Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication.

BACKGROUND: Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy. METHODS: Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments. RESULTS: The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy. CONCLUSIONS: We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.

IGF2BP2-related modification patterns in pancreatic cancer: A machine learning-driven approach towards personalized treatment.

Pancreatic cancer (PC) is a malignant digestive system tumor with a very poor prognosis. N6-methyladenosine (m6A) is mediated by a variety of readers and participates in important regulatory roles in PC. Based on TCGA_PAAD, ICGC_AU_PAAD, ICGC_CA_PAAD, GSE28735 and GSE62452 datasets, We mapped the multi-omics changes of m6A readers in PC and found that m6A readers, especially IGF2BP family genes, had specific changes and were significantly associated with poor prognosis. An unsupervised consensus clustering algorithm was used to explore the correlation between specific expression patterns of m6A readers in PC and enrichment pathways, tumor immunity and clinical molecular subtypes. Then, the principal component analysis (PCA) algorithm was used to quantify specific expression patterns and screen core genes. Machine learning algorithms such as Bootstrapping and RSF were used to quantify the expression patterns of core genes and construct a prognostic scoring model for PC patients. What's more, pharmacogenomic databases were used to screen sensitive drug targets and small molecule compounds for high-risk PC patients in an all-around and multi-angle way. Our study has not only provided new insights into personalized prognostication approaches, but also thrown light on integrating tailored risk stratification with precision therapy based on IGF2BP2-mediated m6A modification patterns.

Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer.

Pancreatic cancer (PC) is a digestive malignancy with worse overall survival. Tumor immune environment (TIME) alters the progression and proliferation of various solid tumors. Hence, we aimed to detect the TIME-related classifier to facilitate the personalized treatment of PC. Based on the 1612 immune-related genes (IRGs), we classified patients into Immune_rich and Immune_desert subgroups via consensus clustering. Patients in distinct subtypes exhibited a difference in sensitivity to immune checkpoint blockers (ICB). Next, the immune-related signature (IRS) model was established based on 8 IRGs (SYT12, TNNT1, TRIM46, SMPD3, ANLN, AFF3, CXCL9 and RP1L1) and validated its predictive efficiency in multiple cohorts. RT-qPCR experiments demonstrated the differential expression of 8 IRGs between tumor and normal cell lines. Patients who gained lower IRS score tended to be more sensitive to chemotherapy and immunotherapy, and obtained better overall survival compared to those with higher IRS scores. Moreover, scRNA-seq analysis revealed that fibroblast and ductal cells might affect malignant tumor cells via MIF-(CD74+CD44) and SPP1-CD44 axis. Eventually, we identified eight therapeutic targets and one agent for IRS high patients. Our study screened out the specific regulation pattern of TIME in PC, and shed light on the precise treatment of PC.

Comprehensive machine learning-generated classifier identifies pro-metastatic characteristics and predicts individual treatment in pancreatic cancer: A multicenter cohort study based on super-enhancer profiling.

Rationale: Accumulating evidence illustrated that the reprogramming of the super-enhancers (SEs) landscape could promote the acquisition of metastatic features in pancreatic cancer (PC). Given the anatomy-based TNM staging is limited by the heterogeneous clinical outcomes in treatment, it is of great clinical significance to tailor individual stratification and to develop alternative therapeutic strategies for metastatic PC patients based on SEs. Methods: In our study, ChIP-Seq analysis for H3K27ac was performed in primary pancreatic tumors (PTs) and hepatic metastases (HMs). Bootstrapping and univariate Cox analysis were implemented to screen prognostic HM-acquired, SE-associated genes (HM-SE genes). Then, based on 1705 PC patients from 14 multicenter cohorts, 188 machine-learning (ML) algorithm integrations were utilized to develop a comprehensive super-enhancer-related metastatic (SEMet) classifier. Results: We established a novel SEMet classifier based on 38 prognostic HM-SE genes. Compared to other clinical traits and 33 published signatures, the SEMet classifier possessed robust and powerful performance in predicting prognosis. In addition, patients in the SEMetlow subgroup owned dismal survival rates, more frequent genomic alterations, and more activated cancer immunity cycle as well as better benefits in immunotherapy. Remarkably, there existed a tight correlation between the SEMetlow subgroup and metastatic phenotypes of PC. Among 18 SEMet genes, we demonstrated that E2F7 may promote PC metastasis through the upregulation of TGM2 and DKK1. Finally, after in silico screening of potential compounds targeted SEMet classifier, results revealed that flumethasone could enhance the sensitivity of metastatic PC to routine gemcitabine chemotherapy. Conclusion: Overall, our study provided new insights into personalized treatment approaches in the clinical management of metastatic PC patients.

Clinical Analysis of C-Shaped Embedded Pancreaticojejunostomy in Pancreaticoduodenectomy.

Background: Comparing the effects of C-shaped embedded anastomosis and pancreatic duct-jejunal mucosal anastomosis on the incidence of pancreatic fistula after pancreaticoduodenectomy (PD) to find a better pancreaticojejunal anastomosis method that can reduce the occurrence of complications during the operation and benefit the patients. Methods: A retrospective subresearch method was used to select the clinical data of patients who have undergone pancreaticoduodenectomy in our hospital from December 2019 to March 2021. The indicators to be collected for this study include gender, age, body mass index, preoperative liver function (total bilirubin, alanine aminotransferase, and albumin), preoperative comorbidities (diabetes, chronic pancreatitis), and pancreatic condition (texture, pancreatic duct diameter). The patients were divided into two groups according to the method of pancreaticojejunostomy: C-shaped embedded anastomosis group (n = 38) and pancreatic duct-jejunal mucosal anastomosis group (n = 30). The duration of pancreaticojejunostomy, biliary-enteric anastomosis, gastrointestinal anastomosis, intraoperative blood loss, upper abdominal surgery history, pathological type, intraoperative blood loss, pancreaticojejunostomy time, combined pancreatic fistula, biliary fistula, hemorrhage, and abdominal infection were observed and compared. According to the different methods of pancreaticojejunostomy during operation, they were divided into group A: C-shaped embedded pancreaticojejunostomy group (38 cases), and group B: pancreatic duct-jejunal mucosal anastomosis group (30 cases). The postoperative complications were compared between the two groups, and the observed indicators were analyzed with statistical methods. Results: The average pancreaticojejunostomy time in group A was 32.13 ± 4.52 min, and the average pancreaticojejunostomy time in group B was 43.23 + 4.31 min. The difference was statistically significant (p < 0.05). Neither group A nor group B had a grade C fistula. The incidence of biochemical fistula in group A was 21.05% (8/38), and the incidence of biochemical fistula in group B was 13.3% (4/30). The difference was not statistically significant (p > 0.05). The incidence of grade B fistula in group A was 5.20% (2/38), and the incidence of grade B fistula in group B was 26.67% (8/30). The difference was statistically significant (p < 0.05). There were no perioperative deaths in the two groups. Conclusion: According to the results of data analysis, it can be seen that both the two types of pancreaticojejunostomy have good clinical effects, but that in terms of reducing the grade of pancreatic fistula, the C-shaped embedded pancreaticojejunostomy is obviously better and safer. At the same time, the C-shaped embedded pancreaticojejunostomy can shorten the time of pancreaticojejunostomy and is easier to operate, thus worthy of clinical promotion.

Ferroptosis-Related IncRNAs Are Prognostic Biomarker of Overall Survival in Pancreatic Cancer Patients.

Pancreatic cancer (PC) is one of the most lethal malignancies, the mortality and morbidity of which have been increasing over the past decade. Ferroptosis, a newly identified iron-dependent cell death pattern, can be induced by iron chelators and small lipophilic antioxidants. Nonetheless, the prognostic significance of ferroptosis-related lncRNAs in PC remains to be clarified. We obtained the lncRNA expression matrix and clinicopathological information of PC patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) datasets in the current study. Firstly, we conducted Pearson correlation analysis to delve into the ferroptosis-related lncRNAs, and univariate Cox analysis was implemented to examine the prognostic values in PC patients. Twenty-three prognostic ferroptosis-related lncRNAs were confirmed and loaded into the least absolute shrinkage and selection operator Cox (LASSO-Cox) analysis, then a ferroptosis-related lncRNA prognostic marker (Fe-LPM) was established in the TCGA dataset. Risk scores of patients were calculated and segregated PC patients into low-risk and high-risk subgroups in each dataset. The prognostic capability of Fe-LPM was also confirmed in the ICGC dataset. Gene set enrichment analysis (GSEA) revealed that several ferroptosis-related pathways were enriched in low-risk subgroups. Furthermore, we adopted a multivariate Cox regression to establish a nomogram based on risk score, age, pathological T stage and primary therapy outcome. A competing endogenous RNA (ceRNA) network was also created relied on four of the twenty-three ferroptosis-related lncRNAs. In conclusion, the eight Fe-LPM can be utilized to anticipate the overall survival (OS) of PC patients, which are meaningful to guiding clinical strategies in PC.

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients.

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

Immune cells regulate matrix metalloproteinases to reshape the tumor microenvironment to affect the invasion, migration, and metastasis of pancreatic cancer.

This study aimed to identify author, country, institutional, and journal collaborations and assess their impact, along with knowledge base, as well as identify existing trends, and uncover emerging topics related to matrix metalloproteinase and pancreatic-cancer research. A total of 1474 Articles and reviews were obtained from the Web of Science Core Collection and analyzed by Citespace and Vosviewer. CANCER RESEARCH, CLINICAL CANCER RESEARCH, and FRONTIERS IN IMMUNOLOGY are the most influential journals. The three main aspects of research in matrix metalloproteinases-pancreatic cancer-related fields included the pathogenesis mechanism of pancreatic cancer, how matrix metalloproteinases affect the metastasis of pancreatic cancer, and what role matrix metalloproteinases play in pancreatic cancer treatment. Tumor microenvironment, pancreatic stellate cells, drug resistance, and immune cells have recently emerged as research hot spots. In the future, exploring how immune cells affect matrix metalloproteinases and reshape the tumor microenvironment may be the key to curing pancreatic cancer. This study thus offers a comprehensive overview of the matrix metalloproteinases-pancreatic cancer-related field using bibliometrics and visual methods, providing a valuable reference for researchers interested in matrix metalloproteinases-pancreatic cancer.

Interplay of four types of RNA modification writers revealed distinct tumor microenvironment and biological characteristics in pancreatic cancer.

Background: Pancreatic cancer (PC) is one of the most lethal malignancies and carries a dismal mortality and morbidity. Four types of RNA modification (namely m6A, m1A, APA and A-to-I) could be catalyzed by distinct enzymatic compounds ("writers"), mediating numerous epigenetic events in carcinogenesis and immunomodulation. We aim to investigate the interplay mechanism of these writers in immunogenomic features and molecular biological characteristics in PC. Methods: We first accessed the specific expression pattern and transcriptional variation of 26 RNA modification writers in The Cancer Genome Atlas (TCGA) dataset. Unsupervised consensus clustering was performed to divide patients into two RNA modification clusters. Then, based on the differentially expressed genes (DEGs) among two clusters, RNA modification score (WM_Score) model was established to determine RNA modification-based subtypes and was validated in International Cancer Genome Consortium (ICGC) dataset. What's more, we manifested the unique status of WM_Score in transcriptional and post-transcriptional regulation, molecular biological characteristics, targeted therapies and immunogenomic patterns. Results: We documented the tight-knit correlations between transcriptional expression and variation of RNA modification writers. We classified patients into two distinct RNA modification patterns (WM_Score_high and _low), The WM_Score_high subgroup was correlated with worse prognosis, Th2/Th17 cell polarization and oncogenic pathways (e.g. EMT, TGF-ß, and mTORC1 signaling pathways), whereas the WM_Score_low subgroup associated with favorable survival rate and Th1 cell trend. WM_Score model also proved robust predictive power in interpreting transcriptional and post-transcriptional events. Additionally, the potential targeted compounds with related pathways for the WM_Score model were further identified. Conclusions: Our research unfolds a novel horizon on the interplay network of four RNA modifications in PC. This WM_Score model demonstrated powerful predictive capacity in epigenetic, immunological and biological landscape, providing a theoretical basis for future clinical judgments of PC.

Decompressive craniectomy can improve the recovery of neurological function, daily living ability and life quality of patients with intracerebral hemorrhage after surgery.

OBJECTIVE: To determine the effect of decompressive craniectomy (DC) on the recovery of neurological function, daily living ability and life quality of patients with intracerebral hemorrhage (ICH) after surgery. METHODS: Totally 290 patients with ICH admitted to our hospital from January 2018 to June 2020 were retrospectively enrolled and assigned to two groups according to different surgical methods. Among them, 138 patients who received craniotomy evacuation of hematoma (CEH) only were assigned to a control group (Con group), while the other 152 who received CEH combined with DC to a research group (Res group). The two groups were compared in the total effective rate, hematoma clearance rate, and complication rate. Additionally, the ICP and MMP-9 levels after surgery, National Institutes of Health Stroke Scale (NIHSS), activities of daily living (ADL), Fugl-Meyer Assessment of motor function (FMA), Glasgow outcome scale (GOS), Glasgow coma scale (GCS), and MOS 36-Item Short-Form Health Survey (SF-36) scores before and after surgery were also compared between the two groups. RESULTS: After treatment, the Res group showed a notably higher total effective rate, hematoma clearance rate, and a notably lower complication rate than the Con group. On postoperative day 3 and 7, the Res group showed notably lower ICP than the Con group, and on postoperative day 7, the Res group showed a notably lower MMP-9 level as compared with the Con group. Additionally, 6 months after the surgery, the Res group got notably lower NIHSS scores and higher ADL, GOS, and SF-36 scores as compared with the Con group, and at 1 month after surgery, the Res group got notably higher FMA scores as compared to the Con group. Moreover, on postoperative day 7, the Res group got notably higher GCS scores than the Con group. CONCLUSION: DC can improve the recovery of neurological function, daily living ability and life quality of patients with ICH after surgery.

Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma.

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.