Besides SCLC‐A (ASCL1‐dominant), SCLC‐AN (combined ASCL1/NEUROD1), SCLC‐N (NEUROD1‐dominant), and SCLC‐P (POU2F3‐dominant), IHC and cluster analyses identified a quadruple‐negative SCLC subtype (SCLC‐QN). No unique YAP1‐subtype was found. The highest overall survival rates were associated with non‐neuroendocrine subtypes (SCLC‐P and SCLC‐QN) and the lowest with neuroendocrine subtypes . In univariate analyses, high ASCL1 expression was associated with poor prognosis and high POU2F3 expression with good prognosis. Notably, high ASCL1 expression influenced survival outcomes independently of other variables in a multivariate model. High POU2F3 and YAP1 protein abundances correlated with sensitivity and resistance to standard‐of‐care chemotherapeutics, respectively. Specific correlation patterns were also found between the efficacy of targeted agents and subtype‐specific protein abundances. In conclusion, we investigated the clinicopathological relevance of SCLC molecular subtypes in a large cohort of surgically resected specimens. Differential IHC expression of ASCL1, NEUROD1, and POU2F3 defines SCLC subtypes. No YAP1‐subtype can be distinguished by IHC. High POU2F3 expression is associated with improved survival in a univariate analysis, whereas elevated ASCL1 expression is an independent negative prognosticator. Proteomic and cell viability assays of human SCLC cell lines revealed distinct vulnerability profiles defined by transcription regulators. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.The tissue distribution and prognostic relevance of subtype‐specific proteins  present an evolving area of research in small‐cell lung cancer (SCLC). The expression of subtype‐specific transcription factors and P53 and RB1 proteins were measured by immunohistochemistry (IHC) in 386 surgically resected SCLC samples. Correlations between subtype‐specific proteins and  RB1 and TP53  are so prevalent in SCLC that they cannot define subclasses [Small‐cell lung cancer (SCLC) remains one of the most lethal forms of malignant diseases , 2, 3. Ubclasses , 7. Howebclasses .in silico and preclinical analyses have also questioned the existence of a distinct YAP1 subtype and proposed a unique triple‐negative subtype accompanied by an inflamed gene signature (SCLC‐I) [ASCL1‐high tumors have been reported to be associated with elevated expression of neuroendocrine (NE) markers, whereas NEUROD1‐high tumors with lower overall NE marker expression and, therefore, with a less NE phenotype , 11, 12.(SCLC‐I) . Of noteet al [et al [et al [Analyses of both human SCLC tumors and murine SCLC models revealed that most tumors harbor substantial intratumoral heterogeneity with regard to the expression pattern of subtype‐specific transcription regulators , 16, 17.et al  reportedl [et al  assessedl [et al  investigin vitro efficacy of targeted and chemotherapeutic agents, we also performed a comprehensive mass spectrometry (MS)‐based proteomic analysis in a panel of 26 human SCLC cell lines.In the present multicenter study, we investigated the expression pattern, clinical significance, and prognostic relevance of subtype‐specific transcription factors , as well as P53 and RB1 proteins in a large cohort of surgically treated SCLC patients comprising 386 samples. Additionally, in order to unfold the correlation patterns between subtype‐specific proteins and In this multicenter study, we included 386 patients with histologically confirmed SCLC who underwent surgical resection in five Central European medical centers. The study was conducted in accordance with the guidelines of the Helsinki Declaration of the World Medical Association and with the approval of the national level Ethics Committee of each participating country. Due to the retrospective nature of the study, the requirement for written informed consent was waived. After clinical information was collected, patient identifiers were removed, and, subsequently, patients could not be identified either directly or indirectly. Further details of the study population and treatments are provided in Supplementary materials and methods.whole tissue section (WTS) cohort where complete surgical formalin‐fixed paraffin‐embedded (FFPE) blocks were available (n = 141) or a TMA cohort  and analyzed these two cohorts by IHC separately. TMA and IHC protocols are described in detail in Supplementary materials and methods.We grouped the patients either into a in vitro efficacy of therapeutic agents was assessed by determining their corresponding IC50 values in each cell line. Details concerning MS‐based proteomic analyses and cell viability assays are described in Supplementary materials and methods.In total, 26 commercially available human SCLC cell lines were subjected to in‐depth proteomic analyses. The All statistical analyses were performed using R version 3.6.3 . See Supplementary materials and methods for details.WTS cohort, whose clinicopathological characteristics are summarized in supplementary material, Table WTS cohort, we also found that intratumoral necrosis is a feature of low NEUROD1‐expressing tumors. No statistically significant associations were found between P53 or RB expression and clinicopathological characteristics. The TMA cohort consisted of 245 SCLC patients. Although these patients also underwent lung resection surgery, in their case only TMA specimens were available  and the included patients were predominantly male (76.4%). Of note, due to the relatively long inclusion period, clinicopathological data of the TMA cohort was not available in some of the cases  tendency was observed in the case of ASCL1 expression and tumor stage as in the WTS cohort. Accordingly, the majority of late‐stage SCLC patients had high ASCL1‐expressing tumors in the TMA cohort , and moreover between expression of YAP1 and RB1 (r = 0.227)  Figure . Of noteWTS cohort was 58.9 months, whereas the median overall survival (OS) was 35.3 months. First, we performed a univariate survival analysis in order to identify the clinical prognostic factors for OS  detected in patients with high POU2F3‐expressing tumors, POU2F3 expression did not influence the survival outcomes independently of other clinicopathological variables  and adjuvant CHT (p < 0.001) independently influenced the OS. NEUROD1 expression had no significant impact on survival in our multivariate model.In order to assess if the prognostic value of ASCL1, NEUROD1, and POU2F3 expression was independent of other variables (such as disease stage or therapeutic approaches) in the s Figure . The mods Figure . As expeTMA cohort, the median follow‐up time was 113.3 months, while the median OS was 18.8 months. By using univariate survival analysis , adjuvant CHT , and in those who underwent anatomic resection . Moreover, similar to the WTS cohort, the OS in the TMA cohort was also significantly longer in patients with low ASCL1 , and moreover, low ASCL1 expression was associated with a tendency for better survival .In the t Figure , adjuvanr = −0.488), but no correlation with RB1 and P53. Next, in order to investigate the therapeutic relevance of subtype‐specific protein expressions, we correlated their proteomic abundance with the IC50 values of various targeted and chemotherapeutic agents [50 values of the AURK‐inhibitor alisertib (r = 0.493) and, moreover, between YAP1 abundance and the sensitivities against the CDK‐inhibitors abemaciclib and CGP60474 . Furthermore, we observed that high NEUROD1 proteomic abundance confers in vitro sensitivity to alisertib (r = −0.401), the AURK‐inhibitor barasertib (r = −0.674), abemaciclib (r = −0.502), CGP60474 (r = −0.536), and the IGF‐1R‐inhibitor BMS‐754807 (r = −0.581) , irinotecan (r = −0.554), topotecan (r = −0.569), and etoposide (r = −0.507). Furthermore, we found a statistically significant positive linear correlation between YAP1 abundance and IC50 values for cisplatin (r = 0.628), irinotecan (r = 0.611), and topotecan (r = 0.589).Our in‐depth proteomic analysis identified and quantitated more than 8,000 proteins in each of the 26 human SCLC cell lines. Interestingly, unsupervised clustering of samples based on protein abundance levels of ASCL1, NEUROD1, POU2F3, and YAP1 differentiated a distinct YAP1‐driven, a mixed SCLC‐AN, and a heterogenous SCLC‐P cluster Figure . As for c agents  (supplem) Figure . As for ) Figure , we founin vitro cytotoxicity assays in a large panel of human SCLC cell lines.Comprehensive genomic profiling in recent years has led to the refinement of SCLC classification by schemes defined by distinct gene expression profiles , 8. The classic’ subtype [typical’ SCLC morphology and high expression of NE markers [et al [Heterogeneity is prominent in SCLC tumors in terms of molecular diversity and NE features , 15. The subtype ) represes [et al ) that a s [et al , 11, 16.et al [et al [In our current study, we found no distinct SCLC‐Y subtype, which is consistent with the findings of Baine et al . Insteadl [et al . Neverthl [et al . SCLC‐I l [et al , 33, 34.et al [As for tumoral diversity, two forms of intratumoral heterogeneity were detected in our study. While some tumors had both subtype‐specific marker expressing and nonexpressing cells within the same areas, in other specimens, clusters of these cells were found in spatially distinct regions. This latter phenotype corresponds with the findings of Gay et al  and suppet al , surgicaRB1 [With regard to the correlation pattern of subtype‐specific and P53 and RB1 proteins, we found a positive linear correlation between expression of YAP1 and RB1. This is in line with a recent preclinical study suggesting that YAP1‐expressing SCLC cell lines might be associated with intact RB1 . NeverthDue to the low number of surgical cases with appropriate clinical data and to the morphological heterogeneity (i.e. combined SCLC/NSCLC or SCLC/large cell neuroendocrine carcinoma) of tissue samples, the clinicopathological and prognostic relevance of subtype‐defining proteins remains incompletely elucidated. Our study is among the first to report the highly distinct prognostic relevance of molecular subtypes in surgically‐treated SCLC patients. In the current study, the highest OS rates were associated with non‐NE (SCLC‐P and SCLC‐QN), whereas the lowest with NE  subtypes. In line with this, we found that in our univariate models the individual (i.e. subtype‐independent) expression of ASCL1 and POU2F3 expressions were associated with impaired and improved survival outcomes, respectively. Indeed, high‐grade NE features have been described as a sign of poor prognosis in lung cancer patients , 36. MorTMA cohort, our group previously found that NE‐low SCLCs are associated with increased immune cell infiltration  as compared to NE‐high tumors [et al [A clear mechanistic link between NE features and survival, however, is yet to be elucidated in SCLC patients. Nevertheless, by using cases from the current h tumors . This iss [et al , who sugOf note, besides the bleak immunological landscape, another possible explanation for the poor survival outcomes in patients with NE‐high SCLC might be that these tumors are also associated with excessive hormone production, and thus with a higher rate of paraneoplastic syndromes , 42. Theet al [POU2F3‐driven SCLC tumors do not express classical NE lineage markers but express markers of the tuft cell lineage . Therefoet al . In theiet al  and founet al . NotablyRB1 mutation status, and sensitivity to standard‐of‐care CHT [RB1 [Despite the recent progress in our understanding of the molecular underpinnings of SCLC, the in‐depth proteomic characteristics of human SCLC still represents an area of active investigation , 45, 46.care CHT , 47. SpeCHT [RB1 , 47.Our results concerning the sensitivity of NEUROD1‐high‐expressing cells to AURK‐ and CDK‐inhibitors are in line with previous studies , 4, 10. et al found that the loss of YAP1 might be a promising predictor of CHT responses in SCLC [et al also found a statistically nonsignificant tendency towards improved cisplatin response in SCLC‐P cell lines [To date, little is known about the therapeutic efficacy of chemotherapeutic agents in the context of molecular subtypes. In line with our results, Ito  in SCLC . Given tll lines .TMA cohort were included over a long time‐period. Therefore, clinicopathological data were not available for some cases. Additionally, although most antigens in FFPE blocks are well preserved over time [WTS cohort concerning especially the prognostic relevance of subtype‐defining proteins and the presence of the SCLC‐QN subtype, they might not offer the same quality as the more recently prepared TMAs or the WTS specimens. Second, expression patterns on TMA samples might be also biased by intratumoral heterogeneity. However, to partly overcome this issue all of our TMAs contained two separate tissue cores from each patient. Notably, none of these limitations applied to the WTS cohort. Third, in our study SCLC‐QN was defined as a subset characterized by low expression of all four transcription regulators. However, in order to facilitate diagnosis, positive diagnostic markers are also needed for SCLC‐QN. In a recent study, Gay et al [Our study has certain limitations due to its partly retrospective nature. First, patients in the ver time , 50, decver time , 24, Ki‐ver time , 27, SYPver time , and INIver time ) even inay et al  refers tBy investigating the prognostic relevance and tissue distribution of subtype‐specific proteins in surgically treated SCLC patients, the current multicenter study attempts to fill a knowledge gap in our understanding of SCLC. We validated the new molecular subtype classification using a large cohort of surgical specimens and, moreover, found that differential expression of ASCL1, NEUROD1, and POU2F3 defines unique SCLC subtypes. However, our IHC analyses did not distinguish a specific YAP1‐driven subtype. Instead, we report an SCLC‐QN subtype accompanied by low expression of all four SCLC transcription regulators. Furthermore, we also showed that high POU2F3 expression is associated with improved median OS in a univariate analysis, whereas high ASCL1 expression is an independent negative prognosticator in surgically treated SCLC. Finally, our proteomic analyses of SCLC cell lines provided insight into specific correlation patterns between transcription regulators and the therapeutic efficacy of targeted and CHT agents. Altogether, our results might help in the development of subtype‐specific therapeutic approaches and follow‐up strategies in this devastating disease.ZM, NB, KS, VL and BD were responsible for conceptualization. ZM, OP, CL, BF, ZL, JM, MR, LB, JK, MK, JS, MM and GMV were responsible for data curation. ZM, AL and ZV were responsible for formal analysis. ZM, NB, AS, SP, KS, VL and BD were responsible for investigation. ZM, NB, KD, JF, BS, MR, HP, GMV, VL and BD were responsible for methodology. ZM, KD, JF, GG, BS, CR, FRH, HP, KB, FRV, MAH, TK, KH and BD were responsible for project administration. ZM and OP were responsible for software. ZM and OP were responsible for visualization. OP was responsible for validation. FO, GG, LB, HP, IK, MK, JS, MM, GMV, KB, FRV, MAH, TK and KH were responsible for resources. KS, VL and BD were responsible for supervision. ZM and BD were responsible for writing ‐ original draft. All authors were responsible for writing ‐ review and editing.Supplementary materials and methodsClick here for additional data file.Supplementary figure legendsFigure S1. Representative IHC images of specimens from the TMA cohortFigure S2. Expression of subtype‐specific markers and P53 and RB1 in the WTS cohortFigure S3. Kaplan–Meier estimates for OS in surgically resected SCLC patients according to basic clinicopathological characteristics in the WTS cohortFigure S4. Kaplan–Meier estimates for OS in the WTS cohort according to NE subtypesFigure S5. Kaplan–Meier curves for OS in surgically resected SCLC patients according to basic clinicopathological characteristics in the TMA cohortFigure S6. Correlation between the proteomic abundances of subtype‐specific transcription factors and the in vitro efficacy of targeted and chemotherapeutic agentsClick here for additional data file.Table S1. Antibodies used for immunohistochemistryClick here for additional data file.Table S2. Clinicopathological characteristics of the WTS cohortClick here for additional data file.Table S3. Clinicopathological characteristics of the TMA cohortClick here for additional data file.Table S4. Prognostic impact of subtype‐specific markers and other relevant proteins in the WTS cohortClick here for additional data file.Table S5. Prognostic impact of subtype‐specific markers and other relevant proteins in the TMA cohortClick here for additional data file.


The aim of this study is to validate the application of the sellar barrier concept for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning approach.Recently, it was defined that the n = 11/155) and inliers into separate datasets, the latter further separated into training (n = 115/144) and inlier test (n = 29/144) datasets. The machine learning analysis was performed using different novelty detection algorithms , whose performance was assessed separately and as an ensemble on the inlier and outlier test sets.We conducted a prospective cohort study, from June 2019 to September 2020: data from 155 patients with pituitary subdiaphragmatic adenoma operated through endoscopic approach at the Division of Neurosurgery, Università degli Studi di Napoli “Federico II,” were included. Preoperative magnetic resonance images (MRI) and intraoperative findings were analyzed. After processing patient data, the experiment was conducted as a novelty detection problem, splitting outliers , MRI barrier: strong (p = 1.405 × 10−6), MRI barrier: weak (p = 4.487 × 10−8), intraoperative barrier: strong (p = 2.788 × 10−7), and intraoperative barrier: weak (p = 2.191 × 10−10). We recorded 11 cases of intraoperative leakage that occurred in the majority of patients presenting a weak sellar barrier (p = 4.487 × 10−8) at preoperative MRI. Accuracy, sensitivity, and specificity for outlier detection were 0.70, 0.64, and 0.72 for IF; 0.85, 0.45, and 1.00 for LOF; 0.83, 0.64, and 0.90 for oSVM; and 0.83, 0.55, and 0.93 for the ensemble, respectively.According to the type of sellar barrier, patients were classified into two groups, i.e., strong and weak barrier; a third category of mixed barrier was defined when a case was neither weak nor strong. Significant differences between the three datasets were found for Knosp classification score (sellar barrier at MRI and its in vivo features as observed during endoscopic endonasal surgery. The novelty detection models highlighted differences between patients who developed an intraoperative CSF leak and those who did not.There is a true correlation between the type of  Endoscopic endonasal approach, representing the most suitable technique , is indiPituitary macroadenomas present a predominantly vertical growth pattern and, albeit being round and soft, displace and compress pituitary gland tissue: the latter gets to be a part along with arachnoid and sellar diaphragm of the interface between the tumor and the supradiaphragmatic area . Accordivia standard endoscopic corridor more troublesome, leading to an increased risk of CSF leak  or significant artifacts on the images used for the analysis were excluded.Patients with intra-suprasellar subdiaphragmatic pituitary adenoma scheduled for tumor resection All patients underwent radiological preoperative assessment with a specific MRI protocol for the sellar region that included sagittal and coronal slices in T1-weighted volumetric sequences, with and without contrast; with axial and sagittal slices of the sealing region in T2-weighted, fluid-attenuated inversion recovery (FLAIR), and Echo Spin gradient sequences (1.5 and 3.0 T resonator).sellar barrier were made with the Horos for Mac-OSX . The measurements were made as explained in previous publications  approach –24. A Kaapproach .k categories to k − 1 indicator variables. The final feature set comprised the following:1. Age2. Knosp classification score3. Gender4. Status: Growth hormone (GH) secreting5. Status: GH Prl secreting6. Status: Prl secreting7. Status: Nonfunctioning8. Size: Microadenoma9. MRI barrier: Strong10. MRI barrier: Weak11. Intraoperative barrier: Strong12. Intraoperative barrier: Weak13. Presurgical treatmentGiven the distribution of classes within the datasets, the experiment was treated as a novelty detection problem. Therefore, outliers  and inliers were split into separate datasets. Then, the latter was further separated with an 80%/20% proportion into training and inlier test datasets. The only variable with missing values within all datasets was the modified Knosp classification score . An imputer based on the mode of this parameter was fit on the training set (mode = 2) and used to transform all datasets to remove the missing values. Then, continuous variables were normalized using a min–max scaler (range = 0–1), also fit exclusively on the training data and used to transform all datasets. Finally, principal component analysis was used to reduce the dimensionality of the data to two vectors, again by fitting only on the training set features and transforming all datasets.Patient data were processed based on their nature using the pandas, numpy, and scikit-learn Python packages . OrdinalThe machine learning analysis was performed using the scikit-learn Python package . DiffereAll statistical tests were conducted in R . Continuous data are presented as mean and standard deviation. Categorical and ordinal data are presented as value counts and proportions. The Shapiro–Wilk test was used to assess the normality of distribution of continuous data. Analysis of variance and Fisher exact tests were used to assess for differences in variable distribution among the training and test groups. Precision , recall , accuracy , and f-score  were calculated as accuracy metrics.One hundred and fifty-five patients were enrolled in the study . Regarding the pituitary adenomas’ features, 129 (83%) were macroadenomas and 26 (17%) were microadenomas; 81 were nonfunctioning tumors (52%), while 43 (27%) were GH secreting, 15 (9.6%) were adrenocorticotropic hormone (ACTH) producing, 12 (0.6%) were prolactinomas, and finally 3 (0.2%) were GH/prolactin (PRL)-secreting adenomas. According to Micko grading scale, i.e. modified Knosp classification .The distribution according to the sellar barrier subtype on MRI was as follows: 108 (69.7%) adenomas had a strong barrier, 13 a weak barrier (0.8%) –3, and fp = 0.0015), MRI barrier: strong (p = 1.405 × 10−6), MRI barrier: weak (p = 4.487 × 10−8), intraoperative barrier: strong (p = 2.788 × 10−7), and intraoperative barrier: Weak (p = 2.191 × 10−10). Accuracy, sensitivity, and specificity for outlier detection were 0.70, 0.64, and 0.72 for IF, 0.85, 0.45, and 1.00 for LOF; 0.83, 0.64, and 0.90 for oSVM; and 0.83, 0.55, and 0.93 for the ensemble. Confusion matrices and accuracy metrics are presented in The training dataset included 115 (74%) patients without intraoperative fistula, while the inlier and outlier test sets included 29 (19%) and 11 (7%) patients, respectively. Patient clinical and demographic data are presented in The possibility to predict outcomes is critical to ensure the highest standards of surgical care, above all to satisfy patient inquiries with regard to the pros and cons of the procedure they are about to undergo.CSF leak is one of the most threatening complications of transsphenoidal pituitary surgery, and also per its related potential complications, such as meningitis  or tensiYears of peculiar care and efforts, in terms of materials and reconstruction techniques, have been given to this issue , 29–35, It is crucial to highlight that albeit intraoperative CSF leak rates are reported as high as 10.3%–69%, postoperative CSF leak rates lower down to 1.3%–8% , 40–54.In the present case series, we found a rate of intraoperative CSF leak of 7.1% and postoperative CSF leak of 0%, which are similar to those reported in the literature for this kind of surgery. These findings suggest that the rate of patients with intraoperative CSF leak who finally developed a postoperative CSF leak is negligible; this is mostly because of the improvement of reconstruction techniques over the years and the refinement of surgical skills in skull base surgeons .However, there are only few reports concerning the possible risk factors that can be detected preoperatively to predict an intraoperative CSF leakage, but univocal consensus has not achieved yet: different groups claimed an increased risk of postoperative fistula upon the opening of the third ventricle, or in cases of patients with higher BMI –11.sellar barrier concept and its role in predicting the risk of intraoperative CSF leakage was recently introduced , MRI barrier: weak (p = 4.487 × 10−8), intraoperative barrier: strong (p = 2.788 × 10−7), and intraoperative barrier: weak (p = 2.191 × 10−10).Our ML model identified with outstanding accuracy that there is a cogent correlation between the weak barrier type and the intraoperative CSF leakage MRI barrier: strong (locus minoris resistentiae,” whose careless manipulation during tumor removal can expose the increased risk of intraoperative CSF leakage.These findings are relatively new and might provide further issue to be considered when defining the surgical planning. Hence, also per intraoperative observation, a weak barrier represents a “In the near future, this computer-aided decision-making tool might improve surgical quality by regularly identifying those patients at higher risk of developing intraoperative CSF leakage and related complications; validated machine learning tools might change routine surgical practice if properly setup: the creation of a computerized predictive algorithm can be a crucial step to further refine modern neurosurgery.Our teams are working on a risk classification of intraoperative CSF leak to refine the most appropriate surgical strategy and adequately inform the patient about its postoperative course.Claude Bernard used to say, “who doesn't know what he's looking for, doesn't understand what he finds.” The clinical application of the sellar barrier concept will allow the skull base surgery team to predict the scenario they will encounter. Thanks to this, you will be able to carefully select the method to be used in the reconstructive phase.During preoperative consultation, the use of imaging software allows the surgeon to show the sellar barrier to the patient and explain with a graphic support about their risk of CSF leakage. Thanks to this, the surgeon can speak clearly and precisely with the patient. It allows the surgeon to inform the patient about his/her possible postoperative evolution: postoperative nasal symptoms, surgery time, and the risk of postoperative CSF fistulae, among others. This type of information would have legal implications in the postoperative period –14.The concept of the sellar barrier cannot be considered as a totally independent predictor factor of CSF leakage.This is a prospective cohort study with a small series of patients. A multicenter study with more extensive patient series is required to validate this concept and its clinical applicability.Finally, it is worth reminding that the downside of the ML model can be its troublesome application to a clinical context: the interpretation of this model conceives the human–machine interaction as its highest moment.The sellar barrier is a new parameter to be considered in the risk assessment of intraoperative CSF leak. The present study demonstrates the efficacy of the sellar barrier concept in patients operated for endoscopic endonasal pituitary removal and strengthens its clinical applicability.sellar barrier at MRI and its in vivo features as observed during endoscopic endonasal surgery. The novelty detection models highlighted differences between patients who developed an intraoperative CSF leak and those who did not.There is a true correlation between the type of


Lactobacillus genus). Furthermore, Lactobacillus helps produce folate that lowers the levels of homocysteine (Hcy); a hallmark non‐proteinogenic amino acid that defines the importance of epigenetics, and its landscape. In this study, we decided to test whether hydrogen sulfide (H2S), another Hcy lowering agent regulates the epigenetic gene writer DNA methyltransferase (DNMT), eraser FTO and TET2, and thus mitigates the skeletal muscle remodeling. We treated hyperhomocysteinemic  mice with NaHS (the H2S donor). The results suggested multi‐organ damage by HHcy in the CBS+/−mouse strain compared with WT control mice (CBS+/+). H2S treatment abrogated most of the HHcy‐induced damage. The levels of gene writer (DNMT2) and H3K9 (methylation) were higher in the CBS+/− mice, and the H2S treatment normalized their levels. More importantly, the levels of eraser FTO, TET, and associated GADD45, and MMP‐13 were decreased in the CBS+/− mice; however, H2S treatment mitigated their respective decrease. These events were associated with mitochondrial fission, i.e., an increase in DRP1, and mitophagy. Although the MMP‐2 level was lower in CBS+/− compared to WT but H2S could further lower it in the CBS+/− mice. The MMPs levels were associated with an increase in interstitial fibrosis in the CBS+/− skeletal muscle. Due to fibrosis, the femoral artery blood flow was reduced in the CBS+/− mice, and that was normalized by H2S. The bone and muscle strengths were found to be decreased in the CBS+/− mice but the H2S treatment normalized skeletal muscle strength in the CBS+/− mice. Our findings suggest that H2S mitigates the mitophagy‐led skeletal muscle remodeling via epigenetic regulation of the gene writer and eraser function.Ketone bodies (KB) serve as the food for mitochondrial biogenetics. Interestingly, probiotics are known to promote KB formation in the gut (especially those that belong to the  −/+ mice skeletal muscle is mitigated by the H2S treatment successfully.Abnormal mitophagy in the CBS In addition, cystathionine gamma‐lyase (CSE) knockout mice exhibit lethal myopathies  production enhancement that could be beneficial rather than simply lowering the plasma Hcy levels  are associated with frailty in elders, skeletal muscle malfunctioning, metabolic injury, and associated mortality  during tissue perfusion, and muscle atrophy. In addition, the proposed study has the potential to identify key signaling pathways that invariably govern the skeletal muscle adaptability that is most likely affected by the HHcy condition and the therapeutic H2S production. A positive outcome from the study will provide mechanistic aspects of the adaptive response after ischemia, and related injury. Furthermore, it may also provide a newer mechanism(s) of skeletal muscle metabolism regarding insulin resistance, mitochondrial dynamics, antioxidant capability, and tissue remodeling. The results from this study will help aid in formulating preventive or curative strategies and assess the usefulness of H2S treatment in numerous forms of muscle myopathy.Our proposal would not solely unravel the relative contribution(s) of the presence of H2S signaling in skeletal muscle perfusion, regeneration, and muscle frailty. Our strategy of employing the CBS heterozygous knockout mice would also help cater to the needs of HHcy patients with genetic mutation either in the methylenetetrahydrofolate reductase (MTHFR), CBS, or the methionine synthase (MS) gene. The MTHFR converts 5,10‐methylenetetrahydrofolate to 5‐methylenetetrahydrofolate while 5‐methylenetetrahydrofolate donates a methyl group for the conversion of Hcy to methionine the second reaction is catalyzed by the MS whose activity depends on the presence of vitamin B12  and given to mice for 6 weeks. Each mouse drinks approximately 5 ml/day. A total of 25 ml was prepared fresh each day for each mouse  and a breeding pair of the CBS2.2Blood pressure was measured via tail vein through non‐invasive Coda instruments  activity as described earlier  were resolved on SDS‐PAGE (10%) and then transferred to PVDF membranes. Immunoblotting with primary and secondary antibodies was performed according to the manufacturers' protocol. ECL Immobilon Forte Western HRP substrate (Millipore) was used to image the blots in a Bio‐Rad Chemi Doc System. The intensities of the bands were normalized via GAPDH for all the target proteins. The quantification was performed using Image Lab Software , fat obese associated protein , di‐methyl‐histone (Lys9) , and matrix metalloproteinases‐13  were ordered from Cell Signaling Technology. Antibodies for dynamin‐related protein‐1 , muscle RING‐finger protein‐1  were purchased from Abcam. Whereas DNA 5‐cytosine methyltransferase 2 , musclin , growth arrest, and DNA damage‐45 protein , glyceraldehyde 3‐phosphate dehydrogenase  were purchased from Santa Cruz Biotechnology. Briefly, after sacrifice, the gastrocnemius muscle was removed, and stored at −80°C. Protein samples were extracted by homogenization in an ice‐cold RIPA buffer containing protease inhibitor cocktail. The homogenates were centrifuged at 13,100 2.5Using Zymography the proteolytic activity of MMP‐2 on the substrate gel was quantitated. For the preparation and running of the gel, the separation of the band was carried out on 10% of Acrylamide: bis‐Acrylamide (29:1) in 0.375 M Tris–HCl, pH 8.80, 0.1% SDS, and 3 mg/ml of gelatin. Stacking gel had 4% of acrylamide‐bis acrylamide, and 0.1% SDS in 0.125 M Tris–HCl pH 6.80. Sample loading buffer had 50 mM Tris–HCl, pH 6.8, 10% glycerol, 1% SDS, 0.01% bromophenol blue. Collagenase buffer was comprised of 50 mM Tris–HCl pH 7.6, 0.2 M NaCl, 5 mM CaCl2, and 0.001% Brij‐35. The staining solution contained 0.1% Coomassie brilliant blue R250, 40% methanol, and 10% acetic acid. The distaining solution had 40% methanol and 10% acetic acid. The loaded sample consisted of serum (1 μl) with sample loading buffer on the SDS‐PAGE gel. The gel was run at 120v until the dye ran off the gel. The gel was then soaked in 2.5% Triton x‐100 on a shaker for 2 h, followed by washes (four times) in the distilled water. Then soaked in collagenase buffer and incubated at 37°C overnight on a shaker. Prior to staining, the gel was briefly rinsed in the distilled water and then stained with a staining solution for 2 h. The gel was then de‐stained with a distaining solution for 3 h. A bio‐Rad system was used to scan the gel, and the band was quantitated  at the end of the treatment using methods reported before, which binds reversibly with O2 thus the amount of O2 released in the peripheral tissue, including the muscle depends on the quantity of the circulating RBCs . The mice were then euthanized; soleus and gastrocnemius muscle tissues were harvested from CBS2.8Grip strength of the forelimbs (front 2 paws), and all limbs (4 paws) was evaluated using the Grip Strength Meter . As per the manufacturer's protocol, mice were held by the tail, lowered toward the apparatus, and allowed to grab the metal grid using 2 or 4 paws. The mice were pulled backward™ horizontally, and the force applied to the grid just before they lost their grip was recorded as the peak tension (converted to grams by the transducer). Peak force was measured two times in succession for each mouse for the front 2 paws, and all 4 paws. The mean value of both trials was used for analysis. Mice were given a minimum break of 5 min between trials ; a gaseous Hcy lowering agent, mitigates skeletal mitophagy led remodeling by epigenetic regulation of the gene writer, and eraser function during hyperhomocysteinemia (HHcy)  have vasoactive effects on the body, and hence it is likely that they all participate in blood pressure regulation  Figure . Hcy ind+/−strain. The H2S treatment increased the musclin levels in the CBS+/− mice. To determine the levels of epigenetic gene writer, eraser, and mitochondrial remodeling aspects, we then measured the DNA 5‐cytosine methyltransferase 2 (DNMT2), di‐methyl‐histone (Lys9) , fat obese associated protein (FTO), ten‐eleven translocate 2 (TET2), growth arrest and DNA damage‐45 protein (GADD45b), matrix metalloproteinases‐13 (MMP‐13), dynamin‐related protein‐1 (DRP1), and the muscle RING‐finger protein‐1 (MuRF1) respectively  mice . In that context, we detected an increased level of MMP2 Figure , and cols Figure . Finallye Figure .2S treatment did help normalize these musculo‐skeleton abnormal changes.In conclusion, our data support the hypothesis that an increase in gene writer, and a concomitant decrease in eraser cause mitochondrial fission, and mitophagy that make the skeletal muscle weak in the hyperhomocysteinemic (HHcy) mice Figure . An inte4+/−mice. Additionally, we found HHcy decreases musclin levels in skeletal muscle tissue, therefore, decreases skeletal muscle physical endurance and impairs mitochondria oxidative capacity. H2S treatments mitigate these impairments. Also, HHcy induces lower body mass, poor physical performance, and enhanced atrophic responses. These findings suggest that HHcy is detrimental to skeletal muscle homeostasis and long‐term function. Our study investigates the affected signal mechanisms in three key areas: tissue perfusion, tissue regeneration, and muscle atrophy. Furthermore, it showed the prevention of progressive muscle impairments in HHcy by either NaHS administration or reducing the Hcy levels and enhancing H2S levels in skeletal muscle tissue milieu in CBS heterozygous knockout mice.We have found that HHcy promotes skeletal muscle atrophy, and injury through the epigenetic disruption of the gene writer, and eraser function. The gene writer (DNMT) and eraser (FTO) were altered (Ijiri et al., MS and SCT designed the research; PK, YZ, RPH, and IS performed the experiments. RPH, MS, and SCT analyzed the data and interpreted the results. MS, SPM, and SCT drafted, edited, and finalized the manuscript. All authors approved the final version of the manuscript before its submission.No conflicts of interest, financial or otherwise.The experiments in this study were performed in accordance with the relevant guidelines and regulations.


Oral erythroplakia has been classically considered as the potentially malignant disorder with the highest rate of malignant development into squamous cell carcinoma. This critical systematic review and meta‐analysis aim to estimate the malignant development rate of oral erythroplakia and identify the associated risk factors.We performed a bibliographic search in PubMed, Scopus, Web of Science, Embase, and LILACS, with keywords “erythroplakia,” “erythroplasia,” “malignant transformation,” “malignant development,” “malignization,” “carcinogenesis,” “oral cancer,” “oral squamous cell carcinoma,” “mouth neoplasm,” and “prognosis.” Meta‐analysis was conducted using a random‐effects model.Ten observational studies with 441 patients met the inclusion criteria, whose mean malignant development rate was 12.7% and with a mean follow‐up period of patients of 6.66 years. In the initial biopsy, 42.8% of oral erythroplakia were already squamous cell carcinoma. The buccal mucosa was the most frequent location of oral erythroplakia, but the floor of the mouth was the most common site of malignant development. All patients who underwent malignant development showed epithelial dysplasia on the initial diagnostic biopsy.Overall malignant development rate of OE in the meta‐analysis was 19.9%. We could not associate any specific clinicopathological feature with the malignant development. The presence of epithelial dysplasia in the initial biopsy remains the worst prognostic factor. Further observational studies on OE are needed, with well‐established diagnostic criteria and good clinical follow‐up, in order to identify the true risk of malignant development of oral erythroplakia and the related risk factors. Only longitudinal observational studies (type of studies) were used.2.3Electronic searches were carried out in MEDLINE via PubMed, EMBASE, Web of Science, Scopus, WHO regional bibliographic database LILACS, and the Conference Proceedings Citation Index databases. Searches combined thesaurus  and free terms to maximize sensitivity. The algorithms used in each database contained the following keywords: “erythroplakia,” “erythroplasia,” “malignant transformation,” “malignant development,” “malignization,” “carcinogenesis,” “oral cancer,” “oral squamous cell carcinoma,” “mouth neoplasm,” and “prognosis.” These words were subjected to syntax adaptation for each database. All of the databases were searched from inception to February 2022 (upper limit).We performed a second search, introducing each keyword in an unstructured fashion to check whether every article on the topic was retrieved. Potentially relevant articles that any of the authors were already familiar with and reference lists from the retrieved articles were also comprehensively checked. All retrieved references were managed using Mendeley v.1.19.9 .2.4Criteria for eligibility of the studies included in the qualitative and quantitative analysis were as follows: (1) original research articles published in English language, (2) longitudinal observational studies, and (3) studies that analyses the MD of OE. The exclusion criteria were as follows: (1) letters to the editor, case reports, conference abstracts, clinical trials, etc.; (2) previous systematic reviews and meta‐analysis; (3) studies which reported on patients with diagnosis of erythroleukoplakia; (4) studies without a complete clinicopathological diagnosis of OE; and (5) studies that did not report specific data on OE.2.5κ score = 0.78), all of the studies were independently read by the reviewers. Any discrepancies between the researchers were resolved by a third or fourth researcher blinded to the study hypothesis (ILIM and JMA).Articles were selected in two phases by two authors (APJ and MPS), first by screening titles and abstracts that apparently meet inclusion criteria, and then by reading the full texts to assess their final inclusion. During the calibration exercise, reviewers thoroughly discussed the criteria and applied them to a sample of 50% of the retrieved studies to determine the inter‐examiner agreement. After adequate agreement was achieved (The information was retrieved by two investigators (APJ and MPS) using a custom‐made extraction sheet. The recorded data included: the first author and year of publication, country, study design, sample size, gender and age of patients, tobacco/alcohol/betel consumption, number and location of OE, histopathological data, follow‐up period, number of MD cases, and time until MD.2.6In the main meta‐analysis, we computed the prevalence MD events and that of total OE case by dividing the number of events by the sample size of the study. Then, we weighted the study‐specific log prevalence by the inverse of their variance to compute a pooled prevalence. For retrieved studies, the Clopper–Pearson interval was applied to estimate 95% confidence intervals (CIs). Combined pooled proportions were estimated with a random‐effect model (DerSimonian and Laird method). A further pre‐planned meta‐analysis to acknowledge the effect of clinicopathological factors on the MD of OE was considered, but this was discarded due to poor reporting of primary data. Therefore, we opted to perform a meta‐analysis of prevalence with the degrees of ED/presence of SCC in initial biopsy at diagnosis time‐point.χ2) and Higgins I2 test parameters were calculated. Cochran's Q test p < 0.1 was considered significant to assume apparent heterogeneity. The Higgins I2 statistic cut‐off points of 25%, 50%, and 75% were considered to indicate low, moderate, and high heterogeneity, respectively.For statistical heterogeneity analysis, Cochran Q χ2 and HigpEgger <0.1 as significant). The Metafor free R software package  was used for all statistical analysis as for figure plotting with user‐written commands. The significance level considered in all statistical analyses was 5% (p < 0.05).We assessed publication bias, first visually, using funnel plots, and then, more formally, using the test proposed by Egger et al.2.7Risk of bias (RoB) was assessed using the Quality in Prognosis Studies–QUIPS tool, supported by Cochrane Prognosis Methods Group for prognosis studies. QUIPS considers the following domains: (1) study participation, (2) study attrition, (3) prognostic factor measurement, (4) outcome measurement, (5) study confounding, and (6) statistical analysis and reporting.33.1We identified 974 registers through the aforementioned search, whose abstracts were reviewed for contents relevant to the topic of this study, resulting in 633 exclusions. A total of 341 papers were then retrieved and, after careful consideration, 318 of them were excluded because their information was not useful for this study. The remaining 23 (2.37%) registers were checked according to the inclusion/exclusion criteria set for this investigation.After the critical analysis of the studies, 10 (1.03%) studies from different geographical areas met the inclusion criteria: USA,3.2Only five studiesIn the five studies without a follow‐up period of patients,3.3In this review, 90 women (20.4%) and 336 men (79.6%) with OE lesions were analyzed, mostly over 50 yearsTwo studiesRegarding the presence of ED, different evaluation systems were used; thus, we divided ED into low‐grade ED (previous mild to moderate) and high‐grade ED (previous moderate to severe), following the WHO proposal.Only Feng et al.3.4I2 = 91.74%, p‐value by Q test = 0.00001) .In the main meta‐analysis, three studies) Figure . A remar) Figure ; nonethepEgger = 0.549) and low‐grade ED (pEgger = 0.685). The exceptions were high‐grade ED (pEgger = 0.001) and absence of ED (pEgger = 0.002), for which bias could not be ruled out.In the secondary analysis, we performed a pooled analysis on the prevalence of initial histopathological diagnosis: SCC, high‐grade ED, low‐grade ED, and absence of ED Table . Globall3.5The QUIPS tool showed that RoB was high in three studies4Erythroplakia is a premalignant disorder first described as a raised, velvety red plaque in the male and female genital mucosa.In 1955, Blau & Hymanany oral mucosal abnormality that is associated with a statistically increased risk of developing oral cancer”,Taking into account the current definition of OPMD: “We think that clinical follow‐up of patients is necessary to assess the true MD of OE as an OPMD. Therefore, when we considered the studies without a follow‐up period, we achieved a “wrong” MD rate of 50.51%, similar to that of previous reviews (44.9%–50%).All these findings question whether OE is the OPMD with the highest risk of MD, as previously stated.Except for the study by Chuang et al.,We also observed interesting differences between the location OE and where MD occurred. Some infrequent areas of OE like the FOM, the tongue, and the retromolar area are common sites of MD . In contrast, more frequent areas of OE like the buccal mucosa and the lip have a low rate of MD . These results suggest the existence of specific locations of the oral mucosa with a bigger risk for MD of OE. Also, they point to the need for a thorough clinical and histopathological differential diagnosis with all mucous pathologies clinically similar to OE, preferably SCC, but also oral lichenoid disease (OLD), erythematous candidiasis, lupus erythematosus, pemphigoid, and so forth.Candida infection, prosthetic stomatitis, immunological processes, and so forth. In our opinion, this classification is misleading and should not be used. Currently, we cannot consider as an OPMD an oral lesion that is already a SCC in the histopathological analysis; or can all “red plaque‐like” lesions that may appear on the oral mucosa be considered as an OE.Candida spp., and without ED or low‐grade ED, could actually represent other disorders.In 1972, ShearThe histopathological diagnosis is the gold standard technique for the prognostic assessment of all OPMD, including OE. Different oral mucous pathologies of an inflammatory nature may cause epithelial changes similar to those seen in ED, making the final clinicopathological diagnosis very difficult.Unfortunately, we had several limitations during the performance of this systematic review and meta‐analysis. First, is the low number of observational studies on the MD of OE, and with good clinical follow‐up period of patients; and second, is the lack of standardization and changes in the diagnostic criteria for OE over the years. This jeopardized the association between the MD of OE and different clinicopathological features.5In summary, overall MD rate of OE in this study was 19.9%, significantly lower than previously reported. We were unable to associate this MD with any specific clinicopathological feature, including ED. Nevertheless, we believe the presence of ED in the initial diagnostic biopsy remains the main prognostic factor in this OPMD. Further observational studies are needed, with well‐established diagnostic criteria and good clinical follow‐up, in order to identify the true risk of MD of OE and its related factors. This way we could design effective preventive and therapeutic programs.Conceptualization, data curation, formal analysis, methodology, resources, software, validation, visualization, and writing‐original draft: Alejandro Ismael Lorenzo‐Pouso. Conceptualization, investigation, methodology, writing‐original draft, writing‐review & editing: Irene Lafuente‐Ibáñez de Mendoza. Conceptualization, data curation, formal analysis, methodology, project administration, resources, supervision, validation, writing‐review & editing: Mario Pérez Sayáns Data curation, investigation, methodology, writing‐original draft: Alba Pérez‐Jardón. Data curation, investigation, methodology, writing‐original draft: Cintia M. Chamorro‐Petronacci. Data curation, investigation, methodology, supervision, writing‐original draft: Andrés Blanco‐Carrión. Conceptualization, data curation, formal analysis, investigation, project administration, resources, supervision, validation, writing‐review & editing: José Manuel Aguirre‐Urizar.This research did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors.Authors confirm there is no conflict of interest regarding the submission of this manuscript.Figure S1 PRISMA flow diagram. Synthesis of the bibliographic analysis.Click here for additional data file.Figure S2 RoB. Risk of bias of the included studies according to Quality in Prognosis Studies (QUIPS).Click here for additional data file.


Abies alba Mill.) is one of the most valuable conifer wood species in Europe. Among the main opportunistic pathogens that cause root and butt rot on silver fir are Armillaria ostoyae and Heterobasidion abietinum. Due to the different enzymatic pools of these wood‐decay fungi, different strategies in metabolizing the phenols were available.Silver fir (This work explores the changes in phenolic compounds during silver fir wood degradation.Phenols were analyzed before and after fungus inoculation in silver fir macerated wood after 2, 4 and 6 months. All samples were analyzed using high‐performance liquid chromatography coupled to a hybrid quadrupole‐orbitrap mass spectrometer.p‐coumaric were initially produced and then degraded. Scopoletin was accumulated. Pyrocatechol, acetovanillone and methyl vanillate were found for the first time in both degrading and non‐degrading wood of silver fir.Thirteen compounds, including simple phenols, alkylphenyl alcohols, hydroxybenzoketones, hydroxycinnamaldehydes, hydroxybenzaldehydes, hydroxyphenylacetic acids, hydroxycinnamic acids, hydroxybenzoic acids and hydroxycoumarins, were detected. Pyrocatechol, coniferyl alcohol, acetovanillone, vanillin, benzoic acid, 4‐hydroxybenzoic acid and vanillic acid contents decreased during the degradation process. Methyl vanillate, ferulic acid and Despite differences in the enzymatic pool, both fungi caused a significant decrease in the amounts of phenolic compounds with the accumulation of the only scopoletin. Principal component analysis revealed an initial differentiation between the degradation activity of the two fungal species during degradation, but similar phenolic contents at the end of wood degradation. Phenols were evaluated during silver fir degradation after inoculation with fungi. In plants they constitute one of the most common and widespread groups of substances which arise biogenetically from the pentose phosphate, shikimate and phenylpropanoid pathways. The plant‐associated fungi have symbiotically adopted these pathways into their metabolic cycle and mimic the plants by producing phenols.Armillaria spp. and Heterobasidion spp. have dual life strategies, being necrotrophic on living trees and subsequently saprotrophic on dead wood. As both fungi are considered white rot fungi, they have a versatile machinery of enzymes to attack directly the lignin barrier. The two fungal genera differ also significantly in symptomatology.Armillaria ostoyae strain were publishedHeterobasidion abietinum sequence is not described yet. Interestingly, in comparison with other white rot fungi, Armillaria shows an underrepresentation of ligninolytic gene families and an overrepresentation of pectinolytic gene families.Armillaria spp. as white rot fungus, based on the presence of genes encoding lignin‐decaying enzymes in their genomes.Armillaria species primarily decay the cellulose, hemicellulose and pectin components of the plant cell wall, and leave lignin unattacked during early stages of decay.Heterobasidion spp. have more gene families typically involved in lignin degradation or modification, including laccases and peroxidase.The fungi Heterobasidion spp. to produce laccase in liquid and solid culture conditions, with H. annosum ss. producing ca 5–6 times more laccase than H. parviporum, indicating that great differences exist between Heterobasidion species' abilities to cause wood decay.Heterobasidion irregulare genome revealed a repertoire of genes encoding lignocellulose‐degrading enzymes, including 179 glycoside hydrolases (GHs), eight manganese peroxidases (MnPs) and 17 multicopper oxidases (MCOs).H. annosum ss., the induction of many GHs, MCOs, five MnPs and one oxidoreductase was observed, being specific for wood degradation. A total of 31 predicted GH genes were found upregulated in heartwood, 20 in sapwood and 23 in bark compared to the control.Heterobasidion parviflorum, a close relative of H. abietinum, it was proved that in addition to transcriptome variation, also variation in the methylome (DNA cytosine methylation) is an important epigenetic modification in the lifestyle transition of this fungus.H. annosum were found, including unknown2, unknown3, coniferin, astringin, taxifolin, piceid and isorhapontin, whereas in sapwood the concentrations did not differ following treatment. These results indicate that bark of Sitka spruce has a stronger and earlier response to wounding and pathogen inoculation than sapwood.Results for wood block degradation correlated well with the ability of the 2 and water.para‐coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, linked by ether and carbon–carbon type bonds.White rot fungi have been investigated extensively since the mid‐1980s for their bioremediation capacities;Phanerochaete chrysosporium, during lignin depolymerization.Abies alba Mill.) wood which evaluated the distribution and variation of extractable total phenols and tannins in the logs of four conifers after 1 year on the ground.A. ostoyaeH. abietinum,Heterobasidion annosum F‐group.In order to depolymerize and mineralize the complex lignin molecule, these fungi secrete various combinations of strong extracellular oxidative lignolytic enzymes known as “ligninases”.Various chromatographic techniques have been used to identify and quantify phenolic compounds of lignin, including gas chromatography (GC), liquid chromatography (LC), size exclusion chromatography (SEC), capillary electrophoresis (CE) and two‐dimensional chromatography.Considering the already well‐described lignin degradation realized by rot fungi22.1m/z were dissolved in separate solutions, for a final of eight total mixtures  was purchased from Thermo Fisher Scientific Inc. .LC‐MS grade acetonitrile (99.9%), LC‐MS grade methanol (99.9%) and MS grade formic acid (98%) were purchased from Fluka . Potato dextrose agar (PDA) was purchased from Oxoid . Deionized water used for the preparation of the sample and eluent solutions was obtained using an Arium®Pro Lab Water System . The phenol standards used for quantitative determination were grouped into 15 classes according to their chemical structure2.22O, 100% MeOH and H2O/MeOH at 75:25 (v/v), 50:50 (v/v) and 25:75 (v/v). The different sample preparation tests were conducted on a sample of silver fir sapwood taken from a living tree located in the Trentino Region .The extraction procedure is an important step for the quantification of wood extractives, such as phenolic compounds. The extraction yield of phenolics is affected by several factors, such as the solvents used with varying polarities, the sample‐to‐solvent ratio, extraction time, temperature and the characteristics of the sample.2.3A. ostoyae and H. abietinum, obtained from the fungal culture collection of the Pathology Lab of the Edmund Mach Foundation (TN), were used. In order to promote mycelium growth, both fungal species were axenically cultivated on 20 g L−1 of sterile PDA plates for about 20 days at room temperature. A total of 12 Petri dishes were prepared . After fungal growth, the mycelium of A. ostoyae and H. abietinum was scratched off from the plates and cut into small pieces using a sterile scalpel blade and then inoculated axenically onto 3 g of sterile macerated silver fir wood in 18 sterile glass vials of 10 cm3 , in nine replicates per each fungal species, out of which three were harvested per time point. Macerated wood was prepared by milling sapwood of silver fir with an M20 mechanical mill  to obtain wood chips of about 3 mm in diameter. Silver fir sapwood was previously taken from a living tree located in the “Abeti Soprani” forest in the Molise Region  using a chainsaw. To promote fungal growth in macerated silver fir wood, 5 mL of sterile ultra‐pure water was added in each glass vial.2O/MeOH 75:25, v/v). The solutions were first homogenized for 2 min using an Ultrasonic processor  and were then shaken using a Multi Reax  for 15 min. The samples were centrifuged at 15,000 rpm and 10°C for 5 min and then suspended again with the Multi Reax; afterwards, they were left to rest for 1 hour. Finally, all samples were centrifuged for 5 min at 15,000 rpm, the supernatants were filtered with 0.45‐μm PTFE filter cartridges (Sartorius AG) into analytical 2‐mL vials and 10 μL per sample was injected.Two different species of white rot fungi, 2.4−1. Mass detection was performed in negative ion mode using full MS data‐dependent MS/MS analysis (full MS–dd MS/MS). Full mass spectra were recorded with a resolution of 70,000 full width at half‐maximum , an automatic gain control (AGC) target of 5·105 ions and a maximum injection time (IT) of 150 ms. Data‐dependent mass spectra were recorded with a resolution of 17,500 FWHM , an AGC target of 1·105 ions and an IT of 50 ms. The mass spectrometer was operated using the following parameters: spray voltage, 2.80 kV; sheath gas flow rate, 30 arbitrary units; auxiliary gas flow rate, 20 arbitrary units; capillary temperature, 310°C; capillary gas heater temperature, 280°C. Data acquisition and processing were carried out with Thermo Scientific™ Dionex™ Chromeleon™ 7.2 Chromatography Data System software.The analysis of phenolic compounds was carried out according to the method described by Barnaba et al. (2018). In particular, the identification and quantification of these compounds was performed using a Thermo Ultimate™ 3000 HPLC  coupled to a hybrid quadrupole‐orbitrap mass spectrometer , equipped with heated electrospray ionization (HESI‐II). Chromatographic separation was carried out by injecting 10 μL of sample on an Accucore™ Polar Premium LC column , using a water–acetonitrile gradient at a flow rate of 0.3 mL min2.5p‐coumaric acid, pyrocatechol, scopoletin, vanillic acid and vanillin). Limits of quantification (LOQs) were established as 10 standard deviations of 10 replicated blank samples according to EURACHEM (1993). Method accuracy was estimated as recovery (%) of one sample spiked at two increasing concentration levels, covering the quantitation range of each phenol , each one analytically replicated three times. Precision was assessed as the relative standard deviation (RSD%) of the same experimental samples used for calculating accuracy.The characteristics of the method were studied using the 13 pure standards corresponding to the phenolic compounds quantified in the analyzed samples . Principal component analysis (PCA) was carried out to evaluate the relationships between phenolic compounds at different times of silver fir wood degradation and the activities of two fungal species.Statistical analysis was performed using XLSTAT . Significant differences between the concentrations of phenolic compounds measured during different times of degradation were determined using the Kruskal–Wallis test . The use of different temperatures (30°C and 80°C) to dry the macerated wood samples did not influence the final content of phenolic compounds in the extracted samples . However, several studies showed that the use of high temperatures in the drying process might promote possible concurrent degradation of phenolic compounds.3.2−. Due to confirmed sample compounds, accuracy‐mass tolerance was set at <5 ppm and RT and dd‐MS/MS spectra were compared with those collected from available standards  corresponding to the deprotonated molecules [M‐H]s Figure .−1 of standard mixture solution, while it was between 51% and 106% in the samples supplemented with 1 mg L−1 of standard mixture solution. The precision values  were always below 8% for the samples supplemented with 1 mg/L of standard mixture solution, while for those supplemented with 0.2 mg L−1 the values were always lower than 16%  was detected. To the best of our knowledge, this compound was found for the first time in fresh silver fir wood, while other studies showed the production of pyrocatechol due to the action of ligninolytic enzymes of white rot fungi during lignin degradation.−1) was found. Coniferyl alcohol is one of the main components of lignin, particularly abundant in coniferous species, such as silver fir.−1) were found. These results agree with Zarzyński,−1 and from 0.15 to 0.20 mg kg−1, respectively. These compounds were detected for the first time in silver fir wood. Among hydroxycinnamic acids, a low content of ferulic acid (from 0.05 to 0.50 mg kg−1) was found. This compound was previously identified in wood of other coniferous species, such as Norway spruce, by Metsämuuronen and Siren (2014). Finally, as regards hydroxybenzoic acids, benzoic acid, 4‐hydroxybenzoic acid and vanillic acids were found with remarkable concentrations ranging from 14.2 to 22.8 mg kg−1, from 4.4 to 6.3 mg kg−1 and from 59.3 to 79.7 mg kg−1, respectively.Several phenolic compounds were identified in silver fir wood samples. As reported in Table In particular, among all the phenolic compounds, vanillic acid was the most abundant. Few studies reported the presence of this compound in the bark and wood extract of silver fir.3.4Armillaria and Heterobasidion genera, were evaluated during 6 months of wood degradation. The phenol content was analyzed at 2, 4 and 6 months. Table The changes in the phenolic profiles of silver fir wood, in relation to the activity of the two different fungal species belonging to the Specifically, considering phenols already available in macerated wood, a similar decay pattern was observed for coniferyl alcohol, vanillin, benzoic acid, 4‐hydroxybenzoic acid and pyrocatechol for both rot fungi.−1), complete degradation of this compound was observed during the decay process, differently from what was reported by several studies that observed the production of vanillin during lignin degradation by different species of basidiomycete fungi like Aspergillus sydowii,Pycnoporus cinnabarinusSchizophyllum commune.P. chrysosporium highlighted the production of this compound due to the oxidation of β‐O‐4 linkages of lignin by lignin peroxidase.In particular, vanillin, benzoic acid, 4‐hydroxybenzoic acid and pyrocatechol were rapidly metabolized by both fungal species, showing a decreasing trend during the degradation process with a total consumption of vanillin and pyrocatechol. Considering vanillin, despite starting from high concentrations  from fungal inoculation compared to the other time points Table . This alA. ostoyae the consumption of this phenol was faster and almost complete already after 4 months. As regards acetovanillone, several studies reported the presence of this compound during lignin degradation by white rot fungi.H. abietinum, while an initial accumulation and subsequent degradation appeared in the samples inoculated with A. ostoyae. In the literature, different pathways of ferulic acid degradation induced by different fungal species were reportedH. abietinum. However, in the following months, methyl vanillate was completely consumed  after the inoculation of both fungal species, the coniferylaldehyde concentration exceeded 1 mg kg−1 and subsequently decreased until this compound was almost completely degraded at 6 months (t3). Several studies reported the production of this compound after the enzymatic degradation of coniferyl alcohol by white rot fungi.Trametes versicolor. Several studies reported the presence of p‐coumaric acid due to the action of lignin‐degrading enzymes.−1 and 0.27 mg kg−1 of this phenol after 2 months (t1) with A. ostoyae and H. abietinum, respectively. After that, degradation was observed in both cases, with total consumption only in the samples inoculated with A. ostoyae. Several studies reported different strategies of p‐coumaric acid degradation by various fungal species.P. cinnabarinus, the oxidative degradation of the p‐coumaric acid side chain led to the formation of p‐hydroxybenzoic acid.During wood degradation, the formation of coniferaldehyde, −1 after 6 months (t3) in samples inoculated with A. ostoyae and H. abietinum, respectively. Scopoletin is produced after the degradation of ferulic acid via the phenylpropanoid pathwayFinally, the hydroxycoumarin scopoletin was the only phenol investigated that showed accumulation during degradation, reaching a concentration of 0.6 and 0.9 mg kgArmillaria and Heterobasidion genera, the trends of phenolic compounds were not so variable. However, according to previous studies,P < 0.05; Table The decreases observed for many of the detected phenolic compounds indicated that different fungal species are able to efficiently metabolize most of the phenolic compounds.3.5H. abietinum after 2 months of degradation, while, at the same time, coniferaldehyde and p‐coumaric acid better explained samples inoculated with A. ostoyae. In particular, vanillin and vanillic acid were already present in wood before the degradation process; these two compounds were probably released from the degradation of guaiacyl‐β‐coniferyl ether.H. abietinum, but at the same time a lower consumption of vanillic acid and a higher consumption of ferulic acid, H. abietinum might converted ferulic acid to vanillic acid, as reported for the ubiquitous white rot fungus S. commune.p‐coumaric acid, both were produced due to the action of lignin‐degrading enzymes,p‐coumaric by H. abietinum.PCA was applied for the content of each phenolic compound quantified in macerated silver fir wood samples, in order to evaluate the correlations between phenolic profiles and the activities of two fungal species at different times of wood degradation Figure . PCA, wiSubsequently , a homogenization was observed of the samples based on the degradation of various investigated phenols and the accumulation of scopoletin in samples inoculated with both fungal species, suggesting a possible use of this parameter as a marker indicative of fungal degradation of wood.TABLE S1: Statistical distribution of phenolic content detected in silver fir sawdust samples at time t0 and at different times of silver fir wood degradation  by Armillaria ostoyae and Heterobasidion abietinum. Data are expressed in mg kg−1 for all phenolic compounds.Click here for additional data file.


Cervical squamous cell carcinoma (CSCC), caused by the infection of high‐risk human papillomavirus, is one of the most common malignancies in women worldwide.RNA expression data, including those from the Cancer Genome Atlas, Gene Expression Omnibus, and Genotype‐Tissue Expression databases, were used to identify the expression of RNAs in normal and tumor tissue. Correlation analysis was performed to identify the immune‐related long noncoding RNAs (IRLs) and hypoxia‐related genes (IRHs) that can influence the activity of the immune system. Prognosis models of immune‐related RNAs (IRRs) were used to construct a coexpression network of the immune system. We identified the role of IRRs in immunotherapy by correlation analysis with immune checkpoint genes (ICGs). We then validated the expression data by integrating two single‐cell sequencing data sets of CSCC to identify the key immune features.+CD8+ T‐cells and tumor‐associated macrophages (TAMs) in tumor tissues. TGFBI+ TAMs are a kind of blood‐derived monocyte‐derived M0‐like TAM linked to hypoxia and a poor prognosis. IFI30+ M1‐like TAMs participate in the process of immune‐regulation and showed a role in the promotion of CD8+ T‐cells and Type 1 T helper (Th1)/Th2 cells in the coexpression network, together with several IRLs, IRGs, and ICGs.In total, six immune‐related gene (IRG), four IRL, and five IRH signatures that can significantly influence the characteristics of the tumor immune microenvironment (TIME) were selected using machine learning methods. The expression level of ICGs was significantly upregulated in GZMB+ monocyte‐derived IFI30+ TAMs participated in our coexpression network to regulate the TIME, showing the potential to be a novel immunotherapy target. The enrichment of M0‐like TAMs was associated with a worse prognosis in the high‐risk score group with IRH signatures. Remarkably, M0‐like TAMs in tumor tissues overexpressed TGFBI and were associated with several well‐known tumor‐proliferation pathways.CD16 Cervical squamous cells carcinoma (CESC), caused by the infection of high‐risk human papillomavirus (HR‐HPV). We investigate a combination of multiple immune biomarkers as a clinically signature that can predict prognosis, diagnosis, relapse, and therapy in CESC patients. The coexpression network was constructed by IRRs. Single‐cell analysis has revealed the heterogeneity of immune cells in a variety of cancer types.In recent years, machine learning (ML) has become widely used in various fields.22.1https://xenabrowser.net/) of the University of California. The TCGA–CESC FPKM sequence data were translated to TPM sequence data. Meanwhile, RNA‐seq microarray datasets (GSE138080 and GSE63514) and clinical information of cervical squamous cell carcinoma (CSCC) were downloaded from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds). GSE138080 obtained 10 healthy cervical tissues and 10 CSCC. GSE63514 obtained 24 healthy cervical tissues and 28 tumor tissues (27 CSCC and 1 missing). GSE168652 includes two samples . GSE171894 contains four fresh samples from patients with CSCC. Also, Ensembl identifiers were translated to gene symbols by annotation files (gencode.v22.annotation.gtf3.gz) downloaded from Gencode (https://www.gencodegene.org/) to distinguish between the messenger RNA (mRNA), microRNA, and lncRNA.TCGA and GTEx count data, TCGA–cervical squamous cells carcinoma (CESC) fragments per kilobase of transcript per million mapped reads (FPKM) data, and corresponding clinical information were downloaded from the Xena database . The common Cuffdiff analysis packages (DESeq. 2) were used to identify the differentially expressed genes (DEGs)  in the TCGA–GTEx cohort. The GEO chip microarray data (GSE138080 and GSE63514) were analyzed using the R limma package.The IRGs were downloaded from the IMMPORT database  was applied to confirm the correlation efficiency in screening the IRLs and IRHs in TPM sequence data. A total of 200 HRGs were downloaded from MSigdb hallmark gene sets (http://www.gsea-msigdb.org/gsea/msigdb/).A coexpression strategy  between the two groups was calculated by the Kaplan–Meier method. The receiver operating characteristic (ROC) curve was constructed using the survival ROC package in R to analyze the predictive accuracy. The prediction of it was analyzed by the Kaplan–Meier logrank test and time‐dependent ROC curve analysis. Hazard ratios (HRs) <1 indicated protective genes, while HRs of <1 indicated risk genes. Then, the risk models were built based on multivariable Cox models. The formula of risk score calculation was as follows:2.6http://cis.hku.hk/TISIDB/) with the goal of analyzing the interactions between tumors and the immune system. Immunoinhibitors and immunostimulators that were significantly correlated with IRRs in terms of gene expression were selected (p < .05).Immune checkpoint genes (ICGs) associated with IRRs were downloaded from the TISIDB database  from a 53‐year‐old patient with human papillomavirus 18‐positive CSCC. GSE171894 contains four fresh samples from patients with CSCC who underwent concurrent chemoradiotherapy.https://www.labome.com/method/cells-Markers.html) and existing literature to distinguish different cell types. The R package Seurat was used to complete the initial quality control process .The top 2000 variable genes were used in the follow‐up analysis. Cell markers were downloaded from cellsMarker (2.8The Monocle2 package (version 2.8.0) was used to analyze single‐cell trajectories to discover the cell state transitions. The trajectory was visualized as a two‐dimensional t‐distributed stochastic neighbor embedding (t‐SNE) graph, and dynamically expressed heatmaps were constructed using the plot_pseudo‐time_heatmap function.2.9CellPhoneDB2.10https://string-db.org/). The output data were entered into Cytoscape (version 3.8.2) to construct the PPI network.A protein–protein interaction (PPI) network was created using the STRING database (2.11https://bioinfogp.cnb.csic.es/tools/venny).Data were analyzed by R (version 3.6.3). The Venn plot was drawn by Venny (2.12https://www.proteinatlas.org/). CancerSEA is a database designed to decode 14 functional states of cancer cells at a single‐cell resolution, which includes 41,900 cancer single cells from 25 cancer types.The protein expression immunochemistry image was obtained from online datasets The Human Protein Atlas . Second, we removed DEIs (p > .05) that showed no significant effect on prognosis in public online databases . Finally, we built a diagnosis signature by logistic regression based on eight DEIs, and area under the ROC curve (AUC) values (best models) were selected by a Gaussian mixture model (GMM‐based clustering). There were a total of 255 different gene‐combination formulas and a total of 255 logistic regression models  was 1  and a testing set (n = 152) randomly; then, we calculated the Gaussian distribution of the relapse rates of the two sets and found that, in Cluster 9 of the training set, the prediction rate was 0.69, while the prediction rate of the testing set was 0.62 . First, 11 DEIs associated with OS were selected by univariate Cox regression  revealed that most (65/76 DEGs) of the HRGs had a very strong association with 597 differentially expressed IRGs in TCGA corhorts. These IRHs include 32 downregulated genes and 44 upregulated genes. With the univariate Cox regression and Lasso algorithm ] + [ISG20 expression × (−0.3295)] + [TGFBI expression × (0.2535)] + [PLIN2 expression × (0.2101)] + [LOX expression × (0.2065)]. Patients in the training set and testing set were divided into two high‐ and low‐risk groups according to the median risk score. Kaplan–Meier survival analysis  and testing (p = .023) sets. The AUC values were 0.808 and 0.794, respectively, for the training set and testing set  were positively correlated with a variety of ICGs  and marker genes of different immune cells, such as CD8+ T‐cells  and B‐cells (CD19), forming a network of connections between myeloid cells and T‐cells. This indicates that the complexity of IFI30+ TAMs in promoting immune effects, which may cause T‐cells to fatigue while stimulating T‐cell toxicity. A boxplot  with M2‐like macrophages. IFI30 is positively correlated (p < .01) with M1‐like macrophages. TGFBI is positively correlated (p < .01) with M0‐like macrophages. For IRHs  appeared in the high‐risk score group relative to the whole cohort, with a lower percentage of CD8+ T‐cells (p < .001). A total of 14,852 lncRNA expression profiles in the TCGA–GTEx dataset were obtained in this study. A total of 305 IRLs were screened out by coexpression analysis from 1187 differentially expressed lncRNAs. The correlations between IRGs and ICG are shown in Figure IFI30, GZMB, ISG20, AC092580.4, AC017002.1, and AC002331.1 are strongly correlated with ICGs . The correlations  signaling pathways, cell cycle pathways, tumor necrosis factor (TNF)‐κB signaling pathways, Janus kinase–signal transducer and activator of transcription signaling pathways, and apoptosis pathways, were enriched in high‐level expression groups. The expressions of IFI30 and TGFBI in tumor and normal tissues were verified by THPA database  tended to be expressed in CD8+ T‐cells, natural killer (NK) cells, and Treg cells. LGALS9, HAVCR2, and IL10RB tended to be expressed in macrophages. The expressions of CTLA4, TIGIT, and HAVCR2 in this depleted T‐cell subset were significantly higher than those of PD‐L1 (CD274) and LAG3, indicating that TNFRSF9, CTLA4, TIGIT, and HAVCR2 could be better targeted for CC immunotherapy.We examined the expression of 70 common ICGs in TCGA–GTEx datasets. We tested the expression levels of 40 differentially expressed ICGs in the single‐cell sequencing data of GSE171884. The ratios of these 40 genes, including 36 upregulated genes and 4 downregulated genes, as well as 25 stimulators and 15 inhibitors, are presented in Figure 3.7S100A7, SPRR3, SPP1, FABP4, KRTDAP, APOD, SPRR2A, LYPD2, YZ, and C1QB) are pictured in Figure + T‐cells, and macrophages. The top five markers of the main cell lineages were visualized using a dot plot  in tumor tissues were higher than those in normal tissues . The GO terms showed “regulation of apoptotic process” and “cell migration” were enriched for macrophages. GO terms including “regulation of cell differentiation” and “regulation of cell death” were enriched for fibroblast cells. GO terms “regulation of hypoxia” and “regulation of programmed cells death” were enriched in epithelial cells (most of which were tumor cells). In smooth muscle cells, GO terms included “epithelium development” and “positive regulation of developmental process.” GO terms including “innate immune response” and “immune effector process” were enriched in T‐cells. GO terms “cell–cell signaling” and “positive regulation of signaling” were enriched in endo cells. The death of T‐cells was related to the prognosis of patients.GSE168652 contained two samples, including adjacent normal tissues and tumor tissues, from the same patient with cervical squamous carcinoma. The raw data included 20,277 unique features and 11,394 cells from normal tissues and 13,104 cells from tumor cells. Through the quality control process, we determined 30 principal components used for clustering and t‐SNE with an elbow plot Figure . The topt Figure . Most ofs Figure . The cons Figure . The moss Figure  of each 3.8+ T‐cell subgroups  and GZMK (GZMK+ CD8+ T‐cells). GZMB is an apoptosis‐related gene, which cleaves and activates GSDME and caspase‐3, NK cells, and CD8+ killer lymphocytes, while chimeric antigen receptor T‐cells trigger pyrolysis. The trajectory of CD8+ T‐cells was visualized as a t‐SNE plot. GZMB+ CD8+ T‐cells were downstream in the development time, showing a state of decay. However, GZMK+ CD8+ T‐cells also tend to convert into GZMB+ CD8+ T‐cells. It was found that the expressions of CTLA4 and TIGIT in GZMB+ CD8+ T‐cells were relatively higher than those in GZMK+ CD8+ T‐cells . According to reports, these molecules can inhibit the activity of CD8+ T‐cells by communicating with dendritic cells and macrophages. To observe the process of T‐cell exhaustion (time trajectory), we found through chronological analysis that CD8+ T‐cells from the active Cluster 2 tend to transform into debilitating T‐cells. In addition, clusters of T‐cells  representing exhaustion appeared at the end of the differentiation trajectory, during this process. TIM‐3 and TIGIT have been reported as marker molecules of T‐cell exhaustion in chronic viral infections and cancer models. To investigate molecular interaction networks, we found that the high expression of the Havcr2 gene in Cluster 9 may lead to immunosuppression, culminating in the failure of immunotherapy. Interestingly, we noticed that the toxicity gene and CXCL13, which dictates effective responses to programmed death ligand 1 blockade genes, are upregulated during tumor cell evolution  and tumor epithelial cells. The macrophages in GSE168652 were divided into normal and tumor groups. Through a pseudotime analysis, we found that macrophages in adjacent normal tissues tended to convert into tumor‐infiltration macrophages  and the cell–cell communication molecules in the TME. The interaction network was visualized by R. Macrophages possessed the most interaction pairs with cells from other lineages Figure , revealis Figure . The vols Figure . The imms Figure . We calcs Figure , upper. s Figure , lower. s Figure  of norma3.10SPP1, TGFBI, IFI30, and C1QC) in myeloid cell clusters from colon cancer (GSE146771) are shown by a TSNE plot in Figure + M0‐like TAMs are a kind of CD14+ CD16− blood‐derived monocytes. The expressions of five genes  are shown by a t‐SNE plot in Figure + TAMs, SPP1+ TAMs, and CD16+ blood‐derived monocyte IFI30+ TAMs are three significantly different TAM subgroups in six cancers . The correlations between these genes and 14 functional states indicate that TGFBI has a strong positive correlation with EMT, hypoxia, metastasis, invasion, and angiogenesis. SPP1 is found to be mainly involved in EMT, metastasis, and hypoxia.The expressions of four genes . We also examined the expression of HRGs, most of which had a strong correlation with IRGs. PPI network analysis showed that IRHs had strong correlations with each other, except ISG20 (which was overlapped in IRG models). This study constructed risk score models for the IRHs. Patients in the low‐risk IRH group had a longer median OS than those in the high‐risk group, indicating that a hypoxic environment leads to a poor prognosis. The risk score showed that regulatory T‐cells, Th cells, and M0 and M1 macrophages were related to OS. We built a coexpression network between IRMs and IRLs. The IRLs associated with a good prognosis were AC092580.4, AC017002.1, and AC002331.1. The IRHs associated with a good prognosis were ISG20 and BCL2. Finally, the IRHs associated with a bad prognosis were LOX, PLIN2, and TGFBI.We tried to use Gaussian regression models to select the best logistic regression models to diagnose and predict the relapse of TCGA CC patients. Finally, we had a combination of six genes , this indicates that TNFRSF9, CTLA4, TIGIT, and HAVCR2 may be better targets for immunotherapy, while CXCL12 is upregulated during the evolution of CD8+ T‐cells and may be a marker of effective response to programmed death ligand 1 blockade. GZMB, IFI30, AC092580.4, AC017002.1, and AC002331.1 had strong correlations with ICGs , which are highly expressed in CD8+ T‐cells and TAMs.In comparing the occurrence and development of normal tissues and tumor tissues, we found that the expression levels of genes encoding activation and cytotoxic molecules, such as IFI30, and TGFBI in macrophages were higher than in other cells. SPP1 and TGFBI were also poor prognostic markers and IFI30 was a good prognostic marker of CC, respectively. The proportions of immune cells in tumor tissues appear higher than those in normal tissues. We checked the correlations between IRRs and macrophages, especially C1QC+ TAMs and SPP1+ TAMs. IFI30 participates in the process of immune‐regulation, which plays a role in the promotion of C1QC+ TAMs to CD8+ T‐cells in the co‐expression network. The influence of C1QC+ TAMs and CD8+ T‐cells was not only affected by the interaction of ligands and receptors but also the regulation of the mRNA and lncRNA coexpression network. TGFBI+ M0‐like TAMs are a kind of blood‐derived monocyte‐derived TAMs gradually polarized to M2 under the influence of hypoxia‐influencing factors after entering tumor tissue. CD14+ TGFBI+ M0‐like TAMs compose the greatest proportion of total TAMs and are related to tumor angiogenesis, hypoxic necrosis, and tumor necrosis factor signaling and PI3K–AKT signaling pathways.In this study, we used single‐cell sequencing to identify the expression of IRRs in normal and tumor tissues. The expressions of SPP1, +CD8+ T‐cells, GZMB+CD8+ T‐cells, IFI30+C1QC+ TAMs, and TAMs play vital roles in TME. We analyzed the communication relationship between tumor cells, then finally assessed the immune microenvironment formed by these IRRs and immune cells and the impact of these on the patient's recurrence, prognosis, and diagnosis. Immune system deficiency plays an important role in the progression of CC and the immune microenvironment of CC tissue is the key to personalized cancer treatment.From the perspective of IRRs and immune‐related cells, we established immune‐related hypoxia prognosis, immune‐related lncRNA, and immune cell prognosis models. We also used Gaussian distribution to develop IRG models that have significant predictive properties for recurrence and diagnosis. We analyzed the interaction network of IRGs and lncRNA related to OS and analyzed the impact of hypoxia factors on the immune environment. Through two single‐cell sequencing analyses of CSCC, we identified the expression of these genes on tumors and normal cells. The GZMKKun Deng contributed to the conception of the study. Ruiling Yin performed the data analyses and manuscript writing. Hongyan Han, Xuedong Tong, Yan Li, and Xiuming Zhai contributed significantly in writing the manuscript. All authors read and approved the final manuscript.The authors declare no conflicts of interest.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.


Although metoclopramide has many adverse effects, torsade de pointes (TdP) is rare. We describe a fatal case of repeated ventricular fibrillation due to TdP following repeated administration of metoclopramide. Administration of multiple doses of metoclopramide over a short time to a patient with risk factors for TdP should be avoided. Although metoclopramide has many adverse effects, torsade de pointes (TdP) is rare. Repeated administration of metoclopramide in combination with risk factors for TdP may cause ventricular fibrillation following TdP. Her medical history consisted of a complete atrioventricular block on a DDD pacemaker with basic rate 60 bpm and upper rate response 130 bpm, atrial fibrillation, ischemic cardiac disease, and chronic kidney disease. She had been taking enalapril, spironolactone, azosemide, carvedilol, apixaban, rabeprazole, rosuvastatin, nicorandil, and zolpidem. On admission, her body temperature was 36.7°C, blood pressure 109/75 mmHg, heart rate 58 bpm, and oxygen saturation 98% on room air. She reported slight tenderness on percussion in the right hypochondriac region. The laboratory data showed leukocytes 20,600/μl, aspartate aminotransferase 27 U/L, alanine aminotransferase 41 U/L, creatinine 2.07 mg/dl, potassium concentration 4.0 mmol/L, magnesium concentration 1.8 mg/dl, and C‐reactive protein 25.9 mg/dl. Her electrocardiogram (ECG) revealed QTc interval of 453 ms Figure . Her ech3QT prolongation caused by metoclopramide has been reported in some studies.To our knowledge, there have only been three surviving cases of TdP associated with metoclopramide administration Table . All casInformation on sudden cardiac death from metoclopramide is limited. A case–control study reported that the adjusted odds ratio of metoclopramide for sudden cardiac death compared to no exposure was 4.31.Intravenous magnesium is a first‐line therapy, and maintaining potassium levels has been recommended in patients with TdP.Metoclopramide may promote QT prolongation and lead to VF following TdP. Administration of multiple doses of metoclopramide over a short time to a patient with risk factors for TdP should be avoided.YW, YT, and IN were involved in the clinical management. YW wrote the manuscript. IN and HW was involved in the revision of the manuscript. All authors approved the final version for submission.The authors declare that they have no conflicts of interest to disclose.Ethical approval was not required. Informed consent was obtained from the patient.Written informed consent was obtained from the patient to publish this report in accordance with the journal's patient consent policy.