Long-Term Outcome of Photobiomodulation Therapy for Refractory Diabetic Wound After Secretory Carcinoma of the Parotid Gland Surgery: A Case Report.

Objective: To evaluate the efficacy and safety of photobiomodulation therapy (PBMT) in the treatment of diabetic patients with refractory wound. Background: Refractory wound is one of the most challenging clinical complications of diabetes mellitus. Studies have shown that PBMT can promote wound healing in many ways. Methods: We reported a 55-year-old male patient with refractory diabetic wound after secretory carcinoma of the parotid gland surgery responding to 810 nm laser. Results: After PBMT, the refractory diabetic wound healed gradually without adverse events. During follow-up 5-years, the healed wound remained stable and showed no signs of recurrence. Conclusions: PBMT can be potentially considered as a therapeutic method in diabetic patients with refractory diabetic wound.

CXCL family-related classification predicts prognosis and response to immunotherapy in patients with head and neck squamous cell carcinoma based on TCGA and GEO databases.

Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent malignant cancer worldwide. The cysteine X cysteine (CXC) chemokine family contains 17 members, which are reportedly crucial for the growth, invasion, metastasis, and microenvironment of tumor cells. Although the precise functions of CXC ligands (CXCLs) in HNSCC are unclear, these proteins may play important roles in controlling tumor growth and forming the tumor immune environment. Methods: We downloaded the RNA sequencing and matched clinicopathological data of 379 patients with HNSCC as the training set from The Cancer Genome Atlas and two datasets from the Gene Expression Omnibus for use as validation sets. Results: Through consensus clustering, we identified two subtypes of HNSCC associated with the CXCL family, named cluster1 and cluster2. Patients with the cluster1 subtype showed favourable clinical outcomes, significant immune cell infiltration, and improved immune response signalling pathway modulation. We also developed a nomogram of CXCL family scores for therapeutic use and for predicting the overall survival (OS) of patients with HNSCC. Patients with lower scores showed longer OS and higher immune cell infiltration in their tissues. Conclusions: We developed a new classification method for HNSCC using the CXCL gene family, which can be used clinically to evaluate the prognosis and response to immunotherapy in patients with HNSCC.

Unveiling the immune symphony: decoding colorectal cancer metastasis through immune interactions.

Colorectal cancer (CRC), known for its high metastatic potential, remains a leading cause of cancer-related death. This review emphasizes the critical role of immune responses in CRC metastasis, focusing on the interaction between immune cells and tumor microenvironment. We explore how immune cells, through cytokines, chemokines, and growth factors, contribute to the CRC metastasis cascade, underlining the tumor microenvironment's role in shaping immune responses. The review addresses CRC's immune evasion tactics, especially the upregulation of checkpoint inhibitors like PD-1 and CTLA-4, highlighting their potential as therapeutic targets. We also examine advanced immunotherapies, including checkpoint inhibitors and immune cell transplantation, to modify immune responses and enhance treatment outcomes in CRC metastasis. Overall, our analysis offers insights into the interplay between immune molecules and the tumor environment, crucial for developing new treatments to control CRC metastasis and improve patient prognosis, with a specific focus on overcoming immune evasion, a key aspect of this special issue.

Classification of SLC family-related genes involved in ferroptosis predicts lung cancer prognosis and immunotherapy response.

Lung adenocarcinoma, the most frequent type of lung cancer, is the leading cause of cancer-related deaths worldwide. Ferroptosis, controlled cell death that involves a high degree of iron-dependent lipid peroxidation, has been linked to tumor therapy sensitivity, patient prognosis, and cancer development. The solute carrier superfamily has over 400 members and comprises the largest class of transporters in the human genome. Solute carrier proteins can facilitate the movement of different substrates across biological membranes, which is crucial for physiological activities, including ferroptosis. Here, we developed a new model to further explore the role of the solute carrier family in ferroptosis in the lung adenocarcinoma immunological milieu. We used consensus clustering to classify patients with lung cancer into two subgroups (cluster1 and cluster2). Patients in the cluster1 subtype had a better prognosis and higher immune cell infiltration ratios than those in the cluster2 subtype. Furthermore, to evaluate the prognosis, the immune cell infiltration ratio, and the medication sensitivity of patients with lung adenocarcinoma, we developed gene scores related to the solute carrier family. In conclusion, we successfully developed a model incorporating the solute carrier family and ferroptosis to predict survival and the impact of immunotherapy on patients with lung cancer.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay.

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

FAM family gene prediction model reveals heterogeneity, stemness and immune microenvironment of UCEC.

Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients. Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs. Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy. Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing.

Background: Hepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients. Methods: The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR. Result: 11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong. Conclusion: TEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.

Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics.

Purpose: By using a radiomics-based approach, multiple radiomics features can be extracted from regions of interest in computed tomography (CT) images, which may be applied to automatically classify kidney tumors and normal kidney tissues. The study proposes a method based on CT radiomics and aims to use extracted radiomics features to automatically classify of kidney tumors and normal kidney tissues and to establish an automatic classification model. Methods: CT data were retrieved from the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19) in The Cancer Imaging Archive (TCIA) open access database. Arterial phase-enhanced CT images from 210 cases were used to establish an automatic classification model. These CT images of patients were randomly divided into training (168 cases) and test (42 cases) sets. Furthermore, the radiomics features of gross tumor volume (GTV) and normal kidney tissues in the training set were extracted and screened, and a binary logistic regression model was established. For the test set, the radiomic features and cutoff value of P were consistent with the training set. Results: Three radiomics features were selected to establish the binary logistic regression model. The accuracy (ACC), sensitivity (SENS), specificity (SPEC), area under the curve (AUC), and Youden index of the training and test sets based on the CT radiomics classification model were all higher than 0.85. Conclusion: The automatic classification model of kidney tumors and normal kidney tissues based on CT radiomics exhibited good classification ability. Kidney tumors could be distinguished from normal kidney tissues. This study may complement automated tumor delineation techniques and warrants further research.

Identification and validation of neurotrophic factor-related genes signature in HNSCC to predict survival and immune landscapes.

Background: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common type of cancer worldwide. Its highly aggressive and heterogeneous nature and complex tumor microenvironment result in variable prognosis and immunotherapeutic outcomes for patients with HNSCC. Neurotrophic factor-related genes (NFRGs) play an essential role in the development of malignancies but have rarely been studied in HNSCC. The aim of this study was to develop a reliable prognostic model based on NFRGs for assessing the prognosis and immunotherapy of HNSCC patients and to provide guidance for clinical diagnosis and treatment. Methods: Based on the TCGA-HNSC cohort in the Cancer Genome Atlas (TCGA) database, expression profiles of NFRGs were obtained from 502 HNSCC samples and 44 normal samples, and the expression and prognosis of 2601 NFRGs were analyzed. TGCA-HNSC samples were randomly divided into training and test sets (7:3). GEO database of 97 tumor samples was used as the external validation set. One-way Cox regression analysis and Lasso Cox regression analysis were used to screen for differentially expressed genes significantly associated with prognosis. Based on 18 NFRGs, lasso and multivariate Cox proportional risk regression were used to construct a prognostic risk scoring system. ssGSEA was applied to analyze the immune status of patients in high- and low-risk groups. Results: The 18 NFRGs were considered to be closely associated with HNSCC prognosis and were good predictors of HNSCC. The multifactorial analysis found that the NFRGs signature was an independent prognostic factor for HNSCC, and patients in the low-risk group had higher overall survival (OS) than those in the high-risk group. The nomogram prediction map constructed from clinical characteristics and risk scores had good prognostic power. Patients in the low-risk group had higher levels of immune infiltration and expression of immune checkpoints and were more likely to benefit from immunotherapy. Conclusion: The NFRGs risk score model can well predict the prognosis of HNSCC patients. A nomogram based on this model can help clinicians classify HNSCC patients prognostically and identify specific subgroups of patients who may have better outcomes with immunotherapy and chemotherapy, and carry out personalized treatment for HNSCC patients.

Putative rabbit conjunctival epithelial stem/progenitor cells preferentially reside in palpebral conjunctiva.

PURPOSE: Conjunctival epithelia play an important role in forming a physical protective barrier of the ocular surface. To protect the integrity of the ocular surface, the conjunctival epithelial cells (CECs) must be self-renewing from local stem cells. In this study, the distribution of conjunctival epithelial stem/progenitor cells was determined. METHODS: In a long-term bromodeoxyuridine (BrdU) label-retaining study, adult New Zealand white rabbits were injected intraperitoneally with BrdU 50 mg/kg/d for 7 days and follow-up for 6 weeks. In the short-term BrdU label-retaining study, rabbits were injected intraperitoneally with BrdU 50 mg/kg once and BrdU-labeled CECs were detected after 1, 3, 5, and 7 days, respectively. In vitro, growth kinetics, proliferating cell nuclear antigen (PCNA) immunocytochemical staining and colony-forming abilities of rabbit palpebral, fornical and bulbar CECs cultured in an identical environment were investigated and compared between different parts. RESULTS: In the long-term BrdU label-retaining study, BrdU-labeled cells concentrated at the mucocutaneous junction. In the short-term BrdU label-retaining study, the peak concentration of BrdU label-retaining conjunctival epithelial cells progressively migrated from the mucocutaneous junction to the fornix in rabbits 1, 3, 5 and 7 days postinjection respectively, but a focus of BrdU-labeled cells remained in the mucocutaneous junction at all postinjection intervals. Rabbit palpebral CECs grew faster and assigned more PCNA-positive cells and higher colony-forming abilities than the other two parts in vitro. CONCLUSIONS: Long-term retention of BrdU-labeled cells at the mucocutaneous junction indicated that slow-cycling stem cells may locate at this position. The epithelial stem/progenitor cells at the mucocutaneous junction may serve as an origination of transient amplifying cells that migrate towards the fornix. Rabbit palpebral CECs (including mucocutaneous junction epithelial cells) possess higher self-renewing ability in vitro. Collectively, conjunctival epithelial stem/progenitor cells in the rabbit may preferentially reside in palpebral conjunctiva, especially the mucocutaneous junction.