Genome-wide comparative analyses highlights selection signatures underlying saline adaptation in Chilika buffalo.

Chilika, a native buffalo breed of the Eastern coast of India, is mainly distributed around the Chilika brackish water lake connected with the Bay of Bengal Sea. This breed possesses a unique ability to delve deep into the salty water of the lake and stay there to feed on local vegetation of saline nature. Adaptation to salinity is a genetic phenomenon, however, the genetic basis underlying the salinity tolerance is still limited in animals specifically in livestock. The present study explores the genetic evolution that unveils the Chilika buffalo's adaptation to the harsh saline habitat (water and food system). For this study, whole genome resequencing data on 18 Chilika buffalo and for comparison 10 Murrah buffalo of normal habitat were generated. For identification of selection sweeps, intrapopulation and interpopulation statistics were employed. A total of 709, 309, 468, and 354 genes were detected having selection sweeps in Chilika buffalo using the nucleotide diversity (θπ), Tajima's D, nucleotide diversity ratio (θπ-ratio), and FST methods, respectively. Further analysis revealed a total of 23 genes including EXOC6B, VPS8, LYPD1, VPS35, CAMKMT, NCKAP5, COMMD1, MYLK3, B3GNT2 were found to be common by all the methods. Furthermore, functional annotation study of identified genes provided pathways such as MAPK signaling, renin secretion, endocytosis, oxytocin signaling pathway, etc. Gene network analysis enlists hub genes, provide insights into their interactions with each other. In conclusion, this study has highlighted the genetic basis underlying the local adaptive function of Chilika buffalo under saline environment.

The Novel-B-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Esophageal Carcinoma.

BACKGROUND: The current understanding of the prognostic significance of B cells and their role in the tumor microenvironment (TME) in esophageal carcinoma (ESCA) is limited. METHODS: We conducted a screening for B-cell-related genes through the analysis of single-cell transcriptome data. Subsequently, we developed a B-cell-related gene signature (BRGrisk) using LASSO regression analysis. Patients from The Cancer Genome Atlas cohort were divided into a training cohort and a test cohort. Patients were categorized into high- and low-risk groups based on their median BRGrisk scores. The overall survival was assessed using the Kaplan-Meier method, and a nomogram based on BRGrisk was constructed. Immune infiltration profiles between the risk groups were also compared. RESULTS: The BRGrisk prognostic model indicated significantly worse outcomes for patients with high BRGrisk scores (p < 0.001). The BRGrisk-based nomogram exhibited good prognostic performance. Analysis of immune infiltration revealed that patients in the high-BRGrisk group had notably higher levels of immune cell infiltration and were more likely to be in an immunoresponsive state. Enrichment analysis showed a strong correlation between the prognostic gene signature and cancer-related pathways. IC50 results indicated that patients in the low-BRGrisk group were more responsive to common drugs compared to those in the high-BRGrisk group. CONCLUSIONS: This study presents a novel BRGrisk that can be used to stratify the prognosis of ESCA patients and may offer guidance for personalized treatment strategies aimed at improving prognosis.

Identification of a novel lactylation-related gene signature predicts the prognosis of multiple myeloma and experiment verification.

Multiple myeloma (MM) is an incurable hematological malignancy with poor survival. Accumulating evidence reveals that lactylation modification plays a vital role in tumorigenesis. However, research on lactylation-related genes (LRGs) in predicting the prognosis of MM remains limited. Differentially expressed LRGs (DELRGs) between MM and normal samples were investigated from the Gene Expression Omnibus database. Univariate Cox regression and LASSO Cox regression analysis were applied to construct gene signature associated with overall survival. The signature was validated in two external datasets. A nomogram was further constructed and evaluated. Additionally, Enrichment analysis, immune analysis, and drug chemosensitivity analysis between the two groups were investigated. qPCR and immunofluorescence staining were performed to validate the expression and localization of PFN1. CCK-8 and flow cytometry were performed to validate biological function. A total of 9 LRGs (TRIM28, PPIA, SOD1, RRP1B, IARS2, RB1, PFN1, PRCC, and FABP5) were selected to establish the prognostic signature. Kaplan-Meier survival curves showed that high-risk group patients had a remarkably worse prognosis in the training and validation cohorts. A nomogram was constructed based on LRGs signature and clinical characteristics, and showed excellent predictive power by calibration curve and C-index. Moreover, biological pathways, immunologic status, as well as sensitivity to chemotherapy drugs were different between high- and low-risk groups. Additionally, the hub gene PFN1 is highly expressed in MM, knocking down PFN1 induces cell cycle arrest, suppresses cell proliferation and promotes cell apoptosis. In conclusion, our study revealed that LRGs signature is a promising biomarker for MM that can effectively early distinguish high-risk patients and predict prognosis.

Insights into treatment-specific prognostic somatic mutations in NSCLC from the AACR NSCLC GENIE BPC cohort analysis.

BACKGROUND: Treatment of non-small lung cancer (NSCLC) has evolved in recent years, benefiting from advances in immunotherapy and targeted therapy. However, limited biomarkers exist to assist clinicians and patients in selecting the most effective, personalized treatment strategies. Targeted next-generation sequencing-based genomic profiling has become routine in cancer treatment and generated crucial clinicogenomic data over the last decade. This has made the development of mutational biomarkers for drug response possible. METHODS: To investigate the association between a patient's responses to a specific somatic mutation treatment, we analyzed the NSCLC GENIE BPC cohort, which includes 2,004 tumor samples from 1,846 patients. RESULTS: We identified somatic mutation signatures associated with response to immunotherapy and chemotherapy, including carboplatin-, cisplatin-, pemetrexed- or docetaxel-based chemotherapy. The prediction power of the chemotherapy-associated signature was significantly affected by epidermal growth factor receptor (EGFR) mutation status. Therefore, we developed an EGFR wild-type-specific mutation signature for chemotherapy selection. CONCLUSION: Our treatment-specific gene signatures will assist clinicians and patients in selecting from multiple treatment options.

A mitochondria-related genes associated neuroblastoma signature - based on bulk and single-cell transcriptome sequencing data analysis, and experimental validation.

Background: Neuroblastoma (NB), characterized by its marked heterogeneity, is the most common extracranial solid tumor in children. The status and functionality of mitochondria are crucial in regulating NB cell behavior. While the significance of mitochondria-related genes (MRGs) in NB is still missing in key knowledge. Materials and methods: This study leverages consensus clustering and machine learning algorithms to construct and validate an MRGs-related signature in NB. Single-cell data analysis and experimental validation were employed to characterize the pivotal role of FEN1 within NB cells. Results: MRGs facilitated the classification of NB patients into 2 distinct clusters with considerable differences. The constructed MRGs-related signature and its quantitative indicators, mtScore and mtRisk, effectively characterize the MRGs-related patient clusters. Notably, the MRGs-related signature outperformed MYCN in predicting NB patient prognosis and was adept at representing the tumor microenvironment (TME), tumor cell stemness, and sensitivity to the chemotherapeutic agents Cisplatin, Topotecan, and Irinotecan. FEN1, identified as the most contributory gene within the MRGs-related signature, was found to play a crucial role in the communication between NB cells and the TME, and in the developmental trajectory of NB cells. Experimental validations confirmed FEN1's significant influence on NB cell proliferation, apoptosis, cell cycle, and invasiveness. Conclusion: The MRGs-related signature developed in this study offers a novel predictive tool for assessing NB patient prognosis, immune infiltration, stemness, and chemotherapeutic sensitivity. Our findings unveil the critical function of FEN1 in NB, suggesting its potential as a therapeutic target.

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis.

Background: Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis. Methods: CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay. Results: Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls. Conclusion: This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products.

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

Combining single-cell and bulk RNA sequencing, NK cell marker genes reveal a prognostic and immune status in pancreatic ductal adenocarcinoma.

The NK cell is an important component of the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), also plays a significant role in PDAC development. This study aimed to explore the relationship between NK cell marker genes and prognosis, immune response of PDAC patients. By scRNA-seq data, we found the proportion of NK cells were significantly downregulated in PDAC and 373 NK cell marker genes were screened out. By TCGA database, we enrolled 7 NK cell marker genes to construct the signature for predicting prognosis in PDAC patients. Cox analysis identified the signature as an independent factor for pancreatic cancer. Subsequently, the predictive power of signature was validated by 6 GEO datasets and had an excellent evaluation. Our analysis of relationship between the signature and patients' immune status revealed that the signature has a strong correlation with immunocyte infiltration, inflammatory reaction, immune checkpoint inhibitors (ICIs) response. The NK cell marker genes are closely related to the prognosis and immune capacity of PDAC patients, and they have potential value as a therapeutic target.

A novel amino acid metabolism-related gene signature to predict the overall survival of esophageal squamous cell carcinoma patients.

Background: Esophageal squamous cell carcinoma (ESCC) has a poor early detection rate, prognosis, and survival rate. Effective prognostic markers are urgently needed to assist in the prediction of ESCC treatment outcomes. There is accumulating evidence of a strong relationship between cancer cell growth and amino acid metabolism. This study aims to determine the relationship between amino acid metabolism and ESCC prognosis. Methods: This study comprehensively evaluates the association between amino acid metabolism-related gene (AAMRG) expression profiles and the prognosis of ESCC patients based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the expression of prognosis-related genes. Results: A univariate Cox regression analysis of TCGA data identified 18 prognosis-related AAMRGs. The gene expression profiles of 90 ESCC tumor and normal tissues were obtained from the GSE20347 and GSE67269 datasets. Two differently expressed genes (DEGs) were considered as ESCC prognosis-related genes; and they were branched-chain amino acid transaminase 1 (BCAT1) and methylmalonic aciduria and homocystinuria type C protein (MMACHC). These two AAMRGs were used to develop a novel AAMRG-related gene signature to predict 1- and 2-year prognostic risk in ESCC patients. Both BCAT1 and MMACHC expression were verified by RT-qPCR. A prognostic nomogram that incorporated clinical factors and BCAT1 and MMACHC gene expression was constructed, and the calibration plots showed that it had good prognostic performance. Conclusions: The AAMRG signature established in our study is efficient and could be used in clinical settings to predict the early prognosis of ESCC patients.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer.

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

New HCC Subtypes Based on CD8 Tex-Related lncRNA Signature Could Predict Prognosis, Immunological and Drug Sensitivity Characteristics of Hepatocellular Carcinoma.

Purpose: Hepatocellular carcinoma has become one of the severe diseases threatening human health. T cell exhaustion is deemed as a reason for immunotherapy resistance. However, little is known about the roles of CD8 Tex-related lncRNAs in HCC. Materials and Methods: We processed single-cell RNA sequencing to identify CD8 Tex-related genes. CD8 Tex-related lncRNAs were identified based on their correlations with mRNAs. Unsupervised clustering approach was used to identify molecular clusters of CD8 Tex-related lncRNAs. Differences in prognosis and immune infiltration between the clusters were explored. Machine learning algorithms were used to construct a prognostic signature. Samples were classified as low- and high-risk groups based on their risk scores. We identified prognosis-related lncRNAs and constructed a ceRNA network. In vitro experiments were conducted to investigate the impacts of CD8 Tex-related lncRNAs on proliferation and apoptosis of HCC cells. Results: We clarified cell types within two HCC single-cell datasets. We identified specific markers of CD8 Tex cells and analyzed their potential functions. Twenty-eight lncRNAs were identified as CD8 Tex-related. Based on CD8 Tex-related lncRNAs, samples were categorized into two distinct clusters, which exhibited significant differences in survival rates and immune infiltration. Ninety-six algorithm combinations were employed to establish a prognostic signature. RSF emerged as the one with the highest C-index. Patients in high- and low-risk groups exhibited marked differences in prognosis, enriched pathways, mutations and drug sensitivities. MCM3AP-AS1, MAPKAPK5-AS1 and PART1 were regarded as prognosis-related lncRNAs. A ceRNA network was constructed based on CD8 Tex-related lncRNAs and mRNAs. Experiments on cell lines and organoids indicated that downregulation of MCM3AP-AS1, MAPKAPK5-AS1 and PART1 suppressed cell proliferation and induced apoptosis. Conclusion: CD8 Tex-related lncRNAs played crucial roles in HCC progression. Our findings provided new insights into the regulatory mechanisms of CD8 Tex-related lncRNAs in HCC.

Steroid Profiling and Circadian Cortisol Secretion in Patients with Mild Autonomous Cortisol Secretion: A Cross-Sectional Study.

CONTEXT: Mild autonomous cortisol secretion (MACS) is diagnosed based on post-dexamethasone cortisol>1.8 mcg/dL. Scarce evidence exists on steroid circadian secretion and steroid metabolome in MACS. OBJECTIVE: To characterize 24-hour (h) urine steroid metabolome in patients with MACS and determine circadian differences in urine steroid profiling and cortisol concentrations in patients with MACS versus referent subjects. DESIGN: Cross-sectional study, 2018-2023. SETTING: Referral center. PARTICIPANTS: Patients with MACS and age-, sex-, BMI-, and menopausal status-matched referent subjects. MEASUREMENTS: Urine was collected over 24h period as separate day- and night-time collections. High-resolution mass spectrometry assay was used to measure 25 steroids. A subgroup of patients and referent subjects were admitted for every 2h serum measurements of free and total cortisol. OUTCOMES: Steroids, sums, and ratios. RESULTS: Patients with MACS (n=72) had lower mcg/24h median androgens (2084 vs 3283, P<0.001), higher glucocorticoids (15754 vs 12936, P<0.001), and higher glucocorticoid/androgen ratio (8.7 vs 3.9, P<0.001), compared to referent subjects. Patients also had lower steroid day/night ratios compared to referent subjects, reflecting a higher relative nocturnal steroid production in MACS. In a subgroup of 12 patients with MACS and 10 referent subjects, the 24-hour area under the curves for total and free cortisol were similar. However, evening mean total (5.3 vs 4.0 mcg/dL, P=0.056) and free (0.2 vs 0.1 mcg/dL, P=0.035) cortisol was higher in patients vs referent subjects. CONCLUSION: Patients with MACS demonstrate an abnormal urine steroid metabolome, with a high glucocorticoid to androgen ratio, and a higher nocturnal steroid production.

Use of acoustic signals in Cape fur seal mother-pup reunions: individual signature, signal propagation and pup home range.

The Cape fur seal (Arctocephalus pusillus pusillus) is one of the most colonial mammals, with colonies of up to hundreds of thousands of individuals during the breeding season. During the lactation period, mothers and pups are regularly separated as females undertake multi-day foraging trips at sea. Mothers and pups use a mutual vocal recognition system to reunite after separation. Such communication is highly constrained by both high background noise and risk of individual confusion owing to the density of seals. This study aimed to experimentally assess the acoustic features relevant for mother-pup vocal identification and the propagation properties of their calls. Playback experiments revealed that mother and pup individual vocal signatures rely on both temporal and frequency parameters: amplitude and frequency modulations, timbre and fundamental frequency (f0). This is more parameters than in any colonial species studied so far. The combinational use of acoustic features reinforces the concept that both environmental and social constraints may have acted as selective pressures on the individual vocal recognition systems. Theoretical propagation distances of mother and pup vocalisations were estimated to be below the range of distances at which mother-pup reunions can occur. This suggests that Cape fur seals may have strong abilities to extract vocal signals from the background noise, as previously demonstrated in the highly colonial king penguin. Investigating the transmission of information throughout the propagation of the signal as well as the ability of the receiving individual to decipher vocal signatures is crucial to understanding vocal recognition systems in the wild.

The cytidine deaminase APOBEC3C has unique sequence and genome feature preferences.

APOBEC proteins are cytidine deaminases that restrict the replication of viruses and transposable elements. Several members of the APOBEC3 family, APOBEC3A, APOBEC3B, and APOBEC3H-I, can access the nucleus and cause what is thought to be indiscriminate deamination of the genome, resulting in mutagenesis and genome instability. Although APOBEC3C is also present in the nucleus, the full scope of its deamination target preferences is unknown. By expressing human APOBEC3C in a yeast model system, I have defined the APOBEC3C mutation signature, as well as the preferred genome features of APOBEC3C targets. The APOBEC3C mutation signature is distinct from those of the known cancer genome mutators APOBEC3A and APOBEC3B. APOBEC3C produces DNA strand-coordinated mutation clusters, and APOBEC3C mutations are enriched near the transcription start sites of active genes. Surprisingly, APOBEC3C lacks the bias for the lagging strand of DNA replication that is seen for APOBEC3A and APOBEC3B. The unique preferences of APOBEC3C constitute a mutation profile that will be useful in defining sites of APOBEC3C mutagenesis in human genomes.

Head and neck squamous cell carcinomas of unknown primary: Can ancillary studies help identify more primary tumor sites?

A subset of head and neck squamous cell carcinomas present solely as metastatic disease in the neck and are of unknown primary origin (SCCUP). Most primary tumors will ultimately be identified, usually in the oropharynx. In a minority of cases, the primary site remains elusive. Here, we examine the role of ancillary testing, including mutational signature analysis (MSA), to help identify likely primary sites in such cases. Twenty-two cases of SCCUP in the neck, collected over a 10-year period, were classified by morphology and viral status; including human papillomavirus (HPV) testing by p16 immunohistochemistry (IHC) and RT-qPCR, as well as Epstein-Barr virus (EBV) testing by EBER-ISH. CD5 and c-KIT (CD117) IHC was done to evaluate for possible thymic origin in all virus-negative cases. Whole exome sequencing, followed by MSA, was used to identify UV signature mutations indicative of cutaneous origin. HPV was identified in 12 of 22 tumors (54.5%), favoring an oropharyngeal origin, and closely associated with nonkeratinizing tumor morphology (Fisher's exact test; p = 0.0002). One tumor with indeterminant morphology had discordant HPV and p16 status (p16+/HPV-). All tumors were EBV-negative. Diffuse expression of CD5 and c-KIT was identified in 1 of 10 virus-negative SCCUPs (10%), suggesting a possible ectopic thymic origin rather than a metastasis. A UV mutational signature, indicating cutaneous origin, was identified in 1 of 10 (10%) virus-negative SCCUPs. A cutaneous auricular primary emerged 3 months after treatment in this patient. Primary tumors became clinically apparent in 2 others (1 hypopharynx, 1 hypopharynx/larynx). Thus, after follow-up, 6 tumors remained unclassifiable as to the possible site of origin (27%). Most SCCUPs of the neck in our series were HPV-associated and thus likely of oropharyngeal origin. UV signature mutation analysis and additional IHC for CD5 and c-KIT for possible thymic origin may aid in further classifying virus-negative unknown primaries. Close clinical inspection of hypopharyngeal mucosa may also be helpful, as a subset of primary tumors later emerged at this site.

Unveiling a cuproptosis-related risk model and the role of FARSB in hepatocellular carcinoma.

Background: Cuproptosis, a type of regulated cell death that was recently identified, has been linked to the development of a variety of diseases, among them being cancers. Nevertheless, the prognostic significance and therapeutic implications of the cuproptosis potential index in hepatocellular carcinoma (HCC) remain uncertain. Methods: Single-sample gene set enrichment analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA) methodology was conducted to ascertain the identification of modular genes that are closely linked to cuproptosis. In addition, the gene signature indicative of prognosis was formulated by employing univariate Cox regression analysis in conjunction with a random forest algorithm. The efficacy of this gene signature in predicting outcomes was confirmed through validation in both The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Furthermore, a study was undertaken to evaluate the association between the risk score and various clinical-pathological characteristics, explore the biological processes linked to the gene signature, and analyze tumor mutational burden and somatic mutations. Lastly, potential drugs targeting the identified gene signature were identified through screening. Results: The results of our comprehensive analysis across multiple cancer types demonstrated a positive correlation between an elevated cuproptosis potential index (CPI) and an accelerated rate of tumor progression. Furthermore, employing the WGCNA technique, we successfully identified 640 genes associated with cuproptosis. Among these genes, we meticulously screened and validated a seven-gene signature (TCOF1, NOP58, TMEM69, FARSB, DHX37, SLC16A3, and CBX2) that exhibited substantial prognostic significance. Using the median risk score, the division of HCC patients into cohorts with high- and low-risk highlighted significant disparities in survival results, wherein the group with higher risk exhibited a less favorable overall survival. The risk score exhibited commendable predictive efficacy. Moreover, the in vitro knockdown of FARSB significantly hindered cell viability, induced G1 phase arrest, increased apoptosis, and impaired migration in HepG2 and Huh7 cells. Conclusion: Our research has successfully identified a strong seven-gene signature linked to cuproptosis, which could be utilized for prognostic evaluation and risk stratification in patients with HCC. Furthermore, the discovered gene signature, coupled with the functional analysis of FARSB, presents promising prospects as potential targets for therapeutic interventions in HCC.

A High Immune-Related Index with the Suppression of cGAS-STING Pathway is a Key Determinant to Herceptin Resistance in HER2+ Breast Cancer.

Resistance to HER2-targeted therapy is the major cause of treatment failure in patients with HER2+ breast cancer (BC). Given the key role of immune microenvironment in tumor development, there is a lack of an ideal prognostic model that fully accounts for immune infiltration. In this study, WGCNA analysis was performed to discover the relationship between immune-related signaling and prognosis of HER2+ BC. After Herceptin-resistant BC cell lines established, transcriptional profiles of resistant cell line and RNA-sequencing data from GSE76360 cohort were analyzed for candidate genes. 85 samples of HER2+ BC from TCGA database were analyzed by the Cox regression, XGBoost and Lasso algorithm to generalize a credible immune-related prognostic index (IRPI). Correlations between the IRPI signature and tumor microenvironment were further analyzed by multiple algorithms, including single-cell RNA sequencing data analysis. Patients with high IRPI had suppressive tumor immune microenvironment and worse prognosis. The suppression of type I interferon signaling indicated by the IRPI in Herceptin-resistant HER2+ BC was validated. And we elucidated that the suppression of cGAS-STING pathway is the key determinant underlying immune escape in Herceptin-resistant BC with high IRPI. A combination of STING agonist and DS-8201 could serve as a new strategy for Herceptin-resistant HER2+ BC.

Identification of disulfidptosis-related clusters and construction of a disulfidptosis-related gene prognostic signature in triple-negative breast cancer.

Objective: This study aimed to explore disulfidptosis-related clusters of triple-negative breast cancer (TNBC) and build a reliable disulfidptosis-related gene signature for forecasting TNBC prognosis. Methods: The disulfidptosis-related clusters of TNBC were identified based on public datasets, and a comparative analysis was conducted to assess their differences in the overall survival (OS) and immune cell infiltration. Morever, the differentially expressed genes (DEGs) between clusters were recognized. Then, the prognostic DEGs were then chosen. A prognostic signature was constructed by the prognostic DEGs, followed by nomogram construction, drug sensitivity, immune correlation, immunotherapy response prediction, and cluster association analyses. Results: Two disulfidptosis-related clusters of TNBC were identified, which had different OS and macrophage infiltration. Moreover, 235 DEGs were identified between two clusters. A prognostic signature was then constructed by five prognostic DEGs including HLA-DQA2, CCL13, GBP1, LAMP3, and SLC7A11. This signature was highly valuable in predicting prognosis. A nomogram was built by risk score and AJCC stage, which could forecast OS accurately. Moreover, patients with high-risk scores exhibited greater sensitivity to chemotherapy drugs such as lapatinib and had a lower immunotherapy response. Conclusions: Two TNBC clusters linked to disulfidptosis were identified, with different OS and immune cell infiltration. Moreover, a five-disulfidptosis-related gene signature may be a powerful prognostic biomarker for TNBC.

MicroRNA Signatures Associated with Basal Cell Carcinoma Subtypes.

Basal cell carcinoma (BCC) is classified histologically into subtypes that determine treatment decisions. MicroRNAs (miRs) are short noncoding RNAs that may serve as diagnostic biomarkers. We investigated if particular miRs could distinguish BCC subtypes. We sequenced miRs from 55 archival BCC and 9 control skin specimens and then validated these miRs by qRT-PCR assay on a second BCC cohort (18 superficial, 16 nodular, 15 infiltrative) and control skin (n = 12). Expression values for individual miRs were normalized to miR-16-5p, which was the least variant among the control skin and BCC samples. We found that (i) miR-383-5p and miR-145-5p are downregulated in all BCC subtypes compared with control skin, (ii) miR-181c-5p is downregulated in superficial compared with invasive (nodular/infiltrative) BCC, and (iii) miR-22-5p and miR-708-5p are upregulated in infiltrative compared with superficial/nodular BCC and miR-30c-5p is downregulated in infiltrative compared with nodular BCC. Receiver operating characteristic analysis demonstrated excellent capacity of these miRs to discriminate between BCC and control skin (area under the curve, 0.94-0.98), whereas the capacity to discriminate between superficial and invasive subtypes was less robust (area under the curve, 0.7-0.8). Future prospective studies may determine the utility of these miRs as diagnostic biomarkers to guide biopsy and treatment of BCC.

Selection signatures for high altitude adaptation in livestock: A review.

High altitude adapted livestock species (cattle, yak, goat, sheep, and horse) has critical role in the human socioeconomic sphere and acts as good source of animal source products including milk, meat, and leather, among other things. These species sustain production and reproduction even in harsh environments on account of adaptation resulting from continued evolution of beneficial traits. Selection pressure leads to various adaptive strategies in livestock whose footprints are evident at the different genomic sites as the "Selection Signature". Scrutiny of these signatures provides us crucial insight into the evolutionary process and domestication of livestock adapted to diverse climatic conditions. These signatures have the potential to change the sphere of animal breeding and further usher the selection programmes in right direction. Technological revolution and recent strides made in genomic studies has opened the routes for the identification of selection signatures. Numerous statistical approaches and bioinformatics tools have been developed to detect the selection signature. Consequently, studies across years have identified candidate genes under selection region found associated with numerous traits which have a say in adaptation to high-altitude environment. This makes it pertinent to have a better understanding about the selection signature, the ways to identify and how to utilize them for betterment of livestock populations as well as farmers. This review takes a closer look into the general concept, various methodologies, and bioinformatics tools commonly employed in selection signature studies and summarize the results of recent selection signature studies related to high-altitude adaptation in various livestock species. This review will serve as an informative and useful insight for researchers and students in the field of animal breeding and evolutionary biology.