gain-and loss-of-function studies demonstrate that P4HA2 promotes HCC proliferation and metastasis in vitro.Further mechanistic studies revealed that the depletion of P4H A2 promoted apoptosis by activating the inositol-requiring kinase 1(IRE 1α)-induced proapoptotic unfolded protein response(UPR).These results suggest that P4HA2 may modulate cancer cell apoptosis by regulating endoplasmic reticulum(ER)homeostasis and it is expected to be a promising HCC therapeutic target.

Cell lines are extensively used tools, therefore a comprehensive proteomic overview of hepatocellular carcinoma (HCC) cell lines and an extensive spectral library for data independent acquisition (DIA) quantification are necessary. Here, we present the proteome of nine commonly used HCC cell lines covering 9,208 protein groups, and the HCC spectral library containing 253,921 precursors, 168,811 peptides and 10,098 protein groups. The proteomic overview reveals the heterogeneity between different cell lines, and the similarity in proliferation and metastasis characteristics and drug targets-expression with tumour tissues. The HCC spectral library generating consumed 108 hours’ runtime for data dependent acquisition (DDA) of 48 runs, 24 hours’ runtime for database searching by MaxQuant version 2.0.3.0, and 1 hour’ runtime for processing by Spectronaut TM version 15.2. The HCC spectral library supports quantification of 7,637 protein groups of triples 2-hour DIA analysis of HepG2 and discovering biological alteration. This study provides valuable resources for HCC cell lines and efficient DIA quantification on LC-Orbitrap platform, further help to explore the molecular mechanism and candidate therapeutic targets.

The HCC spectral library generated in this work

Hepatocellular carcinoma is the third leading cause of deaths from cancer worldwide. Infection with the hepatitis B virus is one of the leading risk factors for developing hepatocellular carcinoma, particularly in East Asia(1). Although surgical treatment may be effective in the early stages, the five-year overall rate of survival after developing this cancer is only 50-70%(2). Here, using proteomic and phospho-proteomic profiling, we characterize 110 paired tumour and non-tumour tissues of clinical early-stage hepatocellular carcinoma related to hepatitis B virus infection. Our quantitative proteomic data highlight heterogeneity in early-stage hepatocellular carcinoma: we used this to stratify the cohort into the subtypes S-I, S-II and S-III, each of which has a different clinical outcome. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)-high expression of which is a signature specific to the S-III subtype-alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumour xenograft mouse model of hepatocellular carcinoma, we found that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumours that had high levels of SOAT1 expression. The proteomic stratification of early-stage hepatocellular carcinoma presented in this study provides insight into the tumour biology of this cancer, and suggests opportunities for personalized therapies that target it.

Hepatitis B virus (HBV)-encoded X antigen (HBx) contributes to the development of hepatocellular carcinoma (HCC). Although HBx has been implicated in the progression of HCC, its precise function in HBV-associated HCC remains unclear. In the present study, HBx affected 3-phosphoinositide-dependent protein kinase-1 (PDK1) and with-no-lysine (K) kinase (WNK1) signaling, which was identified to be involved in the viability and metastasis of hepatic cells. The phosphorylation of WNK1 was decreased when the hepatic cells were treated with a PDK1 inhibitor. The inhibition of PDK1 activity inhibited the viability and migration of hepatic cells. To the best of our knowledge, the present study is the first to identify the activation of PDK1 in HCC tissues, confirmed using western blot analysis. PDK1-WNK1 signaling may be a potential therapeutic target in HBV-associated liver cancer.

Aberrant kinases contribute to cancer survival and proliferation. Here, we quantitatively characterized phosphoproteomic changes in an HBx-transgenic mouse model of hepatocellular carcinoma (HCC) using high-resolution mass spectrometry, profiled 22,539 phosphorylation sites on 5431 proteins. Using a strategy to interpret kinase- substrate relations in HCC and to uncover predominant kinases in tumors, our results, revealed elevated kinase activities of Src family kinases (SFKs), PKCs, MAPKs, and ROCK2 in HCC, representatives of which were further validated in cell models and clinical HBV-positive HCC samples. Inhibitor combinations targeting Src and PKCs or ROCK2 both synergized significantly to inhibit cell growth. In addition, we demonstrated that phosphorylation at Src Ser17 directly affects its kinase activity. Our phosphoproteome data facilitated the construction of a detailed molecular landscape in HCC and should serve as a resource for the cancer community. Our strategy is generally applicable to targeted therapeutics, also highlights potential mechanisms of kinase regulation.

Mass-spectrometry-based phosphoproteomic workflows traditionally require efficient prefractionation and enrichment of phosphopeptides to gain an in-depth, global, and unbiased systematic investigation of phosphoproteome. Here we present TiO2 with tandem fractionation (TAFT) approach, which combines titanium dioxide (TiO2) enrichment and tandem high-pH reverse-phase (HpRP) for phosphoproteome analysis in a high-throughput manner; the entire workflow takes only 3 h to complete without laborious phosphopeptide preparation. We applied this approach to HeLa and HepG2.2.15 cells to characterize the capability of TAFT approach, which enables deep identification and quantification of more than 14 000 unique phosphopeptides in a single sample from 1 mg of protein as starting materials in <4 h of MS measurement. In total, we identified and quantified 21 281 phosphosites in two cell lines with >91% selectivity and high quantitative reproducibility (average Pearson correlation is 0.90 between biological replicates). More generally, the presented approach enables rapid, deep, and reproducible phosphoproteome analysis in a high-throughput manner with low cost, which should facilitate our understanding of signaling networks in a wide range of biological systems or the process of clinical applications.