Correlation between human colon cancer specific antigens and Raman spectra. Attempting to use Raman spectroscopy in the determination of tumor markers for colon cancer.

Colorectal cancer is the second most common cause of cancer-related deaths worldwide. To follow up on the progression of the disease, tumor markers are commonly used. Here, we report serum analysis based on Raman spectroscopy to provide a rapid cancer diagnosis with tumor markers and two new cell adhesion molecules measured using the ELISA method. Raman spectra showed higher Raman intensities at 1447 cm-1 1560 cm-1, 1665 cm-1, and 1769 cm-1, which originated from CH2 proteins and lipids, amide II and amide I, and CO lipids vibrations. Furthermore, the correlation test showed, that only the CEA colon cancer marker correlated with the Raman spectra. Importantly, machine learning methods showed, that the accuracy of the Raman method in the detection of colon cancer was around 95 %. Obtained results suggest, that Raman shifts at 1302 cm-1 and 1306 cm-1 can be used as spectroscopy markers of colon cancer.

Primary Care Continuity, Frequency, and Regularity Associated With Medicare Savings.

Importance: Reducing Medicare expenditures is a key objective of Medicare's transition to value-based reimbursement models. Improving access to primary care is an important way to reduce expenditures, yet less is known about how visits should be organized to maximize savings. Objective: To examine the association between Medicare savings and primary care visit patterns. Design, Setting, and Participants: This retrospective cohort study used data from a 5% sample of traditional Medicare claims from 2016 to 2019. Participants had at least 3 primary care visits with at least 180 days between the first and the last visit, were not enrolled in Medicare Advantage, did not have end-stage kidney disease, and were not institutionalized. Data were analyzed from June 2022 to April 2023. Exposures: Primary care visit patterns: visit frequency, regularity, continuity of care. Main Outcomes and Measures: Savings in Medicare expenditures; risk-adjusted Medicare expenditures, number of emergency department (ED) visits, and hospitalizations. Results: Among 504 471 beneficiaries (298 422 [59.16%] women; mean [SD] age, 74.26 [10.41] years), temporally regular visits with higher continuity were associated with the highest savings. For these patients, the savings increased with increasing visit frequencies, with peak savings observed at higher visit frequencies as clinical complexity increased. As regularity and continuity decreased, the association between savings and visit frequencies progressively inverted. The group with a regular and highly continuous pattern was associated with greater savings (175.87%; 95% CI, 167.40% to 184.33%; P < .001), lower risk-adjusted expenditures (-16.61%; 95% CI, -16.73% to -16.48%; P < .001), fewer risk-adjusted ED visits (-40.49%; 95% CI, -40.55% to -40.43%; P < .001), and fewer risk-adjusted hospitalizations (-53.32%; 95% CI, -53.49% to -53.14%; P < .001) compared with the irregular noncontinuous group. Conclusions and Relevance: In this cohort study, savings in Medicare expenditures and improvements in acute care utilization were associated with visit frequency, regularity, and continuity in primary care in an interrelated fashion such that optimization of primary care visit patterns along each axis were associated with the largest improvement in outcomes. Demonstrating the magnitude and interdependence of these associations is useful for health care professionals and policymakers as Medicare continues its transition to value-based reimbursement models.

lncRNA NORAD, soluble ICAM1 and their correlations may be related to the regulation of the tumor immune microenvironment in laryngeal squamous cell carcinoma (LSCC).

NORAD, non-coding RNA activated by DNA damage, is a Long non-coding RNA (lncRNA) transcript that modulates genome stability and has been reported to be dysregulated in different cancers. Although it has been reported to be upregulated in tumor cells mostly for solid organ cancers, it has also been reported to be downregulated in some cancers. Although the pathophysiological mechanism is not fully understood, a negative correlation between NORAD and intercellular cell adhesion molecule-1 (ICAM-1) has been shown in experimental models, but this situation has not been evaluated in terms of cancer. We aimed to evaluate the potential roles of these two biomarker candidates together and separately in the clinicopathological axis in Laryngeal squamous cell carcinoma (LSCC) in a case-control study setting. The interactions of NORAD and ICAM1 at the RNA level were evaluated interactively by the RIblast program. sICAM1 (soluble intercellular cell adhesion molecule-1) levels were determined by ELISA in one hundred and five individuals (forty-four LSCC, sixty-one control) and lncRNA NORAD expression in eighty-eight tissues (forty-four LSCC tumors, forty-four tumor-free surrounding tissues) was determined by Real-time PCR. While the energy treesholud was - 16 kcal/mol between NORAD and ICAM1, the total energy was 176.33 kcal/mol, and 9 base pair pairings from 4 critical points were detected. NORAD expression level was found to be higher in tumor surrounding tissue compared to tumor tissue, and sICAM1 was higher in the control group compared to LSCC (p = 0.004; p = 0.02). NORAD discreminte tumor surrounding tissue from tumor (AUC: 0.674; optimal sensitivity:87.50%; optimal specificity 54.55%; cut-off point as >1.58 fold change; P = 0.034). The sICAM1 level was found to be higher in the control (494,814 ± 93.64 ng/L) than LSCC (432.95 ± 93.64 ng/L) (p = 0.02). sICAM1 discreminte control group from LSCC (AUC: 0.624; optimal sensitivity 68,85%; optimal specificity 61,36%; cut-off point ≤115,0 ng/L; (p = 0.033). A very strong negative correlation was found between NORAD expression and patients' sICAM1 levels (r = -.967; n = 44; p = 0.033). sICAM1 levels were found to be 1.63 times higher in NORAD downregulated subjects compared to upregulated ones (p = 0.031). NORAD was 3.63 times higher in those with alcohol use, and sICAM 1 was 5.77 times higher in those without distant organ metastasis (p = 0.043; 0.004). The increased NORAD expression in the tumor microenvironment in LSCC, the activation of T cells via TCR signaling, and the decrease of sICAM in the control group in correlation with NORAD suggests that ICAM1 may be needed as a membrane protein in the tumor microenvironment. NORAD and ICAM1 may be functionally related to tumor microenvironment and immune control in LSCC.

Predicting the predisposition to colorectal cancer based on SNP profiles of immune phenotypes using supervised learning models.

This study explores the machine learning-based assessment of predisposition to colorectal cancer based on single nucleotide polymorphisms (SNP). Such a computational approach may be used as a risk indicator and an auxiliary diagnosis method that complements the traditional methods such as biopsy and CT scan. Moreover, it may be used to develop a low-cost screening test for the early detection of colorectal cancers to improve public health. We employ several supervised classification algorithms. Besides, we apply data imputation to fill in the missing genotype values. The employed dataset includes SNPs observed in particular colorectal cancer-associated genomic loci that are located within DNA regions of 11 selected genes obtained from 115 individuals. We make the following observations: (i) random forest-based classifier using one-hot encoding and K-nearest neighbor (KNN)-based imputation performs the best among the studied classifiers with an F1 score of 89% and area under the curve (AUC) score of 0.96. (ii) One-hot encoding together with K-nearest neighbor-based data imputation increases the F1 scores by around 26% in comparison to the baseline approach which does not employ them. (iii) The proposed model outperforms a commonly employed state-of-the-art approach, ColonFlag, under all evaluated settings by up to 24% in terms of the AUC score. Based on the high accuracy of the constructed predictive models, the studied 11 genes may be considered a gene panel candidate for colon cancer risk screening.

An application of raman spectroscopy in combination with machine learning to determine gastric cancer spectroscopy marker.

BACKGROUND AND OBJECTIVE: Globally, gastric carcinoma (Gca) ranks fifth in terms of incidence and third in terms of mortality. Higher serum tumor markers (TMs) than those from healthy individuals, led to TMs clinical application as diagnostic biomarkers for Gca. Actually, there is no accurate blood test to diagnose Gca. METHODS: Raman spectroscopy is applied as an efficient, credible, minimally invasive technique to evaluate the serum TMs levels in blood samples. After curative gastrectomy, serum TMs levels are important in predicting the recurrence of gastric cancer, which must be detected early. The experimentally assesed TMs levels using Raman measurements and ELISA test were used to develop a prediction model based on machine learning techniques. A total of 70 participants diagnosed with gastric cancer after surgery (n = 26) and healthy (n = 44) were comrpised in this study. RESULTS: In the Raman spectra of gastric cancer patients, an additional peak at 1182 cm-1 was observed and, the Raman intensity of amide III, II, I, and CH2 proteins as well as lipids functional group was higher. Furthermore, Principal Component Analysis (PCA) showed, that it is possible to distinguish between the control and Gca groups using the Raman range between 800 and 1800 cm-1, as well as between 2700 and 3000 cm-1. The analysis of Raman spectra dynamics in gastric cancer and healthy patients showed, that the vibrations at 1302 and 1306 cm-1 were characteristic for cancer patients. In addition, the selected machine learning methods showed classification accuracy of more than 95%, while obtaining an AUROC of 0.98. Such results were obtained using Deep Neural Networks and the XGBoost algorithm. CONCLUSIONS: The obtained results suggest, that Raman shifts at 1302 and 1306 cm-1 could be spectroscopic markers of gastric cancer.

Potential role of immune cell genetic variants associated with tumor microenvironment response in laryngeal squamous cell carcinoma (LSCC) in terms of clinicopathological features.

Immunomodulatory signals regulate the self-tolerance, activation, priming and survival processes of T cells. Programmed cell death protein 1 (PD1), Programmed death-ligand 1 (PD-L1) inhibitory signals and CD27, CD28 costimulators have been detected for many solid organ cancers in tumor-infiltrating T cells. It was aimed to investigate the immune cell-based regulatory genetic variants in laryngeal squamous cell carcinoma (LSCC) in terms of clinicopathological features. Genotyping was performed by PCR-RFLP method for PD-1 rs2227981, PD-L1 rs2890658, CD28 rs3116496, CD27 rs2267966 genetic variants from genomic DNAs extracted from peripheral blood samples in One Hundred Thirty-Six individuals (Sixty-one LSCC and seventy-five controls). Analysis of SNPs was carried out according to multiple inheritance models (co-dominant, dominant, recessive, over-dominant and log-additive). There was no difference between LSCC and control groups in genotype/allele distribution for PD-1 and PD-L1 (p > 0.05). In the PD-1 overdominant model, the CT genotype was found to be high (p = 0.036) in those without a family history. The frequency of C allele (AC+CC) in the PD-L1 dominant model was higher in alcohol users and those with reflux (p = 0.024; p = 0.001 respectively). In the Dominant model for PD-L1, the AA genotype was lower in moderately and well-differentiated tumors than in poorly differentiated tumors (p = 0.02). CD27 AT and CD28 CT genotypes were found to be higher in LSCC patients compared to the control group (p = 0.009; p = 0.01 respectively), while linkage disequilibrium (LD) was detected between CD27 and CD28 (p = 0.02). In the CD28 dominant model, C allele (CT+CC) carriage was found to be high in those with family history and in those without reflux and perineural invasion (p = 0.01; p = 0.01; p = 0.03 respectively). In LSCC, PD-L1 rather than PD-1 has a prognostic effect in terms of clinicopathology, and the LD and clinicopathological relationships detected between CD28 and CD27 genotypes suggest that the hereditary immune checkpoint-dependent T cell traffic may be pathophysiologically important.

Assessment of structural protein expression by FTIR and biochemical assays as biomarkers of metabolites response in gastric and colon cancer.

Colon and gastric cancers are the widespread benign types of cancers which are synchronous and metachronous neoplasms. In terms of the progression and progress of the disease, metabolic processes and differentiation in protein structures have an important role in for treatment of the disease. In this study we proposed to investigate the metabolic process and the differentiation of protein secondary structure among colon and gastric cancer as well as healthy controls using biochemistry and Fourier Transform InfraRed spectroscopy (FTIR) methods. For this purpose, we measured blood serum of 133 patients, which were conducted upon oncology department (45 colon cancer, 45 gastric cancer and 43 control individuals). The obtained spectroscopic results and biochemical assays showed significant reduction in the amount of functional groups in cancer groups contrary with total protein measurements and structure of protein differences between colon and gastric cancers. Differentiations were visible in serum levels of CEA, CA-125, CA-15-3, CA-19-9 AFP (Alpha fetoprotein) of gastric and colon cancer patients as well as in amide III and secondly described amide I regions. Our findings suggest that amide I bonds in colon cancer cells can be helpful in diagnosis of colon cancer. Indeed, our results showed that metabolic processes were higher in gastric cancer group than in colon cancer. Hence, FTIR spectroscopy and curve-fitting analysis of amide I profile can be successfully applied as tools for identifying quantitative and qualitative changes of proteins in human cancerous blood serum. However, what is very important, in PCA analysis we see, that the scatter plot of PC1 (variability 80%) and PC2 (variability 15%) show that the data related to the control and two cancer groups are clustered together with different magnitudes and directions.